ISM UNIVERSITY OF MANAGEMENT AND ECONOMICS FINANCIAL ECONOMICS PROGRAMME
II year student Justina Marčiauskaitė
2013 05 13…………………..
MEASURING SYSTEMIC RISK IN THE BALTIC STOCK MARKET MASTER THESIS
Thesis advisor Assoc. Prof. Dr. N. Mačiulis
VILNIUS, 2013 ABSTRACT
Marčiauskaitė, J. Measuring systemic risk in the Baltic stock market [Manuscript]: Master Thesis: Economics. Vilnius, ISM University of Management and Economics, 2013.
The recent financial crisis has brought significant attention to the systemic risk subject in terms of measurement and prudent monitoring. The systemic risk measurement issue became important not only for the regulators, academics and large financial corporations but also for investors and financial market participants. This paper aims to measure the systemic risk from the perspective of the sectors comprising the Baltic stock market. The research is focused on 9 sectors consisting of 47 listed companies from Vilnius, Riga and Tallinn stock exchanges. The intended research employs CoVaR analytics estimated by quantile regression setting as proposed by Adrian and Brunnermeier (2011). The main objective of the paper is to estimate the risk spillovers from chosen sectors to the whole system, represented by Baltic stock market index. CoVaR measure ultimately was estimated by two strategies, one being bottom quantile system VaR subtracted from system CoVaR conditioned on the specific sector at the same bottom quantile, another measuring CoVaR as the difference between bottom quantile system CoVaR and system CoVaR in 50th quantile, i.e. in the medium state. The findings of both strategies identified Natural resources sector having the highest contribution to the whole systemic risk, meaning that in case this sector becomes distressed, the overall systemic risk of the Baltic stock market would increase dramatically. Manufacturing, Consumer Discretionary and Financial sectors also appeared to be important contributors to the Baltic stock market systemic risk. Interestingly, individual risk of the sectors, measured by VaR, appeared to be significantly different from CoVaR with Spearman’s rank and Kendall’s tau correlation coefficients of 0,34 and 0,25 estimated for the 1st method, respectively, and 0,51 and 0,38 for the 2nd method, respectively. This finding confirms the notion, that relying solely on VaR analytics is not enough to assess the investment risk since it may lead to incorrect interpretations about sector-specific risk. The main implications of the study could be useful tool for the investors when selecting the optimal portfolio consisting of the Baltic stocks and seeking prudent risk diversification.
Keywords: systemic risk, Value-at-Risk, CoVaR, quantile regression.
2 CONTENTS
1. INTRODUCTION ...... 5 2. LITERATURE REVIEW ...... 7 2.1. Defining the concept of systemic risk ...... 7 2.2. Systemic risk measurement ...... 10 2.3. The relationship between stock returns and economic factors ...... 18 3. RESEARCH PROBLEM DEFINITION ...... 22 4. METHODOLOGICAL APPROACH ...... 23 4.1. Value-at-risk (VaR) ...... 23 4.2. Conditional Value-at-Risk (CoVaR) ...... 25 4.3. VaR and CoVaR estimation via quantile regressions ...... 26 4.4. Selection of economic factors explaining stock returns ...... 31 4.4.1. Germany stock market index (DAX) ...... 31 4.4.2. US stock market index (SP500) ...... 31 4.4.3. Oil price ...... 32 4.4.4. 3-month EURIBOR interest rate ...... 32 4.4.5. EU government bond yield (10-year) ...... 32 4.4.6. EUR/USD exchange rate ...... 32 4.4.7. Economic sentiment indicator ...... 33 4.4.8. Inflation ...... 33 4.4.9. Industrial production ...... 33 4.5. Model diagnostic tests ...... 33 4.6. Data...... 35 5. EMPIRICAL RESEARCH RESULTS ...... 37 5.1. Descriptive statistics of data ...... 37 5.2. Estimation of system and sectors’ Value-at-Risk ...... 40 5.3. CoVaR estimation ...... 42 5.4. Marginal sector contribution to the overall systemic risk of the Baltic stock market, CoVaR ...... 44 6. DISCUSSION ...... 48 6.1. Main findings ...... 48 6.2. Findings from the perspective of reviewed literature ...... 49 6.3. Limitations of the study ...... 50 6.4. Implications and proposals for further research ...... 51 7. CONCLUSIONS ...... 52 8. LIST OF REFERENCES ...... 54 9. APENDICES...... 57
3 LIST OF FIGURES
Figure 1. Concept map for systemic risk definitions in literature ...... 9 Figure 2. Intended research design ...... 23 Figure 3. Illustration of Value-at-Risk concept ...... 24 Figure 4. Illustration of quantile regression ...... 27 Figure 5. Sector VaR under 1%, 5% and 10% probability ...... 42 Figure 6. System CoVaR conditioned on top 4 sectors by year...... 43 Figure 7. Marginal contribution of sectors to systemic risk by 2 methods, ...... 45 Figure 8. Sector VaR vs. CoVaR assessed by 1st approach ...... 46 Figure 9. 5% CoVaR of Natural resources, Manufacturing, Consumer discretionary and Financial sectors by year ...... 47
LIST OF TABLES
Table 1. Diversity of systemic risk measures ...... 10 Table 2. The allocation of listed Baltic stock market companies into sectors ...... 35 Table 3. The selected economic factors used in the analysis...... 36 Table 4. Descriptive statistics of the sectors’ returns ...... 37 Table 5. Descriptive statistics of economic factors used in the research ...... 38 Table 6. Stationarity tests for sectors’ returns and economic factors used in the research ...... 39 Table 7. The Spearman’s rank and Kendall’s tau correlations between the system and each of the sector returns ...... 39 Table 8. System and sector VaR under 1%, 5% and 10% maximum loss probability ...... 41 Table 9. System CoVaR conditional on each sector under 1%, 5% and 10% probability ...... 42 Table 10. Highest to lowest marginal contribution of each sector to the overall Baltics market systemic risk...... 44 Table 11. Comparison of sector VaR and CoVaR by 2 approaches ...... 46 Table 12. Correlations between CoVaR and VaR ...... 47
4 1. INTRODUCTION
In the face of recent financial crisis it became evident that contagion and spillover effects of the financial shocks have reached the truly impressive scope across financial institutions and stock markets. The severe aftermath of the financial crisis invoked the need to identify and measure systemic risk and for this purpose special research groups and even new offices were formed within governing bodies of US and Europe to arrive at the most appropriate methods of defining and quantifying systemic risk. Gerlach (2009) in his study for European Parliament's Committee on Economic and Monetary Affairs highlighted 3 important elements of systemic risk: 1) the phenomenon called systemic risk should impact the substantial part of the financial system; 2) the systemic risk constitutes extensive contagion and spillovers transmission mechanism across financial institutions and capital markets; 3) and once the systemic risk becomes “visible”, it will usually lead to prompt and strong policy responses, which we observed in the resolutions of recent financial crisis.
Despite elements mentioned above, the term systemic risk has multiple definitions in the literature and, consequently, extensive diversity of measurement techniques capturing different dimensions of the phenomenon. D. Bisias et al. (2012) accounts for 31 different methods to measure systemic risk. Nevertheless, all these measures share some degree of similarities and helps regulators, academia and various organisations to develop the general guidelines to identify, quantify and mitigate the hazards of the systemic risk. The class of cross-sectional measures capturing the co-dependence of systemic risk has gained serious attention in pursuit of quantifying systemic risk. One of the most prominent cross-sectional approaches is ∆CoVaR methodology offered by Adrian and Brunnermeier (2011).
This research adopts particularly ∆CoVaR technique designed to gauge systemic risk from relative new perspective than was done by Adrian and Brunnermeier. Instead of focusing on financial system and the financial institutions, the attention is concentrated on the sectors comprising the whole Baltic stock market. The studies focusing on financial market sector-specific distress are relatively scarce and even scarcer in the context of the Baltic region, so the contribution of this study is employment of more holistic approach in analysing systemic risk spillovers arising from the sectors of the Baltic stock exchange.
Hence, the main goal of this research is to measure the systemic risk in the Baltic stock market with the emphasis on sector-specific idiosyncratic distress and to quantify the marginal contribution of the selected sectors to the systemic market risk. Following the goal of the research, the tasks to be solved are as follows: Analyse the concept and definition of the systemic risk; Investigate the most common systemic risk measurement methods; In order to build research upon CoVaR methodology, select the most appropriate economic determinants capturing time-varying relation of the system’s and sectors’ financial returns;
5 Calculate VaR and CoVaR of the system and each of the selected sectors; Estimate CoVaR by 2 different approaches, compare the estimated risk measures and identify the sectors mostly contributing to the system-wide risk of the Baltic stock market.
The remainder of the paper is organized in the following parts. In the 2nd section the theoretical framework of the systemic risk concept, it’s measurement methods and the relation between economic factors and stock returns are examined through the set of related literature. The research problem definition is presented in the 3rd part. The following part of the paper introduces the justification and clarification on selected methodological approach and data used. 5th part outlines the empirical research results whereas 6th part of the research invites for a discussion. The final part includes conclusions.
6 2. LITERATURE REVIEW
This part of the paper is intended to analyse the theoretical framework of the concept of systemic risk, its measurement techniques and the relationship between the various economic determinants and stock market returns, which are relevant for constructing the valid model for the research.
The definition of systemic risk is differently interpreted and defined by various organizational and governmental institutions. According to ECB Annual Report 2009, systemic risk is “the risk that the inability of one participant to meet its obligations in a system will cause other participants to be unable to meet their obligations when they become due, potentially with spillover effects (e.g. significant liquidity or credit problems) threatening the stability of or confidence in the financial system” (p. 268). This definition is strongly focused on the banking system and it is unclear how systemic risk impacts the whole economy and what is the role of financial markets in a given characterization. In October 2009 U.S. Federal Reserve Chairman Ben Bernanke defined systemic risk in a slightly broader manner:
“Systemic risks are developments that threaten the stability of the financial system as a whole and consequently the broader economy, not just that of one or two institutions” (http://blogs.wsj.com).
International Monetary Fund, Bank for International Settlement and Financial Stability Board in their 2009 joint report to G20 members admitted the definition of systemic risk is complicated to achieve by stating that:
“Establishing what constitutes systemic importance has proved difficult, and most G-20 members do not have a formal definition. Nonetheless, in practice G-20 members consider an institution, market or instrument as systemic if its failure or malfunction causes widespread distress, either as a direct impact or as a trigger for broader contagion. The interpretation, however, is nuanced in that some authorities focus on the impact on the financial system, while others consider the ultimate impact on the real economy as key.” (p. 5).
The following parts of Literature Review analyses the most relevant studies examining the concept of systemic risk and its measurement. Furthermore, the separate subsection will be designed to present recent studies exploring the relationship between financial returns and economic factors, which are important for building the research model.
2.1. Defining the concept of systemic risk
Despite distinctive definitions of systemic risk mentioned above, most authors and institutions agree that there exists a common factor - a trigger event. Schwarcz (2008) names factors such as a severe economic turbulence or institutional failure that induces a chain of adverse outcomes – sometimes called a domino effect. These outcomes could include financial institution or market failures as well
7 as significant losses to financial institutions or harsh financial market price volatility. In either case, the effect is apparent for financial institutions, financial markets, or both.
Hansen (2013) highlighted the term systemic risk should not be confused with the term systematic risk since their interpretation and measurement is very distinct. Systematic risk is described as macroeconomic or common risk that cannot be avoided through diversification. In other words, the investors get the compensation for this risk. The macroeconomic “shocks” are the source of systematic risk and are usually priced in the security markets. The term systemic risk propagates the risk of major distress or breakdown in the financial markets and has nothing to do with the compensation of expected returns. The presence of such risk induces the rationale for financial monitoring, intervention and regulation (Hansen, 2013).
Hansen (2013) distinguishes three possible notions of systemic risk:
systemic risk as a recent time bank run triggered by liquidity concerns resulting in counterparty contagion. The role of central banks as the “lenders of last resort” in measuring this kind of risk is core to prevent the default of large financial entities or groups of them. systemic risk as the weakness of a financial network where severe contagion is likely to spread and reinforce adverse consequences within the network. The greatest challenge in this systemic risk concept is to indicate those weaknesses of the financial network that can trigger a failure. systemic risk as the potential insolvency of a key element or the part of it of the financial system (Hansen, 2013).
Recent financial crisis has resulted in heightened demand for legislative monitoring and regulation to mitigate systemic risk and the outcome of this demand was US Dodd-Frank Wall Street Reform and Consumer Protection Act, which is really cumbersome and still incomplete despite the fact that it is 2’319 pages long (Hansen, 2013). Similar legislation in Europe was named European Market Infrastructure Regulation (EMIR) and is also focused on reducing the systemic risk in European market (KPMG, 2011).
8 Figure 1. Concept map for systemic risk definitions in literature
Source: Brady and Markeloff (2012, p. 59).
The map of systemic risk definitions was comprehensively presented by Brady and Markeloff (2012) who analysed 65 papers pertaining the financial system and systemic risk. The authors acknowledged that there is a lack of consensus on the ways to define, identify and monitor systemic risk of the financial network. The map indicates that the primary discussion on the term systemic risk is related with banking network assets and the liquidity and the connections to other important linkages evolving the possible contagion matrix (Brady and Markeloff, 2012).
Schwarcz (2008) in his paper “Systemic risk” has anatomized the conceptual meaning of the systemic risk by minimizing the confusion and ambiguity of the basic parameters. Author recognized that many researchers tend to think of systemic risk from the perspective of banks, and only infrequently in terms of financial markets. Following the growth of capital markets, where companies can access funding without going through banks or other intermediary institutions, greater emphasis should be directed to financial markets and the inter-linkages between markets and institutions.
The definition constructed by Schwarcz encompasses the economic shock as primary source where market or institutional failure evoke (through a panic or contagion) either the failure of a chain of markets or institutions or through the range of significant losses to financial institutions, resulting in upturns in the cost of capital or contraction in its availability, often accompanied by substantial financial-market price volatility.
Moreover, the author distinguishes systemic risk from downturns that are induced by normal market
9 swings meaning they are likely to be “systematic risk” which cannot be diversified away and affects all market participants and is in its nature positive phenomena as it helps to equilibrate the market and curbs excessive interest rates or periods of inflation.
In addition, Schwarcz (2008) highlights the importance of regulation in his paper as the external factors imposed by systemic risk would not be prevented because market participants are self- centric and have no inherent motivation to protect the system as a whole. It implies that the regulation of systemic risk appears not only desirable, but necessary Schwarcz (2008).
2.2. Systemic risk measurement
Systemic risk could be measured in various different ways capturing very different financial perspectives and aspects. In the recent years policymakers, regulators, academics and various practitioners are looking for the most comprehensive way to measure systemic risk of the financial system but this task is highly complex since the definition and conceptual meaning of systemic risk could be interpreted differently.
The valuable piece of literature on systemic risk measurement was published by Bisias, Flood, Lo and Valavanis (2012) with the name of “A Survey of Systemic Risk Analytics” where the focus was put on the methods that are used to measure the systemic risk. The authors distinguished 31 different methods to quantify the systemic risk and grouped them based on types of inputs, analysis and outputs produced into 6 broader categories as follows: Macroeconomic measures; Granular foundations and network measures; Forward-looking risk measures; Stress-test measures; Cross-sectional measures; Measures of illiquidity and insolvency.
Table 1 depicts the analytics in each category as was divided by Bisias et al. (2012) and demonstrate the diversity of the models and measures intended to quantify the systemic risk. The part of the methods are available only for regulators due to the confidential information input, others require quoted time-series data from financial markets, which are not present in countries like the Baltic states. Fortunately, there are models such as Cross-sectional measures, which are also employed in this research and are available for academic community and the public.
Table 1. Diversity of systemic risk measures 1. Macroeconomic 2. Granular 3. Forward- 4. Stress-test 5. Cross-sectional 6. Measures of Measures foundations and looking risk measures measures illiquidity and network measures measures insolvency Costly Asset-Price The Default Intensity Contingent Claims GDP Stress CoVaR Risk Topography Boom/Bust Cycles Model Analysis Tests Property-Price, Equity- Network Analysis and Mahalanobis Lessons from Distressed The Leverage Price, and Credit-Gap Systemic Financial Distance the SCAP Insurance Premium Cycle
10 1. Macroeconomic 2. Granular 3. Forward- 4. Stress-test 5. Cross-sectional 6. Measures of Measures foundations and looking risk measures measures illiquidity and network measures measures insolvency Indicators Linkages Macroprudential Simulating a Credit The Option iPoD 10-by-10-by- Co-Risk Noise as Regulation Scenario 10 Approach Information for Illiquidity Simulating a Credit- Multivariate Density Marginal and Crowded Trades and-Funding-Shock Estimators Systemic Expected in Currency Funds Scenario Shortfall Granger-Causality Simulating the Equity Market Networks Housing Sector Illiquidity Bank Funding Risk Consumer Credit Serial Correlation and Shock and Illiquidity in Transmission Hedge Fund Returns Mark-to-Market Principal Broader Hedge- Accounting and Components Fund-Based Liquidity Pricing Analysis Systemic Risk Measures Source: Bisias et al. (2012)
Bisias et al. (2012) did not weight the advantages and disadvantages of all the models since the goal of their paper was to collect, process and systemize the methodology of other researchers for systemic risk analytics and to encourage regulators, academics and other interested stakeholders to take on more research on this challenging and highly complex task (Bisias et al., 2012). Differently to Bisias et al. (2012) Hansen (2013) distinguished the systemic risk measurements by dividing them into 4 broad groups, namely: 1. Tail measures; 2. Contingent claim measures; 3. Network models; 4. Dynamic, stochastic macroeconomic models. Though, Hansen (2013) analysis is rather limited and did not analyse the models very deeply.
The paper falling under cross-sectional systemic risk measurement named “Measuring Systemic Risk” and designed by Acharya, Pedersen, Philippon and Richardson (2010) aims to offer a measure of systemic risk using systemic expected shortfall (SES) and marginal expected shortfall (MES) technique. In their paper the authors focus on optimal regulatory policy, which can be applied for financial institutions since their insolvency imposes costs to the taxpayers and leads to externalities that spill over to the rest of the economy.
Acharya et al. (2010) proved that the systemic-risk component is equal to the amount of under- capitalization of a bank in a future systemic shock in which the overall financial system is under- capitalized. The calculation of MES and SES was based on daily equity returns and also on CDS spreads. The authors provided enough evidence on the strong predicting power in forecasting SES, which was calculated through MES and the leverage. However, the task to measure the actual leverage appeared to be challenging, so the writers used approximation of it. The stress testing results conducted by Acharya et al. (2010) with the focus on simple statistical measures of SES comply with the Federal Reserve Supervisory Capital Assessment Program (SCAP) findings which were presented in May 2009 and considered as creditable disclosure on capital needs of the U.S.
11 banks.
The optimal regulatory framework according to the authors is equal to the sum of two elements, namely, an institution-risk element, more specifically, the expected loss on guaranteed liabilities, and a systemic-risk element, when the financial sector becomes undercapitalized and the expected systemic loss during a crisis is incurred, multiplied by the share of the financial institution's contribution to this under-capitalization. The theory of paper says that, based on the overall probability of a systemic shock, regulation of systemic risk and respective taxation policy should lie in each entities SES based on those aforementioned two elements (Acharya et al., 2010).
BIS working paper on “Attributing systemic risk to individual institutions” by N. Tarashev et al. (2010) uses Shapley value methodology to the field of risk attribution. Authors focus solely on the methodology of attributing systemic risk to the individual institutions. For their metrics of research authors have used Value-at-Risk (VaR) and CoVaR also called Expected Shortfall (ES) technique. Shapley value was initially constructed in the game theory, where the collective effort of a group of players split the generated value and given value is distributed across the players based on their individual inputs. The share of aggregate value of attributed to a particular player is this players Shapley value. In financial system context, the players are institutions that engage in interrelated risky activity and drives systemic risk. Then, the value of system-wide risk is VaR or ES, and systemic importance of each institution is its Shapley value. Shapley value is considered to be cross-sectional measure, which decomposes the systemic risk into elements and is sub-additive meaning that summing up its elements would be equal to the system risk.
The paper outlines three different ways of attributing systemic risk to individual institutions by exploiting Shapley value. First method is varying tail events procedure, which reflects the contribution of individual institutions to the severity of the system shocks. Second method for attributing risk developed by N. Tarashev et al. (2010) captures the degree to which the institution is expected to participate in the systemic shock. Then the Shapley value of the institution is simply the loss it is expected to incur conditional on the trigger event. The third procedure incorporates asymptotic single risk factor model with granularity adjustment meaning it is assumed that financial system is perfectly granular (i.e. the number of banks is large and their sizes are similar). Given the granularity of the system is infinitely fine it is expected the adjustment will decline to zero and using single common risk factor the 3rd procedure is simply the approximation of the 2nd method. Authors illustrate that a bank’s contribution to systemic risk (captured by 1st method) could vary substantially from its participation in the systemic events (captured by 2nd method). This outcome could be simply explained by a mismatch between the expected losses generated by a bank in systemic events and the contribution of this bank to the systemic risk.
Apart from the risk estimation methods, the N. Tarashev et al. (2010) proposes three approaches to regulate the systemic risk: first approach deploys the risk at the level of each institution, more specifically, seeks the target level of systemic risk with the single PD attributed to all institutions. It could go under the name of “micro-prudential” approach with the reference to the currently
12 prevailing policy approach. Other approaches take more a “macro-prudential” perspective - one equalizes the contributions by individual institutions to system-wide risk (i.e. their Shapley values) and another employ the minimization of the general level of capital in the system (ensures the marginal decrease of the systemic risk by imposing an additional unit of capital is the same among the credit institutions).
The analysis on using CoVaR as methodology in measuring systemic risk was investigated in Adrian and Brunnermeier (2011) paper “CoVaR” where authors define the properties and features of CoVaR and CoVaR in estimating systemic risk. In addition, the paper quantifies the extent to which determinants such as leverage, size, and maturity mismatch foresee systemic risk contribution. Later on, the out-of-sample forecasts of a countercyclical, forward-looking measure of systemic risk are being tested and proved to be valid. This research is principally based on the methodology proposed by the authors.
Adrian and Brunnermeier (2011) defined systemic risk having two important components: the systemic risk is built-up during credit booms when low risk environment is assumed and could be labelled as a ‘volatility paradox’ and the second component of systemic risk with the spillover effects that intensify initial adverse shocks in times of crisis. The research outlines direct and indirect spillover effects and is based on the tail covariation between financial institutions and the financial system. Authors distinguish the most attractive CoVaR features to be the following: Cloning property which satisfies the principal of splitting one large institution into ‘n’ smaller clones, the summed systemic risk remains the same as if the there was one large institution. Thus, analysing the distress of a large systemic institution is the equivalent to analyzing one of ‘n’ smaller clones. CoVaR does not differentiate between the causality and common factor effect. However, the authors consider it as an advantage rather than a defect since it helps to capture the concept of being ‘systemic as part of a herd’ even if the direct causal relationship does not exist. Other desired property is tail distribution as CoVaR focuses on more extreme tail distribution than does traditional VaR and reflects shifts in higher moments. Condition property is also very appealing because the probability of conditioning event to be sovereign from the riskiness of institutions strategy, thereby those institutions that are more cautious could posses sharper CoVaR due to the fact that it would be more sensitive to the conditioning event. Endogeneity of systemic risk is the property, which represents the CoVaR endogeneity in relation to other institutions taking risk. The researchers rate this property positively in the context of regulatory framework and incentive to each institution to reduce its exposure to risk if other entities load excessively on it. Directionality meaning CoVaR of the system conditional on certain institution is not the same as the CoVaR of the same institution conditional on the system CoVaR. Exposure CoVaR exhibits the opposite property as directionality since for risk management purposes it may be useful to measure not only the system risk conditional on institution
13 distress but also the institution’s exposure to the system-wide distress, which is similar to stress testing. Another attractive property of CoVaR is that it is easy to adopt for other co-risk measures such as Conditional ES.
The results reached by the authors show that institution’s VaR and its contribution to systemic risk as measured by CoVaR has very intangible link. Therefore the justification of regulatory actions based on detached risk of an institution might not hinder the financial sector from systemic risk. Analysing portfolios, CoVaR and VaR have thin relationship in the cross section whereas relationship in the time series is very solid. Furthermore, the output indicates that firms with higher leverage, more maturity mismatch, and larger size contribute higher to the systemic risk after the period of one quarter, one year, and two years, both at the 1% and the 5% levels. The research of Adrian and Brunnermeier (2011) is quoted in numerous pieces of literature as they evidenced CoVaR being a parsimonious technique of systemic risk that augments other metrics designed for estimating the contribution of system risk by individual financial institutions. CoVaR serves as a looking ahead tool by projecting CoVaR on lagged firm characteristics such as size, leverage, maturity mismatch, and industry dummies. This approach is the appropriate way used to streamline macroprudential policy applications.
One of the attempts to investigate systemic risk in the Baltic region was documented by Valužis and Židulina (2010) in their paper “On the Contagion in the Baltic States”. The researchers sought to answer the question of the systemically relevant credit institutions and their impact to the overall systemic risk to the Baltic banking system. Moreover, the research explored the effects of the shocks between Baltic countries’ banking systems including the possible consequences of liquidity shortages in parent Scandinavian banks to the Baltic banking system. The methods used are simulated model where correlated defaults in network approach are analysed to capture the systemic risk in the interbank markets of the Baltic state region. The simulated methodology is based on official balance sheets submitted to supervisory authorities. Since the Scandinavian banks own the systemically relevant credit institutions in the Baltic States and provide them with funding, making them to be net debtors and hence conveying there are no true credit contagion risk source from parent financial institutions. More specifically, the large banks in the Baltic States are more vulnerable to the cause of risk contagion while the patronizing institutions tend to be the victims (Valužis, Židulina, 2010).
The results of simulated approach of the research find that the collapse of one of the big financial institution will trigger the significant pressure on other financial entities nevertheless will not drive to a complete downfall of the local interbank market. The smaller credit institutions do not load significant risk on the banking market. The paper gauged the systemic risk relying of the hypothetical default correlations between the banks in terms of their classification as big, medium and small without data concerning the mutual exposures and interbank market structure. However, due to the assumption that interbank market structure is complete the weighted and non-weighted centrality indices are uncorrelated. Authors conclude by enhancing the fact that the financial
14 stability of interbank market of the Baltic States is strongly exposed to the regulatory supervision and prudent risk management of Scandinavian banking sector (Valužis, Židulina, 2010).
Given the fact, that actual data of such research are not available and authors used highly hypothetical approach to measure the systemic risk, the results of the analysis could be misleading and not practically validated. Moreover, the results of the research are not fully interpreted and leave a sense of ambiguity.
Other important piece of literature concerning this research is the paper “Systemic Risk in Taiwan Stock Market” written by Sheu and Cheng (2011) where authors empirically explore the impact of sector-specific idiosyncratic risk on the systemic risk of the system and attempt to find links between financial crises, systemic risk and the idiosyncratic risk of a sector specific shock. The output of the investigation perfectly explained Taiwan stock market disturbance during the 2001 dot-com bubble and 2007–2008 financial crisis. Thus, by identifying the larger CoVaR sectors, i.e. the systemic importance sectors, and by exploring the risk indicators, independent variables, of these systemic importance sectors, investors could practically employ the sector-specific CoVaR measure to deepen the systemic risk scrutiny from a macro into a micro prudential perspective. The Sheu and Cheng (2011) research estimated Taiwan stock market as it plays a significant role in Asian markets and for many global investors is attractive investment market. The authors decomposed Taiwan stock exchange into 18 sectors. The results were obtained by using quantile regressions for sectors and whole system returns for the period of 2000 – 2010 monthly stock exchange data in line with controlling macroeconomic state variables. Later the researchers estimated VaR and CoVaR of each individual sector and the whole system and using these metrics arrived at CoVaR exposing marginal contribution of each sector to the overall systemic risk.
According to the empirical results the top 5 highest VaR sectors did not completely correspond to the top 5 highest risk contributory sectors to the system CoVaR. The electronic sector proved to have highest VaR and in line with high systemic CoVaR, however, the banking sector did not demonstrated high sector VaR, but was named as the sector having 2nd highest systemic CoVaR. As opposed to this, the trade and building sectors were found to have high VaRs, but they did not exhibit high systemic CoVaR. It confirms the notion that sector VaR is not necessarily correlated with systemic CoVaR. The results established the proven effects of “too big to fail” and “too interconnected to fail” in Taiwan financial system and, more importantly, relationship between CoVaR measure and the global financial crises perfectly explained Taiwan stock market disturbance during the 2001 dot-com bubble and 2007–2008 financial crisis.
The findings of Huang, Zhou and Zhu (2010) research “Systemic Risk Contributions” reveals that systemic risk indicator lied lower than $50 billion in a pre-crisis period and in 2007 it started to grow extensively topping at $1.1 trillion in March 2009 and followed by a decrease down to $300 billion in December 2009. The authors constructed systemic risk indicator relying on real publicly available financial market information and estimated two key defaults risk determinants from CDS spreads and equivalent equity price movements, i.e. probability of default (PD) of individual banks
15 and correlations of asset returns across banks.
The researchers found that indicator of bank’s contribution to the systemic risk proved to be linearly dependent on its default probability but it did not show any signs of linearity in terms of institution size and asset correlation. Further analysis of Huang, Zhou and Zhu (2010) revealed U.S. banking sector downturn was triggered by premiums of high defaults and liquidity risk as well as actual default risk. According to their results, the size is the prevailing element in quantifying the relative importance of each bank’s systemic risk contribution, however, the size does not change every quarter, so it cannot be regarded as the most significant one. Yet the analysis suggest that time variations in the marginal contributions are caused by risk-neutral default probability and correlations of equity returns (Huang, Zhou and Zhu, 2010).
The results of applying hypothetical distress insurance premium (DIP) indicator, CoVaR and marginal ES in the context of ranking systemically important institutions in Huang et al. (2010) differs rather significantly. The reason behind that could be explained by the fact that CoVaR and marginal ES are statistical measures based only on equity market returns and DIP is build on risk- neutral pricing derived from both CDS and equity data (Huang, Zhou and Zhu, 2010).
In general, systemic risk indicator of the authors is highly hypothetical measure, which does not consider the true underlying economy as it was constructed from fictitious debt portfolio and considered as risk-neutral expectation of portfolio credit losses, which might not be the true assumption in the reality.
An attempt to investigate the determinants of the systemic risk of large global banks was made by Lopez-Espinosa, Moreno, Rubia and Valderrama (2012) in their paper “Short-term Wholesale Funding and Systemic Risk: A Global CoVaR Approach”. The research extended the systemic risk measurements as proposed by Adrian and Brunnermeier (2011) by accounting for some features the previous studies missed out. First, authors paid the attention to the asymmetric contribution to potentially asymmetric financial returns of the system and financial institutions. In other words, in order to improve the predicting power of the model, which essentially focuses on downside risk, where corresponding coefficients show average sensitivity of the system returns to the returns of the bank. Therefore, the study used functional indicator for positive and negative returns in CoVaR model. Second, it is known that many banks went through the recapitalization procedure during the financial downturn, which also had a potential effect for the negative financial returns; therefore dummy variables indicating negative returns as a consequence for recapitalization or crisis were employed in the research.
CoVaR analytics estimated through quantile regression revealed that the most significant economic factors explaining left-tail VaR of the large international banks were volatility index and market returns. Researchers proved that banks have asymmetric returns and it is reflected by different CoVaR coefficients with negative one being on average 3 times larger than positive. In general, the more systemic financial institution was, the more asymmetric its contribution to the system wide
16 risk. In addition, recapitalization had strong effect for European institutions, particularly for Italian banks. The most important empirical evidence of the study disclosed that short-term wholesale funding is the most important determinant when estimating the systemic contribution since it is usually conducted in the over-the-counter operations and exposes the financial institutions to interconnectedness and liquidity risk. In contrast, according to the paper, size, leverage and total assets do not play a role in determining whether bank is risky.
Other ways to measure the systemic risk were taken by Jo (2012) in his paper “Managing systemic risk from the perspective of the financial network under macroeconomic distress” where he associated liquidity risk with solvency risk. The cash outflow and funding costs were taken in a connection to solvency ability of the financial institutions while other analysis assumed it is constant. Moreover, the great emphasis in his network model was devoted to the quality of assets and liabilities rather than analyzing only bilateral exposures and all the analysis was tested on macro stress test by applying the balances of households and corporates (Jo, 2012).
According to the simulated model, the author outlines that local banks in Korea are essential in stabilizing the financial system. The study reveals the three most vulnerable financial segments in case the local banks default, namely, securities firms, foreign bank branches and credit unions. The reasons underlying this fact are due to the largest portion of assets allocated to the loans, which makes these institutions fragile, if macroeconomic shock brings high delinquency ratios of loans. However, the latter finding is based on the pure balance sheet contagion assuming default happens in normal times. In case of severe macroeconomic shock, the results suggest securities firms, credit specialized financial companies (CSFC) together with local banks induce the most financial segments to default. Expansive linkages between securities firms, CSFC and domestic banks in line with solvency risk and liquidity risk impose this result (Jo, 2012).
The paper also introduces the replacement rate, which is expressed as the part of the cash outflows from defaulted sectors replaced with the new resources from the alternative creditors, and incremental funding cost, which shows marginal increase of the cost of funding during distressed times. The impact of macroeconomic shocks is estimated by assumed credit losses, market risk losses and funding losses drawing the conclusion that savings banks and credit unions would experience the defaults immediately the macroeconomic shock hits (Jo, 2012).
Nevertheless, the inference of the author that CSFCs holding the share of only 5% of assets and liabilities in the case of default may cause 92% contagion defaults in the financial system is somewhat doubtful since it is mainly based on the fact that CSFCs have the highest ratio of inter- financial liabilities. On the contrary, the local banks covering 53% of the total financial system assets and liabilities would impose only 84% of contagion defaults. Additionally, author evaluates the framework for reinforcing more healthy financial system in Korea by proposing to reduce bilateral exposures between domestic banks and securities firms as well as domestic banks and CSFC by 20–30% or to increase the Basel ratio for domestic banks by 1.1% according his model (Jo, 2012). Though, the researcher does not weigh the external effects of such restrictions, which
17 can result in negative outcomes for the real economy due to lower lending possibilities and squeezed credit market.
The critique addressed to the cross-sectional models, namely Marginal Expected Shortfall (MES) and CoVaR and also applicable for Shapley value methodology, was presented in the paper “Model risk of systemic risk models” written by Danielsson, James, Valenzuela and Zer (2011). The authors empirically investigated the mentioned systemic risk models and the validity of obtained results. Authors replicated the measurements, which were performed by Adrian and Brunnermeier (2011) and Achraya et al. (2010) and tested the model for the potential misspecifications. MES and CoVaR are closely related methods because they both are derived using VaR and the main difference between these two measures is that MES focuses on conditional risk of the institution given the whole system risk whereas CoVaR concentrates on conditional risk of the system provided the institution in its distress. For analyzing MES measures, authors firstly estimate VaR by using the following 6 different methods: 1. normal GARCH, 2. student–t GARCH, 3. moving average, 4. student–t moving average, 5. exponentially weighted moving average 6. historical simulation. The study finds that VaR results obtained using these methods are significantly different. Then, based on 6 different VaR values authors estimate MES and, as one might expect, found that they also differ significantly hence concluding that the results of MES highly depend on the method VaR is calculated and it may lead to misleading results.
When analyzing CoVaR methodology authors challenge the finding of Adrian and Brunnermeier, which states that VaR and CoVaR differ substantially. The CoVaR was calculated as the difference of system conditioned on specific financial institution in distress and the system conditioned on the same financial institution in medium state. Given the assumption that financial returns are symmetrically distributed the medium state VaR should lay around zero and it follows CoVaR is linear representation of VaR. The authors run quantile regressions for estimating VaR and CoVaR and found that they differ significantly as was reported by Adrian and Brunnermeier (2011). Though, the researchers tested the noise surrounding these estimations by bootstrapping the error terms 1000 times and showed that for 99% confidence intervals have quite wide range of values and the initial results may be misleading. However, the authors used only 12 institutions as opposed to 92 employed by Adrian and Brunnermeier, which might also impacted the final results. Furthermore, the overall conclusions of the paper were that these methods are unreliable when measuring systemic risk, but no improvements were proposed.
2.3. The relationship between stock returns and economic factors
The literature analyzing the relationship between various common variables and stock returns is as
18 equally important as the concept and measurement methods of systemic risk since this research employs the economic variables in deriving VaR and CoVaR measures. The relationship of various macroeconomic variables and stock returns was investigated in extensive number of works during past two or three decades. Given this research focuses on more recent analysis, which covers pre- crisis, crisis and post-crisis periods, the papers were also chosen based on the time-period analysed.
The paper focusing on BRIC countries was documented by Gay (2008), who investigated the connection between stock index prices and two independent variables, namely, exchange rate and oil price for Brazil, Russia, India and China (BRIC) using Box-Jenkins ARIMA model with differently lagged data. The exchange rate was expected to impact the stock prices through depreciation (appreciation) of the local currency thus decreasing (increasing) the cost of local production and in this way boosting the country exports and underlying expectations of the companies’ profit margins. This connection was found in Brazil, Russia and China stock markets, however, it was not very material in explaining stock returns. Next to the exchange rate, the author also focused on oil prices by expecting the oil price has the impact for stock returns due to the fact, that companies would incur higher costs related to inputs of production and it would reduce the cash flows and profits of the companies, so the investors confidence would lower and stock returns would likely fall. The results explaining this relationship differed among the BRICs being more significant only in India with 1 month lagged oil price data. The overall impact of oil prices was rather weak in explaining the stock market returns. Some reasons behind that could be the period of analysis starting from 1999 ended in 2006 when the oil prices started to grow very rapidly.
The work written by Lee and Stewart (2010) conducted quite distinct investigation with the main focus on the cross-border effect between six Baltic and Nordic stock markets as well as external influence from main world indices, specifically, DAX, FTS100 and SP500. Over the period of September 2001 to August 2008 the daily stock returns was applied in EGARCH model setting. Authors proved that returns in Latvia, Denmark, Finland and Sweden exhibit the relationship of their own lagged returns. Moreover, the returns from Latvia are transmitted to Estonia and Sweden, Lithuania’s returns to Finland, and Denmark has impact to Lithuania’s returns. More importantly, the authors found evidence all six countries are significantly impacted by German stock index (DAX) and Latvia and Estonia are affected by US market (SP500). UK stock exchange (FTS100) returns did not have material input to neither country analysed.
The volatility of all the countries was stimulated by its lagged information and was carried from Estonia and Sweden to Finland and from Latvia to Sweden. The German volatility was reflected in Sweden and UK volatility had impact on all three Baltic exchanges. The estimates of the volatility showed significant lagged individual volatility effects in Estonia, Latvia, Lithuania and Sweden and substantial cross-market spillovers. Volatility was taken across from Estonia and Sweden to Finland as well as from Latvia to Sweden. DAX appeared to transmit the volatility to Sweden, whereas FTSE100 – to all three Baltic exchanges. The cross-country effects demonstrated in the research revealed the linkages between Nordic and Baltic stock markets and DAX was found to have an impact to all six markets whereas FTSE100 and SP500 reflected in Baltic stock exchanges through
19 volatility or returns (Lee and Stewart, 2010).
Aside from Nordic and Baltic countries Benakovic and Posedel (2010) analyses 14 Croatian stocks which comprise Croatian stock market index (CROBEX) and 5 macroeconomic factors: inflation, industrial production, interest rate, market index and the price of oil. Using the multiple and cross- sectional regression analysis, the authors seek to find the reflection in stock returns caused by mentioned macroeconomic factors. The dataset used for the study covered the period from January 2004 to October 2009. The results of the model suggested that the most significant influence on selected stocks comes from the Croatian market index. Inflation was significant only in year 2004 and 2008 whilst interest rates, oil prices and industrial production did not appear to be statistically material for selected Croatian stocks. However, the companies specializing in shipping, construction and manufacturing when taken separately demonstrated statistically significant coefficients for the oil price influence, therefore, oil price for returns of such companies’ plays a role. The interesting point was that those coefficients were with the positive sign and the logic behind that, according to authors, could be that these companies index the price of their services and production based on the oil prices, so to keep the oil price effect non-negative (Benakovic and Posedel, 2010).
Similar study was performed by Elhusseiny and Bae (2010) for German and Japan stock markets in connection to the risk factors. In their study the authors exploited ARIMA models for 5 different sectors (banking, insurance, chemicals, telecommunications and utilities) in the stock exchange by testing multifactor pricing methodology of German and Japan information. The factors included in the model were inflation rate, industrial production, changes in anticipated inflation, foreign exchange rate, term structure, oil price and local market indices. The study incorporated two data sets: monthly returns of the sector and monthly information of macroeconomic variables in Germany and Japan over the period of January 1985 to January 2005. In order to present the unexpected changes of the macroeconomic factors, authors took the difference of actual and fitted values. According to the implications of the research, each industry was exposed to different macroeconomic factors, for instance, chemicals, insurance and utilities differed rather significantly in two countries whilst banking and telecommunications have shown to be more parallel. Apart from this, German and Japan stock markets exhibit strong ties to their respective local indices. In general, Japan market displayed stronger relationships between macroeconomic variables and returns of the selected sectors in this way referring to more market efficiency.
Focus on Lithuania stock market was presented in paper of Hsing (2011) where he investigated the relationship of macroeconomic state indicators and Lithuania stock market index. Author constructed 4 versions of regression analysis taking into consideration such explanatory variables as: real GDP (used in all versions) government deficit/GDP ratio (1,3,4 versions) government borrowing/GDP ratio (2 version) M2/GDP ratio (1,2,4 versions)
20 M1/GDP ratio (3 version) LTL/USD exchange rate (all versions) local real interest rate (all versions) expected inflation (all versions) US government bond yield (4 version) EU government bond yield (1,2,3 versions) Germany stock market index (DAX) (all versions) US stock market index (SP500) (all versions)
The sample consisted of quarterly data over the period of Q1 2001 to Q4 2009 and the methodology employed was EGARCH model which is considered to be the most suitable to capture the asymmetric information. All 4 versions of the model reveal explanatory variables are statistically significant at 1% level. The results show that Lithuanian stock market mostly responded to EU area bond, US stock market index, real GDP and LTL/USD exchange rate. Contrary to the Lee and Stewart (2010) this study names US stock market index more influential than German one.
Parallel to the Lithuanian stock market Hsing and Hsieh (2011) conducted the research investigating reflections of macroeconomic state variables in Poland stock market index. The methodology employed was GARCH and ARCH metric and the economic determinants were almost the same as for Lithuania stock market adding industrial production, real Treasury bill rate and, most interestingly, quadratic M2/GDP ratio while withdrawing government deficit/GDP ratio and US government bond yield. The sample of the analysis was the period of Q1 2000 to Q2 2010 and 3 versions were presented in calculations. The findings disclose that Poland stock market is positively impacted by industrial production and US and German stock market indices and gets negative influence from government borrowing to GDP ratio, the real Treasury bill rate, effective exchange rate, anticipated inflation and EU government bond yield as well as quadratic ties with M2/GDP indicator when it exceeds the critical value estimated. Again, the coefficients next to the US and German market indices differs and as in Lithuania case US market index has more impact that German market. Other interesting fact in this study is that effective exchange rate negatively impacts the index given the economic theory that depreciation of the currency awards exporting companies with higher revenues.
The summary of analysed papers including the main comments and critique is presented in Appendix 1.
21 3. RESEARCH PROBLEM DEFINITION
The importance of analyzing systemic risk has never been more evident as it is now due to the severity of financial distress, which has hit the global financial markets including the Baltic region countries in 2008. As a result, the build up of better understanding and defence against systemic risk has emerged as a top priority.
In current financial landscape, the companies are increasingly seeking capital funding through financial market rather than through banks or other credit institutions, therefore capital markets have substantial importance in tracing systemic risk.
The analysis of related literature suggests that the definition of what is the systemic risk is not as easy to find as it may seem. Also, there are numerous different approaches to measure systemic risk and each of them captures different perspectives. Cross-sectional systemic risk measures have gained significant attention in quantifying systemic risk from the financial institutions viewpoint. However, the cross-sectional systemic risk metrics and particularly CoVaR methodology addressed towards stock markets are very limited.
The financial markets of the Baltic region is exposed to the systemic risk as any other financial system, however there is little research made on this subject. Furthermore, the studies focusing on sector-specific distress in terms of measuring systemic risk are also relatively sparse. Consequently, the main goal of this research is to identify the sectors that mostly contribute to the whole systemic risk of the Baltic stock exchange.
This Master thesis will adopt CoVaR methodology proposed by Adrian and Brunnermeier (2011) and will use similar research design as was demonstrated in the study of Sheu and Cheng (2011) in pursuit to identify the marginal contribution to the systemic market risk transmitted from selected sectors constituting the Baltic equity list. The research will seek to find the answer to the following questions: Which sectors have highest VaR and CoVaR measures and are those two measures linked? Which sectors are mostly contributing to the overall systemic risk in the Baltic stock market based on CoVaR analytics?
This type of analysis could bring valuable insights for the investors and other capital market participants choosing the optimal investment portfolios composed of the Baltic stocks and diversifying the underlying risk.
22 4. METHODOLOGICAL APPROACH
This part of the paper provides a description of methodological approach used in the research in line with explanations and justifications of the appropriateness of the chosen methods. The design of the research is similar to Adrian and Brunnermeier (2011) and Sheu and Cheng (2011) papers focusing on cross-sectional systemic risk measurements. For estimating the systemic risk of the Baltic stock exchange the emphasis is given to the sector-specific idiosyncratic failure rather than to the failure of individual institution.
This research adopts the CoVaR and ∆CoVaR methodology to gauge systemic risk of the Baltic stock market. System and sector VaR and CoVaR including ∆CoVaR approached by 2 strategies were developed from economic factors using quantile regression setting as depicted in Figure 2.
Figure 2. Intended research design
4.1. Value-at-risk (VaR)
Value-at-risk (VaR) developed in 1993 and made available since 1994 by J.P. Morgan’s risk metrics has been widely used as a tool for measuring risk in financial markets. The theory behind VaR lies in estimating maximum lost value on a given asset or liability over a given period of time within the certain confidence level (most often at 90%, 95% and 99%). Figure 3 illustrates the concept of the VaR when there is a certain portfolio of assets in terms of money, let it be 1 million EUR, and the estimated potential loss on asset returns given 5% probability. According to Figure 3 example, the estimated value corresponding to left-tail 5% probability of potential loss is -0.82%. Hence, there is 5% chance that the loss on asset returns will be worse than -0.82%, i.e. worse than 82 000 EUR. On the other hand, based on the given example, it could be said that the money lost on the asset will not exceed 82 000 EUR within the confidence level of 95%. However, VaR only signals that losses could be higher than -0.82%, but does not show how deep the loss could be if it falls over the bottom 5% probability area. This method is simple to use and was
23 popularized by the fact that it is easy to understand for non-experts.
Figure 3. Illustration of Value-at-Risk concept 5% chance the returns 95% chance the returns will be better than -0,82% will be worse than -0,82%
-0,82%
Source: Sundt (2012)
However, many recent works challenged VaR as a tool of measuring risk. Wong and Fong (2010) highlighted VaR focused on the asset in isolation, therefore the real risk of the asset might be underestimated, especially when other assets came under stress. Moreover, Dowd and Blake (2006) emphasized that VaR signals only the maximum loss when the tail event does not happen, however it does not warn about the losses that might occur when the left-tail shock hits. Artzner, Delbaen, Eber and Heath (1999) pointed out VaR was not sub-additive meaning VaR of the portfolio can be larger than VaR of separate parts of the portfolio. It suggests that relying solely on VaR is not an appropriate method to gauge systemic risk as well.
The definition of VaR is defined as the conditional quantile p of the asset (Y i ):
i i PrY VaR p p ,
According to Dowd and Blake (2006) there are three basic methods to compute VaR: 1) parametric methods; 2) non-parametric methods (historical simulation); 3) Stochastic Monte Carlo simulation method.
Parametric methods are underpinned by the distribution assumption. However, the distribution assumption leads to the risk of misspecification, so selected distribution should be very accurate which is rather difficult to achieve in practice (Dowd and Blake, 2006).
The most popular method in estimating VaR is historical simulation, which could capture non-linear relationship and is more robust as compared to parametric methods since it is not based on distribution assumption. This method has numerous advantages. First, it is simple since the data used is publicly available in the stock market listings. Second, it focuses on empirical data and reduces the risk of incorrect assumptions if compared to using parametric method. Third, it is
24 comprehensive given the fact that it could be applied to any type of financial position including nonlinear products. One of the negative sides of the historical simulation is that it justifies the possible losses on the basis of historical data, which is sometimes not the case when estimating future events.
It is important to note, that the approach of historical simulation method was used in this research due to its simplicity and advantages mentioned, however, it has some special features due to the fact that VaR values of each sector were estimated using quantile regression coefficients based on sectors’ historical returns and economic determinants, which were used as factors explaining the returns. The more detailed explanation of this approach is exposed in “Quantile regression analysis” section.
Next to parametric and non-parametric approaches there also exists stochastic simulation method (or Monte Carlo simulation), which simulate loss distribution using randomly selected variables. This method is computation intensive but efficient when the number of trial simulations is large enough, otherwise the risk of not accurate loss approximation is also likely. A. Damodaran (unknown date) provides an illustrating example of computational intensity of Monte Carlo simulation by assuming we have a yield curve model with 15 rates and 4 possible values, then it would require 1’073’741’824 (i.e. 415 ) simulations in order to arrive at the complete forecasted solution.
4.2. Conditional Value-at-Risk (CoVaR)
As was mentioned earlier VaR measure does not reveal how severe the losses can be if the extreme shock hits. Therefore, analyzing systemic risk where contagion and spillovers effects are the main concern, VaR cannot capture the exposure of losses conditional on exogenous shocks. Once the prefix “co” is attached to the VaR measure, it becomes VaR conditional on some externality. In other words, CoVaR considers losses surpassing VaR level and is much severe than VaR due to given “bad shock”. The definition of CoVaR in the context of this research can be expressed as follows:
( | )
To put it differently, CoVaR is the VaR of the system conditional on VaR of the selected sector. CoVaR measure has numerous advantages as a risk measure, which were mentioned in Literature review part when investigating the study of Adrian and Brunnermeier (2011). Nevertheless, some of the most desirable properties applicable for particularly this research could be highlighted once more: Especially attractive property of CoVaR is that it can be used for any tail measures to analyse the interdependent risk. Although Adrian and Brunnermeier (2011) applies CoVaR measure to financial system and financial institutions, this measure perfectly suits when
25 analyzing sector-specific systemic risk in the context of financial stock market. CoVaR decomposes system risk conditional on the sector distress into marginal CoVaR measure, exposing the most prominent systemic risk contributors. Cloning property is also very appealing, since it suggests that splitting the sector into smaller “clones” would produce the same CoVaR measure. Though, this property is not applied in this research. Furthermore, CoVaR does not differentiate between the causality and common factor effect. It is helpful property due to the reason it can capture the risk of being “systemic as part of a herd” even if the direct causal relationship does not exist. For instance, if all sectors are exposed to the same common factors, and one of the sectors incurs the distress, it is unlikely it will cause the systemic crisis. In opposition, if the sectors become distressed due to common factor, CoVaR will capture the risk of being “systemic as part of a herd”. Other desired property is tail distribution as CoVaR focuses on more extreme tail distribution than does traditional VaR and reflects shifts in higher moments. Directionality feature of CoVaR states that the system conditional on certain sector is not the same as the CoVaR of the same sector conditional on the system CoVaR. The latter notion would be called Exposure CoVaR.
In this research externality or “bad shock” refers to the certain sector left-tail VaR level. Consequently, this allows investigating the spillover effects among sectors comprising Baltic stock market. According to Adrian and Brunnermeier (2011), the sectors exhibiting coincidental distress with the system, will have higher contribution to systemic risk.
4.3. VaR and CoVaR estimation via quantile regressions
This paper adapts quantile regression method for estimating VaR and CoVaR of each sector in the Baltic stock market.
Quantile regression (sometimes referred to as median regression) initially developed by Koenker and Basset (1978) is a valuable tool in analyzing nonlinear relationship and is becoming more and more popular in various applications. The advantages of using quantile regression as compared to standard ordinary least squares (OLS) regressions is that it does not make any distribution assumption on top of analysis thus reducing the risk of misspecification of the model. It is particularly useful method where data is volatile and extremes are important. There are numerous evidence collected, which confirm financial returns usually exhibit skewness and excess kurtosis. Quantile regression is considered to be a robust and more complete method than OLS since the relationship between the target and independent variables can be analysed throughout the whole distribution rather than focusing only on mean (Hohl, 2009).
Quantile regression gives an accurate picture of co-movement of risk measures during a distress period. Additionally, by expressing the distress of one sector as a function of systemic risk and
26 common economic factors, the quantile regression can separately assess endogenous risks (i.e. shocks from distressed sectors) and exogenous shocks (i.e. shocks from the economic factors) (Fong, Fung and Yu, 2009).
The regression coefficient in standard linear regression stands for one-unit change in the explained variable generated by a one-unit change in the explaining variables tied with corresponding coefficient. The quantile regression metric estimates the change in a certain quantile of the explained variable generated by a one-unit change in the explaining variable (Allen and Singh, 2009).
Figure 4. Illustration of quantile regression
Source: Benoit and Van Den Poel (2009)
While the formulation of the quantile regression is parallel to the standard linear regression model, the estimation of it is rather different and complex. In standard OLS setting the sum of squared vertical distances between observed data points and fitted regression line is minimized by taking negative and positive deviations in squares and thus treating them equivalently. On the contrary, quantile regression model minimizes the sum of absolute vertical distances and weights them by a given quantile without squaring. As depicted in Figure 4, the points above fitted regression line are weighted by q quantile, which is of researcher’s interest, whereas data points below line are weighted by 1-q quantile, where 0 < q < 1 (Hohl, 2009).
In estimating the desired regression quantiles, the minimization problem of absolute error function can be expressed as follows:
ïì ïü rq (u) = Miníq å yi - xibq +(1- q) å yi - xibq ý y³x b y 27 It is solved by using standard linear programming of dual problems for minimization and maximization. There are three main linear programming algorithms, which can be applied to solve the parameters for quantile regression: Simplex method: this method is mostly used because of its stability. The principle of this algorithm is randomly started search for the optimal solution in a special coefficient matrix. This model is the most suitable for moderate size data sets and is built in the mostly used statistical programs as a default method. Interior point method: for huge datasets the simplex method may be not efficient enough, so the alternative algorithm is interior point which instead of searching the optimal solution in matrix setting, solves a sequence of quadratic problems where the relevant interior of the constraint set is approximated by an ellipsoid. This method is considered to be faster than simplex. Smoothing algorithm: this algorithm is also called finite smoothing algorithm. It approximates the linear programming objective function with a smoothing function, so that it can be used to iteratively obtain the solution after a finite number of loops (Chen and Wei, 2005). This study employs the simplex method for solving linear programming algorithms in estimation of quantile regression coefficients due to the reason it is incorporated in Eviews statistical software that is used for this research. Since quantile regression is non-parametric approach, the calculation of confidence intervals is rather more complicated than with regressions based on distribution assumption. Therefore, the confidence interval can be calculated based on three techniques: Sparsity function, where confidence intervals are computed with the basis of the asymptotic normality of the estimated regression quantiles. It is direct and fast approach, but is sensitive to the assumption that data is independent and identically distributed (i.i.d). Though, for computing intervals if data is not i.i.d additional adjustments are required. Inversion of Rank Tests: this method is computation intensive since it is using simplex algorithm for parametric programming in an exponential setting. Therefore, for middle or large sized data is not the most convenient application. Bootstrapping: this method uses residual bootstrap and pairs of variables. For bootstrapping of residuals it is assumed they follow i.i.d. and pairs of x and y are resampled and are allowed to accommodate some form of heteroscedasticity (i.e. deviations of a variables are non-constant). Additionally, this method is more suitable for smaller data sets and is quite robust (Machado and Silva, 2011). What is more, the bootstrapping approach helps in handling heteroscedasticity issue if it is present in a estimated quantile (Isengildina-Massa, Irwin and Good, 2008). This research employs bootstrapping technique for computing confidence intervals and error terms 28 of quantile regression by resampling pairs of XY by 1000 replications. In Eviews, this method is the most convenient for estimating the coefficients of more extreme quantiles because other methods are struggling with this task. The quantile regression is based on the usual linear regression setting as follows: Y =a0 + bX +e (1) Focusing around the specific quantiles (let it be noted " p") we get generic quantile regression expression: ( p) ( p) ( p) Y =a0 + bX +e (2) Focusing on the intended research, the construction of used regressions are explained and exhibited hereinafter in the paper. Given a1,a2...an are defined as the log returns of companies comprising the specific sector and g1,g2....gn are the weights of allocating the returns across the set of the companies in the sector, sector’s return Y was calculated as: i R = a g +a g +...a g given equal weights g =g =g ... =g (3) Yi 1 1 2 2 n n 1 2 3 n i The returns of the selected sectors is denoted as Yt (where “i” is the label of certain sector) and system returns of the system, Yt , represent the returns of the Baltic stock market index. Then in order to capture the time variation of the individual sector returns and returns of the system the conditional distribution is constructed with economic factors as follows: i i i i Regression for sector return: Yt =a + b Mt +et (4) system system system system Regression for system return: Yt =a + b Mt +et (5) Following the task to construct conditionality on specific sector, the whole Baltic stock index return system i (Yt ) was regressed as a function of each sector return ( Yt ) and the set of economic factors on a monthly data under quantile regression: system/i system/i system/i i system/i system/i System conditional on given sector: Yt =a0 +a1 Yt + b Mt +et (6) Where “i” denotes the specific sector, “system” represents the market index of the Baltic stock exchange and “ Mt’’ is the set of economic factors. The most appropriate time lags for economic factors will be adjusted based on the best model fit as well as the underlying justifications. 29 In order to derive VaR and CoVaR estimations, the above mentioned (4), (5) and (6) regressions were run in a monthly data applying quantile regression method of the lowest left-tail 1th, 5th and 10th quantiles with the help of Eviews statistical software. The bottom quantiles of the regressions reflect the most extreme observations located in the distribution and therefore are considered as periods of high distress, which mostly represents the financial crisis period. The predicted values of VaRt and CoVaRt were obtained using the estimated coefficients of the quantile regressions based on the following (7), (8) and (9) derivations in the chosen p-quantile: Y i (p) =ai + b i M +ei t p p t t (7) i ˆ i ˆi VaRt (p) =a p + bpMt system system system system Yt (p) =a p + bp Mt +et (8) system system ˆsystem VaRt (p) =aˆ p + bp Mt system/i system/i system/i i system/i system/i Yt (p) =a0 +a1 Yt +b Mt +et i system/i system/i i ˆsystem/i (9) CoVaRt (p) =aˆ0 +aˆ1 VaRt (p)+ b Mt Finally, once and are estimated, marginal contribution of each sector to the overall systemic risk of the Baltic stock exchange is assessed by exploiting the following 2 strategies: st i i system 1 approach: DCoVaRt (p) = CoVaRt (p)-VaRt (p) nd i i i 2 approach: DCoVaRt (p) = CoVaRt (p)-CoVaRt (50%) First approach of CoVaR, as was proposed by Sheu and Cheng (2011), is constructed as the difference between system CoVaR conditioned on the specific sector and system VaR in the same left-tail quantile. This measure reveals how much each sector contributes to the overall systemic risk of the market in the period of crisis, since VaR of the system is generated in low p-quantiles. Second approach is employed in line with Adrian and Brunnermeier (2011) analytics, which estimates systemic risk as the difference between CoVaR of the system conditional on the sector’s p-quantile VaR level and its median state in the 50th quantile. The second measure of CoVaR reveals how much each sector contributes to systemic risk when it migrates from its medium state 30 of 50% VaR to extreme 5% VaR. The findings established by both methods will allow answering the main question of the study in identifying the sectors mostly contributing to the overall Baltic stock market systemic risk. 4.4. Selection of economic factors explaining stock returns The Baltic countries, often perceived as one homogeneous region, have similar history and economic developments and are small open economies with similar geographical position and natural resources. They are exposed and impacted by common macroeconomic environment and external factors. What is more, all three neighbouring countries have strong linkages to Europe and its currency, the Euro, since all countries pegged their national currencies to euro and Estonia has already adopted the euro in the beginning of 2011. In addition, institutions and markets throughout the three countries are highly concentrated and exhibit strong cross-border activity (IMF, 2007). The set of economic factors is used for the research only as conditioning variables that help to shift the conditional mean of the VaR and CoVaR. Every sector composed of different companies in all three Baltic stock markets could be exposed to those economic factors differently and particular effects of the economic determinants for each sector were not analysed separately. The causal relationship among various economic determinants and stock market returns was discussed and analysed in the literature review part. Based on the analysis, data availability and monthly representation the following 9 economic determinants were selected to be important for all three Baltic stock markets: 4.4.1. Germany stock market index (DAX) European Union is the one of the main export markets for Baltic countries, therefore the prevailing expectations and investor mood of EU is represented by Germany stock market index in this research. This economic factor is also likely to signal some external shocks that ultimately would affect Baltic stock market. It is expected that DAX index will have positive relationship with the Baltic stock index returns. As was discussed in literature review Lee and Stewart (2010) proved that German stock index DAX had a significant effect on the returns of all three Baltic countries in the period of September 2001 to August 2008. 4.4.2. US stock market index (SP500) All three Baltic countries are small open economies therefore external factors influencing their financial market come not only from Europe but also from US which is the mostly observed financial market in terms of investor confidence and expectations. S&P 500 index as well as German DAX is considered to be able to warn the investors of the upcoming shock, so it is 31 expected this factor will capture the part of Baltic financial market trends. What is more, Lee and Stewart (2010) research has revealed that Latvia and Estonia are affected by US market index (SP500) whilst Hsing (2011) in his study showed that US stock market index has more impact for Lithuania’s stock market than German one. Following these reasons, this determinant is also chosen for the research. 4.4.3. Oil price The major share of the companies chosen for research comprises manufacturing industry companies and manufacturing industry plays an important role in the economies of all three Baltic States. According to Benakovic and Posedel (2010) study of 14 Croatian companies stock returns the oil price was statistically significant for those companies, which were specializing in shipping, construction and manufacturing. Given the fact that Croatian market is not very distinct from the Baltic market and the significant number of companies comprising Baltic stock market are exposed to the fluctuations of oil price it is expected that oil price will reflect in Baltic stock market returns. 4.4.4. 3-month EURIBOR interest rate The Baltic countries are highly affected by the EURIBOR interest rate since the major part of the private and public sector debt is denominated in euros. High interest rates would increase the cost of debt for companies dependent on EURIBOR and as economic theory suggest the high interest rates should induce the decrease of stock prices. 3-month EURIBOR is used sine it is the most commonly applied interest rate for private corporate borrowings. 4.4.5. EU government bond yield (10-year) Following the study of Hsing (2011) the EU government bond yield is chosen for the research due to its influence to Lithuanian stock market as was revealed by Hsing. It is considered to represent the world interest rate. High interest rates relative to Lithuanian encourage investors to move their capital abroad fore higher yields and thus impact the stock prices in a negative way. However, higher world interest rate also may induce the appreciation of foreign currency thus making Baltic export cheaper and supporting the exporting businesses (Hsing, 2011). This indicator is chosen for the research based on these implications and is expected to explain the stock returns of the Baltic stock market. 4.4.6. EUR/USD exchange rate In the context of the economic theory the depreciation of the local currencies of three Baltic countries as compared to US Dollar would have several effects such as lower international capital inflows due to inflationary threats, higher volumes of exports since the local production would become cheaper, on the other hand the cost of imports and imported goods would rise and induce higher general price level by affecting the stock prices in either negative or positive way. These 32 inferences were discussed by Hsing (2011) analysis of Lithuanian stock market, so this research uses this determinant due to its potential role in explaining Baltic stock market returns. 4.4.7. Economic sentiment indicator Lithuanian, Latvian and Estonian economies are impacted by the people’s expectations as any other country, so the positive or negative expectations affects the investors’ behaviour and respective risk appetite in stock market performance. Economic sentiment indicator, which includes confidence indicator in industry, in construction, in retail trade and indicator of consumer confidence, was retrieved from Eurostat database and is chosen for the research due to its importance for measuring the general economic environment. This factor is used for the research as the average monthly sentiment indicator value of three Baltic countries. 4.4.8. Inflation Since oil prices are used in this research, inflation plays an complementary role seeking to explain its effect on stock returns, especially in the period of 2006-2008 where the inflation in all three Baltic countries were at the top level and oil prices were especially high. What is more, inflation is characterized to be a warning of the overheated economies. As economic theory suggest high inflation rates reduce the real value of the money and expected cash flows of underlying asset, therefore it is expected that higher inflation will likely result in lower stock returns. This factor is taken for the research as the average monthly rate of Harmonized Index of Consumer Prices (HICP) of three Baltic countries. 4.4.9. Industrial production Industrial production indicator is widely used indicator of economic activity as compared to GDP indicator, which is measured quarterly and is usually lagging 2-3 months. Industrial production reflects economic activity almost coincidently and is calculated on a monthly basis, which is also the reason why this indicator was chosen for the study. Industrial production monthly percentage change was collected from Eurostat by averaging it for all three Baltic countries. 4.5. Model diagnostic tests Seeking to build the empirically robust model it is important to test various assumptions and draw additional insights from the results. Quantile regression confidence intervals and standard errors are estimated very differently as compared to OLS regression. The bootstrapping approach used in this study employs 1000 resamples of XY pair for estimation of confidence intervals and standard errors and due to this approach quantile regressions became less vulnerable to heteroscedasticity issues since bootstrapping helps to correct this property (Isengildina-Massa, Irwin and Good, 2008; Young, Shaffer, Guess, Bensimail and Leon, 2008; Alexander, Harding and Lamarche, 2011). The 33 data stationarity test is critical in building any regression models. By definition, the stationarity property in its weak form means constant mean, constant variance and constant autocovariance structure for each given variable. The non-stationarity data may lead to spurious regression and totally misleading conclusions. The stationarity tests will be performed using Augmented Dickey- Fuller metric to verify that null hypothesis of non-stationarity is rejected. In this context, the estimated critical value of time series should exceed t-statistic of each variable with corresponding p-values lower than 5% significance (Brooks, 2008). Apart from stationarity, other important property is the multicollinearity between the explanatory variables chosen for the regression model and dependent variable. Multicollinearity refers to the strong correlations among the variables and could result in misleading inferences about the relationship between dependent and independent variables if there is strong association between them. The easiest way to investigate the presence of the multicollinearity is to look at the correlation matrix of the chosen regressors. The correlations higher than 0,7 would suggest that variables are collinear (Brooks, 2008). By definition, the correlation coefficient is used to estimate the link between two selected variables and ranges between values of -1 and 1 with correlation coefficient 1 suggesting positive perfect correlation and -1 indicating perfect negative relationship. The more correlation coefficient is close to 1 or -1, the stronger link is between two variables. Yet, correlation values equal to 0 reveal that there is no association between two variables. There are three following types of correlation coefficients: 1) Pearson or ordinary correlation estimates correlation between two variables by random selection and is the most suitable for large number of observations of two variables featuring linear relationship and normality assumption. If the latter two features are not present, ordinary correlation coefficient may be misleading. 2) Spearman’s rank correlation coefficient is used when the assumption of ordinary correlation cannot be realized. In other words, this coefficient is mostly suitable for smaller number of observations and for variables that are not normally distributed, which is likely to occur in this research. It is based on the linear association of paired variables rankings. 3) Other method to measure correlation is Kendall’s tau coefficient, which is similar o Spearman’s rank by the properties taken into account except that it estimates the association between ranks of harmonized pairs, meaning, they both increase or decrease simultaneously, while Spearman’s coefficient measures a straight association between ranks of variables. The correlation analysis used in this research also serves as the indicator for relationship between VaR of each sector and the CoVaR each sector contributes to the systemic risk. According to Adrian and Brunnermeier (2011) the link between these to variables is rather weak, therefore it is important to check this by estimating correlation for those two measures. 34 4.6. Data The data set used in this research comprises monthly series data of quoted main and secondary list companies in the Baltic stock market from January 2003 to December 2012 adjusted for splits, dividends and buybacks totalling to 120 monthly observations. Monthly returns of each company were estimated using natural logarithm of daily stock data and averaging the returns of each month. The period starting from year 2003 was chosen considering the liquidity of the Baltic market, since in earlier years the Baltic stock market exhibited relatively low aggregate liquidity. The equity list employed for measuring systemic risk consists of 47 companies, whereof 30 companies come from the main list and 17 come from the secondary one (for more details see Appendix 1). The selection of the companies was based on the following two criterions: Average monthly turnover of the stock is more than 50 000 EUR; The stock is traded for more than half of the total sample, in this case for more than 60 months out of 120. First North Baltic multilateral trading facility, also called alternative market, was excluded since it does not have legal status as a regulated market of EU. Sectors were allocated based on the companies’ core activity indicated in each company’s profile presented in NASDAQ OMX website and Global Industry Classification Standard developed by Morgan Stanley Capital International (MSCI) and Standard & Poor’s (2010). Utilities sector has the weakest representation in this sample since it is composed of only one company, which satisfied the chosen criterions. Sector’s returns were estimated by joint distribution equally weighting each company returns in a given sector. The detailed allocation of listed Baltic stock market companies into sectors is presented in Table 2. Table 2. The allocation of listed Baltic stock market companies into sectors Notation System Y system Baltic stock market index (OMX Baltic Benchmark GI) Market place Notation Sector Total Latvia Estonia Lithuania Y 1 Industrials 2 4 3 9 Y 2 Consumer discretionary - 5 1 6 Y 3 Food and beverages 1 5 6 Y 4 Manufacturing 3 1 7 11 Y 5 Financials - 1 3 4 Y 6 Natural resources 2 - 2 4 Y 7 Pharmaceuticals 2 - 1 3 Y 8 Telecommunications 1 1 1 3 Y 9 Utilities - 1 - 1 Total 11 13 23 47 Historical values of selected economic determinants were collected from trusted Internet sources on a monthly frequency from January 2003 to December 2012. The notations of each economic factor 35 and their respective sources are indicated in Table 3 below. Table 3. The selected economic factors used in the analysis Notation Economic factor Source of data Bt EU government bond yield (10-year) ECB Statistical Data Warehouse It Inflation HICP retrieved from Eurostat Ot Oil price Europe Brent Oil Spot Price from Thomson Reuters Pt Industrial production index Eurostat St Economic sentiment indicator Eurostat SPt US stock market index (SP500) Yahoo Finance DXt Germany stock market index (DAX) Yahoo Finance USDt EUR/USD exchange rate Eurostat Et 3-month EURIBOR interest rates ECB Statistical Data Warehouse 36 5. EMPIRICAL RESEARCH RESULTS This part of the paper is intended to present the results obtained from estimations and measurements described in the methodological part. The results are presented in the following sequence: Review of descriptive statistics of each sector and index returns and selected economic factors; Review of VaR estimations using quantile regressions of each sector; Review of predicted CoVaR of the Baltic stock index conditional on each of the sectors; Comparison between each sector VaR and estimated contributory index CoVaR. Review and comparison of CoVaR measure of the Baltic stock exchange index conditional on each of the sectors VaR. 5.1. Descriptive statistics of data Appendix 3 depicts returns of each sector calculated using natural logarithm. The dispersion of returns across the sectors of the Baltic stock market exhibits nearly constant variance through time with deeper deviations focusing around the beginning of financial crisis at the end of 2008 and the rebound period at the end of 2009. In September 2008, which was determined to be the official month of the beginning of global financial crisis, all the sectors were affected severely, but the biggest plunge were observed in Consumer discretionary and Financial sectors. Utilities sector, which is comprised of only one Estonian company Tallina Vessi experienced the lowest drop in stock returns, which is not surprising since utilities usually are monopolist companies and are less vulnerable to market swings. At the end of 2009 Consumer discretionary sector returns rebounded the most significantly followed by financial, industrial, manufacturing sectors and the remaining industries. Descriptive general statistics presented in Table 4 reveal that the distributions for 7 sectors are negatively skewed except for Manufacturing and Utilities sectors, which show positive skewness. All sectors except Natural resources exhibit much higher kurtosis as compared to normal one, which is considered to be up to 3. The output suggest that returns does not support the normality assumption since Jarque-Bera statistics for all sectors except Natural resources have lower than 5% significance p-values. Table 4. Descriptive statistics of the sectors’ returns Y4_Manu- Y1_Industrial Y2_Consumer Y3_Food Y5_Financial Y6_Natural Y7_Pharma Y8_Tele Y9_Utilities facture Mean 0,001081 0,010186 0,007617 0,001402 0,006126 0,004582 0,025803 0,001731 -0,001557 Median 0,003989 0,020249 0,012027 -0,000573 0,015081 0,003269 0,033687 0,006106 -0,000707 Maximum 0,254578 0,457434 0,254401 0,301089 0,314981 0,193628 0,381019 0,267622 0,204177 Minimum -0,323989 -0,566304 -0,334453 -0,176336 -0,561124 -0,169163 -0,390070 -0,321864 -0,206178 Std. Dev. 0,095001 0,122659 0,091445 0,081869 0,123329 0,063076 0,102297 0,086682 0,071120 Skewness -0,290103 -0,886557 -0,518295 0,342923 -1,581742 -0,130678 -0,426241 -0,453932 0,155766 Kurtosis 4,653770 8,379906 5,180029 3,919643 9,299285 3,866714 6,209272 5,198941 4,230637 37 Table 4 (cont’d). Descriptive statistics of the sectors’ returns Y4_Manu- Y1_Industrial Y2_Consumer Y3_Food Y5_Financial Y6_Natural Y7_Pharma Y8_Tele Y9_Utilities facture Jarque- Bera 15,35797 160,4366 29,13522 6,580643 248,4431 4,097506 55,13075 28,29780 6,110347 Probability 0,000462 0,000000 0,000000 0,037242 0,000000 0,128896 0,000000 0,000001 0,047115 Sum 0,129675 1,222379 0,914090 0,168202 0,735178 0,549881 3,096386 0,207765 -0,141651 Sum Sq. Dev. 1,073995 1,790384 0,995107 0,797605 1,809993 0,473455 1,245297 0,894139 0,455220 Obs 120 120 120 120 120 120 120 120 91 The statistics of differenced values for each economic determinant are shown in Table 5. From skewness, kurtosis and p-values of Jarque-Bera statistics it is evident that only EU government bond and EUR/USD exchange rate time series is normally distributed, all the remaining 7 variables show rather significant deviations from normality. Table 5. Descriptive statistics of economic factors used in the research PRODUC- BOND INFL OIL SENTIMENT SP DAX EURUSD EURIBOR TION Mean -0,018205 -0,005042 0,658067 0,006443 -0,044818 4,794034 40,87866 0,002098 -0,022239 Median -0,024300 -0,066667 1,460000 0,033333 0,166667 13,81000 95,64000 0,003900 0,003300 Maximum 0,535800 1,666667 13,73000 13,30000 3,300000 121,8800 684,6900 0,083100 0,327400 Minimum -0,588500 -1,300000 -25,65000 -8,900000 -5,766667 -197,6100 -1373,920 -0,104700 -0,945700 Std. Dev. 0,189647 0,503503 6,484151 3,342328 1,557573 48,43819 320,7888 0,033463 0,182447 Skewness 0,022292 0,542257 -1,231666 0,029998 -0,615772 -0,996219 -1,395280 -0,273899 -2,847263 Kurtosis 3,413755 4,186884 5,575447 4,670870 4,046461 5,031481 7,045307 3,492298 14,03766 Jarque-Bera 0,858689 12,81663 62,97545 13,86056 12,95007 40,14626 119,7524 2,689601 764,8605 Probability 0,650936 0,001648 0,000000 0,000978 0,001541 0,000000 0,000000 0,260592 0,000000 Sum -2,166400 -0,600000 78,31000 0,766667 -5,333333 570,4900 4864,560 0,249700 -2,646400 Sum Sq. Dev. 4,244000 29,91475 4961,217 1318,196 286,2721 276858,5 12142843 0,132134 3,927839 Obs 119 119 119 119 119 119 119 119 119 Each economic factor was taken into regression as separate independent variable conditioning the underlying sector returns. The time lag of factors was estimated by selecting the best model fit comparing adjusted pseudo R-squared values and Akaike and Scwarz information criterion, which is usually used to determine the most appropriate model. Oil price, industrial production output, economic sentiment indicator and S&P 500 index appeared to explain the sectoral and index returns better when taken without a time lag. The possible economic reasons for industrial production and economic sentiment to fit the model better without time lag is that this data is especially closely monitored by the investors and financial market participants. Different time lags of SP500 and DAX indices not only improved the statistical power of the bottom quantile regressions but also helped to solve the multicollinearity problem, since the correlation of these two variables when taken with the same time interval exceeded 0,8, therefore was considered to be dangerous for model specification. The final selection of the time lags of all economic factors is as follows: M ={b0Bt-1 + b1It-1 + b2Ot +b3Pt + b4St +b5SPt + b6DXt-1 + b7USDt-1 + b8Et-1} 38 Returns of each sector and economic factors used in the research were tested for stationarity using Augmented Dickey-Fuller test, as was indicated in methodological part, which confirmed the data has no unit roots and is stationary since the p-value attributed to each sector let to reject the hypothesis that data is not stationary. As one might expect, the initial sets of economic factors were subject to non-stationarity, therefore first difference for all 9 economic factors were generated and rechecked for stationarity. Table 6 reports the results of the tests performed on sector returns and modified series of economic factors. Table 6. Stationarity tests for sectors’ returns and economic factors used in the research Augmented Dickey-Fuller test results for sector returns Series t-Stat p-value Series t-Stat p-value Series t-Stat p-value Y_System -7,9269 0,0000 Y4_Manufacture -7,5888 0,0000 Y7_Pharma -6,5576 0,0000 Y1_Industrial -7,8622 0,0000 Y5_Financial -7,6026 0,0000 Y8_Tele -6,8370 0,0000 Y2_Consumer -7,9085 0,0000 Y6_Natural -8,2397 0,0000 Y9_Utilities -9,8789 0,0000 Y3_Food -7,8719 0,0000 Augmented Dickey-Fuller test results for economic factors Series t-Stat p-value Series t-Stat p-value Series t-Stat p-value BOND -9,760888 0,0000 PRODUCTION -9,972959 0,0000 DAX -9,359850 0,0000 INFL -10,93375 0,0000 SENTIMENT -4,036974 0,0018 EURUSD -7,969297 0,0000 OIL -7,100215 0,0000 SP -8,668901 0,0000 EURIBOR -4,462404 0,0004 The correlation matrix of variables used in the study is reported in Appendix 5. It is evident from the correlation coefficients that the associations between economic variables are relatively low with highest Spearman’s rank correlation coefficient between one month lagged German DAX index and oil price standing at 0,37 as well as among EU government bond and 3-month Euribor interest rate equalling to 0,32. Alongside the correlation between economic factors, the correlations between system i dependent variables, Y and Y and economic factors also do not cause serious multicollinearity problems, where the highest correlation coefficient is observed between SP500 and Food sector standing at 0,47 and other pairs having much lower coefficients. However, different picture appear turning to the correlations between index returns and the sectors’ returns, which are used in conditional quantile regression model. Naturally, each sector is the part of the Baltic stock market index, therefore the correlations are expected to be much higher than those within economic factors. These expectations are confirmed in Table 7, showing that returns of the index are near collinear to returns of each of the sector. Although, the model may entail some specification risks, the fact that they are used together with other 9 explanatory variables slightly reduces the risk and the data are analysed further. Table 7. The Spearman’s rank and Kendall’s tau correlations between the system and each of the sector returns System returns and Y1 Y2 Y3 Y4 Y5 Y6 Y7 Y8 Y9 each of the sector: Spearman’s rank correlation 0,854754 0,867017 0,627998 0,727950 0,785459 0,773626 0,705893 0,699283 0,396958 Kendall’s tau correlation 0,684005 0,691819 0,454945 0,535043 0,589255 0,584860 0,522833 0,525275 0,289866 39 Turning to the regression analysis, there are some important results to point. As it is reported in Appendix 6 goodness of fit measured by pseudo R2 appears to explain around 40-50% of the sectors’ returns and 55% of the system returns, which is similar to results obtained by Lopez- Espinosa on financial institutions returns conditioned on certain set of economic variables. The coefficients of predictive quantile regressions capturing the relationship between sector returns and macroeconomic variables differ among sectors. SP500 appeared to be statistically significant at 5% level for all sectors except for Telecommunication sector, however, the coefficients are very small, hence no material effect is recorded. Sentiment has meaningful positive effect on Consumer Discretionary, Food and Manufacturing sectors amounting to 0,02401, 0,01832 and 0,01552, respectively, at 5% significance level, meaning that the one point change in sentiment would result in positive sector return at the 5th quantile. It could be explained by the fact that these sectors are highly dependent on private consumption and general consumer expectations about future prospects of overall economy. Lagged value of long-term EU government bond turned out to have significant negative effect on Food sector at 1% significance, on Manufacturing sector at 5% significance and on the returns of Consumer Discretionary sector at 10% significance level. This result might indicate the sectors’ dependence on the long-term loans since government bonds to some extent affect interest rates on long-term borrowing. Lagged inflation data effectively captured the returns of Natural resources by negative coefficient of -0,05264 at 5% significance and returns of Utilities sector by -0,06318 at 10% significance level. Production factor had significant negative effect only on Financial sector by -0,01372 at 5% significance. Other economic factors did not capture material time-variation of the sector and system returns. CoVaR of 5th quantile regressions reported in Appendix 7 represent conditional comovement of index conditional on each of the sector VaR level. The most significant coefficients capturing time- varying CoVaR measure were sectors’ return except for Food and Utilities sectors, which are known to be less vulnerable to externalities. Some of economic determinants changed the significance, for example EU government bond produced material effect on Pharmaceutical industry with significant negative effect equal to -0,13967. In addition, inflation strongly influenced Manufacturing sector by -0,05271 at 1% significance level. 5.2. Estimation of system and sectors’ Value-at-Risk Value-at-Risk (hereinafter VaR) of each sector and Baltic stock market index was estimated using the coefficients generated from quantile regressions under 1st, 5th and 10th quantile representing VaR at 1%, 5% and 10% maximum tail loss probability and are presented in Table 8. The results reveal that VaR is inversely related to the maximum loss probability level, i.e. the higher VaR, the lower the probability. The highest 1% VaR, which is mostly used for regulatory purposes, was found in 40 Financials, Pharmaceuticals, Consumer discretionary and Industrials representing -0,16825, -0,15038, -0,14992 and -0,13917, respectively. Table 8. System and sector VaR under 1%, 5% and 10% maximum loss probability VaR 1% VaR 5% VaR 10% VaR 50% System: Baltic stock market index -0,07870 -0,07388 -0,06272 0,00623 Y1_Industrials -0,13917 -0,13295 -0,08385 -0,00519 Y2_Consumer discretionary -0,14992 -0,14412 -0,10172 0,00834 Y3_Food -0,11087 -0,09768 -0,05949 -0,00003 Y4_Manufacturing -0,10526 -0,10045 -0,08535 -0,00298 Y5_Financials -0,16825 -0,16825 -0,11056 0,00459 Y6_Natural Resources -0,10397 -0,08402 -0,05615 -0,00216 Y7_Telecommunications -0,10363 -0,09262 -0,06226 0,02295 Y8_Pharmaceuticals -0,15038 -0,09361 -0,07687 -0,00667 Y9_Utilities -0,11072 -0,10489 -0,08505 0,00119 The most widely used VaR at 5% maximum loss probability reports Financials, Consumer Discretionary, Industrials and Utilities sectors having the largest individual risk. The meaning behind these numbers is that, there is 5% chance that the investor would loose more than 16,83% of his portfolio value during one month if he/she chooses Financial sector as a part of the portfolio. The interpretation from other point of view is that, the investors can be 95% sure, that the loss on Financial sector returns will not exceed 16,83% on a monthly basis of the given portfolio on average. The same conclusions apply for 1% and 10% VaR given Consumer discretionary, Industrial and other sectors. Financial sector composed of 4 companies, 3 of them listed in Vilnius stock exchange, is not surprisingly the one with highest VaR at 1% and 5% probability due to its high volatility and vulnerability to not only various market swings but also to the news related with economic sentiment and expectations of the businesses and households. What is more, important shock hit Lithuanian banking system in November 2011 when one of the biggest banks of Lithuania AB Bankas Snoras was nationalized. Although AB Bankas Snoras is not included in this research, the contagion effects apparently were transmitted to other banks, namely AB Šiaulių Bankas and AB Ūkio Bankas. It is worthwile to mention, that AB Ūkio Bankas was also nationalized in January 2013, therefore the extensive volatility and corresponding high VaR of this sector is obvious. Pharmaceutical industry is composed of 3 liquid and also quite volatile Baltic stock market companies producing high VaR at 1% level amounting to -0,15038, however, at 5% level VaR decreases to -0,09361 showing that this sector does not exhibit high VaR through all levels. Again, the implications of these numbers suggest that investors are exposed to 1% probability that they may loose more than 15,04% of their portfolio value on a monthly basis if their choice would be Pharmaceutical industry companies selected for this research. Industrials and Consumer discretionary sectors plays very important role in all three Baltic countries stock exchanges due to the fact that most of the companies composing these two sectors have the highest liquidity and volatility in the Baltic financial market as compared to other sectors 41 and are closely watched and monitored leading companies in their business area. Especially important point is that Industrial sector is composed of companies engaged in construction and transport, which were industries the most severely affected by the financial crisis and the collapse of real estate boom. Figure 5 graphically presents the results of each sector’s VaR at different maximum loss probability levels. Figure 5. Sector VaR under 1%, 5% and 10% probability Sector VaR 0,00000 s s s s s s y g d l l l r e e n n o a a a i a c i i i o t o c r r i i r n c i t l t F u u t i o n s t a t i _ u -0,02000 o a t c c u s e 3 i U e a n d e c i r f n _ Y n a c u F R u I 9 s n _ l _ i m m Y a r a -0,04000 5 1 d r a m r Y u Y M h o t e _ P c a 4 m e _ l N u 8 -0,06000 Y e _ s Y T 6 n _ o Y 7 C Y -0,08000 _ R 2 a Y V -0,10000 -0,12000 -0,14000 -0,16000 -0,18000 VaR 1% VaR 5% VaR 10% 5.3. CoVaR estimation system/i As was described in the methodological part of this study, interdependent CoVaR measure was gauged using coefficients of quantile regressions built on the returns of the system conditioned on each of the sector returns and economic factors and is summarized in Table 9. 1%, 5% and 10% measure stands for the Baltic stock market index conditioned on the each sectors’ VaR level under 1%, 5% and 10% maximum loss probability, respectively, together with its median state represented by 50% . The most significant maximum loss expected in the index given the maximum loss in the underlying sector, , appear to be Natural Resources, Manufacturing, Consumer discretionary and Financials. Table 9. System CoVaR conditional on each sector under 1%, 5% and 10% probability System (Baltic stock market index) conditional on: 1% 5% 10% 50% Y1_Industrials -0,11329 -0,09830 -0,07416 0,00644 Y2_Consumer discretionary -0,10379 -0,10174 -0,07711 0,00579 Y3_Food -0,08530 -0,08355 -0,06382 0,00475 Y4_Manufacturing -0,11461 -0,10763 -0,08508 0,00848 Y5_Financials -0,10178 -0,10018 -0,08022 0,00685 Y6_Natural Resources -0,10779 -0,11062 -0,08194 0,00862 Y7_Telecommunications -0,09214 -0,09315 -0,06965 0,00371 Y8_Pharmaceuticals -0,11958 -0,09468 -0,08163 0,00735 Y9_Utilities -0,09177 -0,09080 -0,08367 0,00279 42 The implications of the numbers reported in Table 9 reveal that the losses of the Baltic stock market index, or the system, would amount to 11,06% given 5% probability in case Natural Resources sector incur its own distress, i.e. its own VaR at 5% maximum loss probability. The equivalent inferences could be drawn to all other sectors at different maximum loss probability levels. When comparing individual 5% VaR of market index equal to -0,07388 and its 5% CoVaR conditioned on each of the sectors’ VaR, naturally CoVaR is much more severe. Overall, the system would incur the deepest losses in case Natural resources, Manufacturing and Consumer Discretionary industries fall into distress. Figure 6 depicts time-varying system CoVaR conditioned on the mentioned 4 sectors. In 2008 the market index was exposed to the highest impact from the distress of these sectors since the scale of potential losses on index values peaked at that time and eroded the portfolio of investors significantly. Natural Resources is the sector, which entails significant monopolistic power and demonstrates the highest volatility in the returns as compared to other sectors, therefore its impact resulted in more than 20% of losses for the index returns in 2008. Figure 6. System CoVaR conditioned on top 4 sectors by year Top 4 highest System CoVaR 5% sectors during the period of 2003-2012 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 -0,05 -0,1 R a V o C -0,15 % 5 -0,2 -0,25 Y6_Natural Resources Y4_Manufacturing Y2_Consumer discretionary Y5_Financials The change of historical volatility of Manufacturing sector was also high, which mostly affected its impact for index CoVaR. What concerns the underlying economic reasons, Manufacturing sector belongs to high importance sectors for all three Baltic economies since this sector is one of the most actively exporting ones. Consumer discretionary sector is composed of very large players of Baltic stock market, such as Apranga, Olympic Entertainment Group, Silvano Fashion Group, Tallina Kaubamaja and other highly volatile and watched companies in the market and it is not surprise that they demonstrate comparatively higher underlying risk and the potential spillover effects for overall market in the Baltic countries. 43 Financial sector appeared to be influencing systemic risk of the market index relatively weaker as compared to its individual risk presented by sharp VaR value. When considering the importance of the companies comprising Financial sector, the Lithuanian banks included do not play a role as the major financial institutions in Lithuania and especially in the Baltic market, since their respective assets and liabilities are far from the share of the largest Scandinavian banks in Lithuania, Latvia and Estonia. 5.4. Marginal sector contribution to the overall systemic risk of the Baltic stock market, CoVaR The empirical evidence of risk co-dependence measure is reported in Table 10 and identifies Natural Resources, Manufacturing, Consumer discretionary and Financial sectors to have the highest marginal contribution to the overall systemic risk in the Baltic stock market based on two DCoVaRi estimation methods. CoVaR was presented using only 5% CoVaR and VaR since it is the mostly used tail loss probability in the financial context and is more representative due to relatively short time-series used. First method of CoVaR, as was proposed by Sheu and Cheng (2011), is constructed as the difference between system CoVaR conditioned on the specific sector and system VaR. This measure reveals how much each sector contributes to the overall systemic risk of the market in the period of crisis, since VaR of the system is generated in the lowest p- quantile. Second method is employed in line with Adrian and Brunnermeier (2011) analytics, which estimates systemic risk as the difference between CoVaR of the system conditional on the sector’s p-quantile VaR level and the median state of each sector. The second measure of CoVaR reveals how much each sector contributes to systemic risk when it migrates from its medium state of 50% VaR to extreme 5% VaR. 5% is chosen to represent the findings of the empirical research since it is the most commonly used in practice. or the marginal contribution to the overall systemic risk gauges by how much the system risk will increase given the particular sector is in the shock. To put it differently, CoVaR gives the percentage point change in the financial systems 5% VaR when a particular sector realizes its own 5% VaR. Table 10. Highest to lowest marginal contribution of each sector to the overall Baltics market systemic risk. 1st approach 2nd approach 5% 5% Y6_Natural Resources -0,036736746 -0,119238188 Y4_Manufacturing -0,03374879 -0,116110358 Y2_Consumer discretionary -0,027856625 -0,107527323 Y5_Financials -0,026297131 -0,107031725 Y1_Industrials -0,024413943 -0,104737567 Y8_Pharmaceuticals -0,020795059 -0,102024051 Y7_Telecommunications -0,019263127 -0,096860034 Y9_Utilities -0,016911319 -0,093584566 Y3_Food -0,009668036 -0,088302025 44 The implications of these findings display the fact that in case these 4 sectors become distressed, the overall systemic risk of Baltic stock market would increase by the given values or in other words incremental losses on the market index would amount to the values reported in Table 10. It is interesting to note, that the estimations based on two methods identify exactly the same highest risk contributing sectors. Although, the method proposed by Adrian and Brunnermeier (2011) displays much stronger effects for the whole system with CoVaR 5% significantly exceeding individual risk of the system in isolation. Given the 2nd method is intended to show the deviation from normal to distressed periods in the system, it delivers much higher nominal effect while 1st method with much lower values reveal how much each sector adds risk when the whole system is already in the crisis event. Figure 7 graphically demonstrates different exposure of CoVaR between two estimated methods. i Figure 7. Marginal contribution of sectors to systemic risk by 2 methods, DCoVaR Analysing the association between Sector VaR vs. CoVaR estimated by 1st approach, Figure 8 reveals that these two measures signal different risks level. 2nd approach ranked the same sectors according to marginal contribution risk, however the absolute values of the latter approach are closer to VaR values. It suggests that it would be useful for investors to employ both risk measures when selecting portfolio since they bring different insights of underlying sector risk. 45 Figure 8. Sector VaR vs. CoVaR assessed by 1st approach Sector VaR -0,18 -0,17 -0,16 -0,15 -0,14 -0,13 -0,12 -0,11 -0,10 -0,09 -0,08 -0,07 -0,01 h c a Y3 o -0,01 r p p a t Y9 -0,02 s 1 Y7 y b -0,02 Y8 R a Y1 V Y5 -0,03 o Y2 C Δ r -0,03 o t c Y4 e S Y6 -0,04 -0,04 Comparing individual risk of each sector and its contribution to the overall systemic risk, the findings are rather different. Highest individual risk was observed in Financial sector with its 5% VaR being at -0,1682 followed by Consumer Discretionary, Industrials and Utilities with -0,14412, -0,13295 and -0,10489 of their 5% VaR, respectively. Table 11 presents the comparison of each sector risk in isolation given 5% maximum loss probability VaR and respective CoVaR measured by 2 methods. Surprisingly, Natural Resources sector exhibits the lowest individual VaR level but is named to be the riskiest sector according to both CoVaR estimations. In contrast, Financial sector has the highest risk in isolation, however, its contribution to the whole system falls behind Natural Resources, Manufacturing and Consumer discretionary. This proves that relying solely on VaR could misinterpret the true underlying risk of the sector. Table 11. Comparison of sector VaR and CoVaR by 2 approaches 1st approach: 2nd approach: i VaR 5% i DCoVaR 5% 5% Y1_Industrials -0,13295 -0,02441 -0,10474 Y2_Consumer discretionary -0,14412 -0,02786 -0,10753 Y3_Food -0,09768 -0,00967 -0,08830 Y4_Manufacturing -0,10045 -0,03375 -0,11611 Y5_Financials -0,16825 -0,02630 -0,10703 Y6_Natural Resources -0,08402 -0,03674 -0,11924 Y7_Telecommunications -0,09262 -0,01926 -0,09686 Y8_Pharmaceuticals -0,09361 -0,02080 -0,10202 Y9_Utilities -0,10489 -0,01691 -0,09358 The correlation coefficients between VaR and CoVaR are investigated by Spearman’s rank correlation and Kendal’s tau correlation as was indicated in methodological part. Correlations of different sectors were averaged to show common correlation for all sectors. Spearman’s correlation coefficients given in Table 12 inform that the relationship among VaR and CoVaR is around 34% 46 when measured by 1st method and 51% by 2nd method, whereas according to Kendall’s tau method correlation varies about 25% by the 1st method and 38% by the 2nd one. Both coefficients are relatively low and indicate weak link between VaR and CoVaR, which goes in line with Adrian and Brunnermeier (2011) results. Table 12. Correlations between CoVaR and VaR 1st approach 2nd approach Spearman’s rank Kendall’s tau Spearman’s rank Kendall’s tau correlation correlation correlation correlation coefficient coefficient coefficient coefficient Sector 5% VaR vs. Sector 5% VaR vs. 0,338082 0,247058 0,506889 0,379915 CoVaR 5% CoVaR 5% Natural resources, Manufacturing, Consumer discretionary and Financial sectors feature higher volatility during the chosen period of time and this is one of the main reasons they appear to be the strongest systemic risk contributors. Figure 9. 5% CoVaR of Natural resources, Manufacturing, Consumer discretionary and Financial sectors by year ΔCoVaR of top 4 sectors by 1st method ΔCoVaR of top 4 sectors by 2nd method 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 0,007 0,01 -0,003 -0,01 -0,03 -0,013 -0,05 -0,023 -0,07 -0,033 -0,09 -0,11 -0,043 -0,13 -0,053 -0,15 -0,063 -0,17 Y2 ΔCoVaR(1) Y4 ΔCoVaR(1) Y5 ΔCoVaR(1) Y6 ΔCoVaR(1) Y2 ΔCoVaR(2) Y4 ΔCoVaR(2) Y5 ΔCoVaR(2) Y6 ΔCoVaR(2) Looking at the trend of the marginal risk contribution to the Baltic stock market as shown in Figure 9 it is evident that Natural resources were the heavily systemic sector in 2008 and 2009 and in the following years the systemic importance faded away but still remained as the leading one. Financial sector appeared to be more systemic in 2005 as it was experiencing the rapidly growing credit and investment activity, though the financial institutions included in this sector are not the most important on Lithuania’s financial landscape and much less to Baltic’s financial landscape. Manufacturing industry kept more or less the same level of systemic contribution during the analysed period. 47 6. DISCUSSION This part of the paper is designed to present the linkages of the main findings of the research and the material presented in the Literature review part as well as to expose the limitations and improvements of the research and provide the implications. 6.1. Main findings The central goal of this research was to answer the question, which sectors of the Baltic stock market contribute mostly to the market systemic risk, by using twofold CoVaR approaches generated through quantile regressions. The principal findings are the following: 1. Calculated VaRs at 5% maximum loss probability for the given period of the index and each of the sectors revealed that the sharpest VaR was observed in Financial sector followed by Pharmaceuticals, Consumer discretionary and Industrials. The results are not surprising given that these sectors experienced the deepest plunge in financial returns during the crisis period. 2. 5% system CoVaR showing how the index would be impacted by distressed sectors identified totally different sectors than VaR. The Natural resources sector appeared to have highest contributory risk to the index. Other highly contributory sectors were named Manufacturing, Consumer discretionary and Financials. The underlying reasons are primarily associated with high volatility in the returns of these sectors and underpin economic importance of these industries in the Baltic stock market. 3. The main finding underpinned by CoVaR was estimated at 5% level by using two strategies: first, CoVaR was calculated as the difference between CoVaR and VaR of the system at the bottom 5th quantile; second, CoVaR was estimated as the difference of CoVaR at 5th bottom quantile and CoVaR at 50th medium quantile representing the tranquil state. The highest marginal contribution to systemic risk was identified by both approaches to be Natural Resources, Manufacturing, Consumer discretionary and Financial sectors. Thereby, in case one or more of these 4 sectors come under distress, the overall systemic risk of the Baltic stock market would increase dramatically. The underpinning reasons for these 4 sectors to be the most significant contributors lies in the underlying volatility of these sectors. 4. Although, the results generated by 2 approaches identified the same rankings of sectors having the highest marginal contribution to the whole system wide risk, the absolute effect captured by 2nd approach was much severe since it assessed the transition from medium state in financial returns to the lowest bottom in the returns. First approach represents CoVaR in the distressed period and shows almost 4 times lower absolute contributory effect since the system is considered to be already in the negative tail event or in its lowest negative quantile. Second approach is reflecting the contributory effect of each sector to systemic risk with CoVaR capturing the exposure risk between medium state period and distressed period. 5. The comparison VaR and CoVaR analytics indicated quite a loose association between two risk measures and confirmed the notion that relying solely on VaR analysis may be not enough to evaluate the extent of risk each sector transmits to the Baltic stock market index. 48 6.2. Findings from the perspective of reviewed literature The findings of the research show that Value-at-Risk (VaR) and Conditional Value-at-Risk (CoVaR) are different measures capturing different risk on the left-hand tail and there is strong mismatch between the sectors identified as the riskiest based on VaR and CoVaR. The highest individual risk was observed in Financials, Consumer Discretionary, Industrials and Utilities while steepest CoVaR lied in Natural Resources, Manufacturing and Consumer discretionary and Financials. The latter finding is parallel to the estimations of Adrian and Brunnermeier (2011), Sheu and Cheng (2011) and Lopez-Espinosa et al. (2012). Turning into cross-sectional sector analysis, Sheu and Cheng (2011) named the most important and volatile sectors in Taiwan as the most systemic and these findings goes in line with this study results, since Natural Resources, Manufacturing and Consumer discretionary and Financials are also the key sectors with the key companies exhibiting high volatility in the Baltic stock market. In contrast to Sheu and Cheng, this study reports much higher correlation between VaR of the each sector and the system conditioned on the sectors although it was estimated using Spearman’s rank and Kendall’s tau correlation coefficients that delivers lower correlation as compared to ordinary coefficient. Furthermore, the research conducted by Lopez-Espinosa et al. (2012) checked two approaches for estimating CoVaR and did not notice any material difference in ranking the most systemic financial institutions. This study equivalently did not find inconsistencies between two approaches, since the same sectors of the Baltic stock exchange were identified as the riskiest in terms of risk transfers to the market index. However, Lopez-Espinosa et al. (2012) did not mentioned the difference of nominal effect between two approaches, which differs at least 4 times and indicates that marginal contribution of the sectors is much lower when estimated by 1st approach focusing on the bottom quantiles of CoVaR and VaR. According to research of Gay (2008) the oil prices did not have significant effect on the financial returns in BRIC countries for the period from 1999 to 2006 and some parallel could be drawn for oil price significance for Baltic stock market since it appeared to be zero. Oppositely to Lee and Stewart (2010) research built on EGARCH model, which found that DAX had material effect on three Baltic countries, this research did not prove DAX had significant effect on index and sector financial returns. The Croatian financial market returns, investigated by Benakovic and Posedel (2010), did not respond to interest rates, oil price and industrial production factors, whereas the Baltic region was impacted by these factors only fragmentally depending on the sector. The findings of Hsing (2011) derived from the analysis of the dependence of market index and various economic factors sampled quarterly did not suggest any similarities to this research since the effects of macro variables were very distinct. Finally, goodness of fit measured by pseudo R2 appears to explain around 40-50% of the sectors’ returns and 55% of the system returns, which is very similar to results obtained by Lopez-Espinosa 49 on financial institutions returns conditioned on certain set of economic variables. 6.3. Limitations of the study Every quantitative research has its limitations and space for potential improvements, therefore this section is intended to expose the vulnerabilities of the implemented study: The first thing to note is issues of data and its representativeness. The Baltic stock market is certainly not the most liquid market in Europe, therefore 47 companies selected for analysis and divided into 9 sectors may be sensitive to the final conclusions. Some companies that did not satisfy the chosen criterion were excluded from the analysis. Also, the returns of each sector were averaged by equally weighting all the companies in the sector, though some companies exhibited much lower market capitalization and trading activity than others, therefore equal weighting might have had some modifications on final conclusions. In addition, only one company represented the utilities sector since there were no companies in this field satisfying the criterion of the research; therefore it does not reflect the common trend in the utilities industry and may be subject to some forms of estimation bias. Other potential shortfall of the research is the notion that the findings made on historical returns can be useful in drawing inferences for today’s world due to the fact that historical trend is not necessarily repeating itself especially having in mind such complex and unpredictable subject as systemic risk. Although Baltic region is regarded to have homogenous nature, there exist some differences between countries and the exposure to selected economic variables varies in practice. Furthermore, each sector and each company comprising the sector may be influenced by those economic factors differently but this study did not attempt to investigate that. According to the model diagnostic tests, quite large part of economic determinants appeared to be insignificant when explaining the financial returns suggesting that the improvement of the model is possible by choosing other factors more relevant for all three Baltic stock market returns. As was discussed in the section of data descriptive statistics, the multicollinearity between index and sector returns was evident and therefore the potential model risk should be addressed for improvement and compared with results of this study. When estimating the 5th regression quantile, relatively low number of observations for assessing VaR and CoVaR measures was used since the research exploits up to 120 observations taken on monthly frequency and the examined 5th regression quantile consisted of maximum 6 observations or even less, therefore there might exist some estimation bias which could be corrected by the change of data frequency or simply by longer data series. Additional disclosures and improvements in the study could be accomplished by using dummy variable indicating crisis period and investigating how the results change. Some additional insights may be achieved when dealing with the asymmetry of returns since Lopez-Espinosa et al. (2012) proved that the correlation between negative and positive returns of financial institutions and the system is very distinct, therefore this might be the 50 case for sectoral returns as well. Next, CoVaR is essentially statistical measure, which does not take into account the economic structure issues, consequently deeper qualitative analysis of each sector’s risk could be added in further researches. 6.4. Implications and proposals for further research This research mainly serves as a valuable tool for investors offering deeper and more holistic insights when choosing the investment portfolios composed of the Baltic stocks and allocating them across sectors, since the optimal risk diversification is surely one of the most important factors for investment decisions. Proposals for future research are the following: On the basis of CoVaR analytics it could be possible to analyse each Baltic country separately with the focus on the company level rather than sector. The problematic of systemic risk measurement could be approached by different techniques, hence the further research could focus on different methods capturing not only the marginal contribution to the system arising from the sectors or companies, but also the opposite relationship showing the impact of system wide risk to each sector or company. What is more, the comparison of two systemic risk methods, for example CoVaR and Marginal Expected Shortfall or Systemic Expected Shortfall could be also carried out in developing this subject broader. 51 7. CONCLUSIONS This study aimed to measure the systemic risk of the Baltic stock market from the perspective of risk spillovers in the left-tail originated from the sectors comprising the stock exchange. Empirical research focused on the Baltic market index, which was treated as the whole system, and 9 sectors composed of 47 selected companies from the Baltic equity list. The sample was constructed on the monthly returns of the index and 9 sectors for the period of January 2003 to December 2012. To reach the goal of the study, methodology based on CoVaR approach through quantile regressions capturing marginal contribution of 9 sectors to the systemic risk was applied. The analysis of the selected literature examining the concept of the systemic risk confirmed that definitions and implications of the subject are truly vague and has many interpretations. On the basis of the literature reviewed, the three basic concepts of systemic risk could be distinguished: first, systemic risk as a recent bank run triggered by liquidity shortages; second, systemic risk as the weakness of the overall financial network; third, systemic risk as the insolvency of a key institution leading to the substantial distress in the overall financial system. The common element in all analysed definitions of systemic risk is a trigger event. The measurement of systemic risk is dictated by the chosen definition and the literature accounts at least 31 different techniques in quantifying this phenomenon. Cross-sectional measures account for the important part of the mostly used techniques. CoVaR method, adopted in this study, was estimated by VaR and CoVaR analytics generated from the set of macroeconomic factors in the quantile regression setting. The literature analysing the dependence between various economic factors and financial returns in different countries disclosed that the influence of the economic variables depends on multiple dimensions, namely, nature of the country, financial entity, period, economic cycle ant other circumstances. The influence of 9 economic factors used in the research with the purpose to explain time-varying financial returns varied across sectors. Empirical findings of tail-risk interdependence between the system and the sectors identified Natural Resources, Manufacturing, Consumer discretionary and Financial sectors to be the most systemic on average. Notably, in case one or more of these 4 sectors come under distress, the overall systemic risk of the Baltic stock market would increase dramatically. The underpinning reasons for these 4 sectors to be the most significant contributors to systemic risk lie in the underlying volatility and the coincident distress with stock market index. Furthermore, widely accepted VaR measure, signalling the individual risk of the sectors, differed substantially from CoVaR estimated by 2 approaches when ranking the most risky sectors. Thus, this evidence shows that assessing the risk of the Baltic stock market from the sector perspective, relying only on VaR is not enough since CoVaR provides additional meaningful time-series and cross-sectional insights on co-dependence variation in tail measures. The contemporary financial landscape has never been more complex, advanced and interconnected and therefore the challenge to quantify the sources of systemic risk in the financial avenue requires 52 multidimensional solutions from various perspectives since this type of risk is extremely difficult to predict. This study supplements the economic theories focusing on risk contagion and the spillovers from externalities in the financial market and could be utilised as a valuable tool for insightful inferences on sector-specific risk in the Baltic stock exchange. 53 8. LIST OF REFERENCES 1. Acharya, V.; Pedersen, L.; Philippon, T.; Richardson, M. 2009. Measuring systemic risk. Technical Report. New York University. 2. 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A Comparison of Multiple Linear Regression and Quantile Regression for Modelling the Internal Bond of Medium Density Fiberboard. Forest Products Journal. Vol. 54, No. 4. 56 9. APENDICES APPENDIX 1. Summary of analysed papers Paper The goal of the paper Methods and data used Results Comments and critique, if any Concept of the systemic risk 1. Schwarcz (2008) Examination of the concept Extensive cross-analysis of The authors stated that institutions are not encouraged to The definition of systemic risk “Systemic risk” of systemic risk, its basic related literature and multi-tiered reduce their individual systemic risk for the sake of the covered quite extensively. parameters and regulatory regulatory approach reducing system-wide risk. The role of central banks as the Though, the proposed solution approaches provider of liquidity of last resort is essential in mitigating for financial contagion problem is systemic risks. too deeply loaded upon the central banks as liquidity providers of the last resort. 2. Hansen (2013) The formalization of systemic Qualitative analysis of recent Author reached 3 systemic risk definitions: Limited systemic risk “Challenges in risk concepts and literature on systemic risk systemic risk as a recent time bank run; measurements were taken into Identifying and measurement challenges problem systemic risk as the weakness of a financial network; account and more detailed Measuring Systemic systemic risk as the potential insolvency of a key analysis of the models could Risk” element. have been performed. Also, paper exposed the challenges of systemic risk measurements such as data availability and validity and model risk. 3. Brady and Markeloff The summary of 65 articles Qualitative content analysis and Author concluded that there is no unified consensus of (2012) “Where is the investigating systemic risk in sample of 65 systemic risk systemic risk definition in the literature. 67% of the ‘system’ in systemic risk order to create objective, related articles using automated systemic risk definitions consider financial system theme, literature? visual representation of content mapping software while 52% of them consider the whole economy, other analytics and concepts Leximancer definitions include events, confidence, institutions, crisis, and markets notions. Systemic risk measurement 4. Bisias et al. (2012) “A Survey of 31 quantitative risk Concise descriptions of each The systemic risk analytics grouped into 6 broad The survey does not attempt to Survey of Systemic Risk measures in economic and systemic risk measure with categories: macroeconomic measures, granular weight potential pluses and Analytics” financial literature required inputs and expected foundations and network measures, forward-looking risk minuses of the risk measures, outputs measures, stress-test measures, cross-sectional measures only describes them. and measures of illiquidity and insolvency 5. “Measuring Systemic Goal of the paper was to The methods used are marginal Results witness strong predicting power in forecasting SES, Risk” by Acharya et al. offer a measure of systemic expected shortfall (MES) and which was calculated through MES and the leverage. The (2010). risk that is based on model systemic expected shortfall stress testing results comply with the Federal Reserve but at the same time is (SES) metrics presented through Supervisory Capital Assessment Program (SCAP) findings practically relevant and could stress tests based on daily used as the benchmark for model check. The optimal be used as the proxy for equity returns and on CDS data. regulatory framework is set as equal to the sum of an regulatory tools. institution-risk element and a systemic-risk element. 6. “Attributing systemic risk To measure the systemic Stylised and hypothetical Authors illustrate that a bank’s contribution to systemic risk The dataset is very hypothetical. to individual institutions” importance of individual dataset. Shapley value could vary substantially from its expected participation in by N. Tarashev et al. institutions using Shapley methodology using VaR and the systemic events (2010) value methods Expected Shortfall (ES) metrics 57 Paper The goal of the paper Methods and data used Results Comments and critique, if any 7. Adrian and Estimation of properties and Quantile regression, VaR, The researchers proved that CoVaR measure gives much Brunnermeier (2011) features of CoVaR and CoVaR, CoVaR using lagged deeper insights when assessing the contributory systemic “CoVaR” CoVaR in estimating macro state variables risk in the financial system. Additionally, the association systemic risk. between VaR and CoVaR appeared to be quite weak suggesting that to estimate VaR is not enough in estimating systemic risk. 8. Valužis and Židulina The revelation of Simulation approach in interbank The paper suggested that the financial institutions are Highly hypothetical approach (2010) “On the systemically relevant credit markets of the Baltic states vulnerable among each other and the collapse of one Contagion in the Baltic institutions and their impact based on the balance sheet data institution will lead to severe pressure to other financial States” to the overall systemic risk to from 2004 to 2009 entities, however it will not cause complete disaster for the Baltic banking system. interbank market. Yet, the spread of distress is limited among small banks. 9. “Systemic Risk in Explore the impact of sector- Taiwan stock market monthly According to the empirical results the top 5 highest VaR Taiwan Stock Market” specific idiosyncratic risk on returns from 2000 to 2009. sectors did not completely correspond to the top 5 highest by Sheu and Cheng the systemic risk in Taiwan Quantile regression, VaR, risk contributory sectors to the system CoVaR. The (2011) stock exchange and attempt CoVaR, CoVaR electronic sector proved to have highest VaR and in line to find links between financial with high systemic CoVaR, however, the banking sector did crises, systemic risk and the not demonstrated high sector VaR, but was named as the idiosyncratic risk of a sector sector having 2nd highest systemic CoVaR. specific shock. 10. Huang, Zhou and Zhu Constructing systemic risk Estimated from CDS spreads Indicator of bank’s contribution to the systemic risk is Fictitious debt portfolio makes (2010) “Systemic Risk indicator relying on real and equivalent equity price linearly dependent on its default probability but non-linear in the results highly hypothetical Contributions” publicly available financial movements, i.e. probability of terms of institution size and asset correlation. The size market information default (PD) of individual banks mostly suggests the underlying relevance of each bank’s and asset return correlations systemic risk contribution and risk-neutral default probability among banks. and correlations of equity returns affects time variations in the marginal contributions. 11. Lopez-Espinosa et al. To identify key determinants CoVaR methodology extended The main determinant encouraging systemic risk (2012) “Short-term driving systemic risk of by recapitalization and crisis contribution of banks appeared wholesale funding, since it Wholesale Funding and financial institutions and test dummies and asymmetric is usually conducted over-the-counter. The size, leverage Systemic Risk: A Global CoVaR methodology for responses. The sample and bank’s assets did not appear to be significant factors CoVaR Approach” asymmetric responses consisted of 54 global financial for systemic risk. It was found that coefficients estimates for institutional from 18 countries negative returns of the banks balance sheet effects were 3 during the period from July 2001 times larger than for positive returns, suggesting significant to December 2009 asymmetry. 12. Jo (2012) “Managing The goal of the paper is to Simulated model considering the The study reveals that in pure balance sheet contagion 3 The inference that CSFC holding systemic risk from the construct more realistically quality of assets and liabilities most vulnerable financial segments in case the local banks only 5% of all assets and perspective of the defined systemic risk and linking liquidity risk to default are securities firms, foreign bank branches and liabilities in the financial system financial network under measuring model associated solvency risk for Korean financial credit unions. In case of macroeconomic shock, local would lead to 92% of system macroeconomic liquidity risk with solvency system composed of 2697 banks, securities firms and credit specialized financial defaults is somewhat doubtful distress” risk various institutions. companies (CSFC) would trigger the financial system whereas the local banks holding downfall. 53% of all assets and liabilities would stimulate only 84% of system collapses. 58 Paper The goal of the paper Methods and data used Results Comments and critique, if any 13. Danielsson et al. (2011) The goal is to test statistical VaR estimation was performed Research found that MES differ significantly when used CoVaR tests were conducted for “Model Risk of Systemic systemic risk measures, by 6 different methods. For with different VaR methodologies and therefore entails high 12 institutions as compared to Risk Measures” namely, Marginal Expected testing MES authors construct model risk. CoVaR results applied for 12 institutions using 92 used by Adrian and Shortfall (MES) and CoVaR the sample of 92 institutions in quantile regression were tested for the width of confidence Brunnermeier, therefore the or model risk US from 1997 to 2010. For intervals and appeared to be quite wide implying the risk of results may be exaggerated and CoVaR tests the sample of 12 misspecification. misleading. In addition, no institutions was used. improvements were proposed. Stock returns and economic factors 14. Gay (2008) “Effect Of To estimate the relationship ARIMA time-series process was The stock market indices were impacted by exchange rate The author could have used Macroeconomic between stock returns and used for every BRIC country in Brazil, Russia and China, however, the relationship did more economic variables, not Variables On Stock economic factors such as stock index from the period of not appear to be very strong. Oil prices had meaningful only exchange rate and oil price. Market Returns For exchange rate and oil price 1999 to 2006. effect only for India stock market index. Four Emerging for BRIC countries Economies: Brazil, Russia, India, And China” 15. Benakovic and Posedel To examine the relationship Methodology was based on The most significant factor explaining the stock returns Authors could have taken (2010) “Do between 14 Croatian stocks multifactor model. The sample were found to be market index, followed by interest rate. economic sentiment into macroeconomic factors and economic factors such comprised monthly data from Some stocks were affected by inflation and oil price. consideration since it is matter for stock returns? as inflation, industrial January 2004 to October 2009. Industrial production had weight on only 3 stocks out of 14. important determinant, Evidence from production, interest rates, especially when analyzing crisis estimating a multifactor market index and oil price. period. model on the Croatian market” 16. Lee and Stewart (2010) To test return and volatility EGARCH model was applied to German DAX appeared to have effect on all 6 markets’ “Asymmetric Volatility interlinkages between Baltic examine return and volatility returns. US index had effect on Latvia and Estonia, UK did and Volatility Spillovers and Nordic stock markets interdependence between the not have any effect on returns analysed. Volatility spillovers in Baltic and Nordic and external indices, such markets. Sample consisted of were observed from Estonia and Sweden to Finland and Stock Markets” German DAX, UK FTSE100 daily stock returns from from Latvia to Sweden. FTSE100 had shown up to transmit and US SP500. September 2001 to August volatility to all Baltic countries. 2008. 17. Elhusseiny and Bae The goal of the research is to ARIMA model was applied for The results show that German and Japan stock returns are (2010) “Risk Factors examine the relationship monthly stock returns from mainly impacted by their market indices. German banking and Industry Stock between different industries January 1985 to January 2005 in industry is significantly affected by term structure trend, Returns: An Empirical and macro factors in Japan Japan and Germany. whereas Japanese banking returns are not affected by this Comparison of German and Germany, and to make variable. Inflation has negative impact on Germany and Japanese Stock the comparison. insurance industry, but no effect on the same industry in Markets” Japan. The foreign exchange, oil prices, industrial production also impact returns depending on industries in Japan and Germany and are mixed. 59 Paper The goal of the paper Methods and data used Results Comments and critique, if any Stock returns and economic factors (cont’d) 18. Hsing (2011) The investigation of the The sample consisted of The research revealed explanatory variables are “Macroeconomic relationship of quarterly data over the period of statistically significant at 1% level. The results show that Variables and the Stock macroeconomic state Q1 2001 to Q4 2009 and the Lithuanian stock market mostly responded to EU area Market: The Case of indicators and Lithuania methodology employed was bond, US stock market index, real GDP and LTL/USD Lithuania” stock market index EGARCH model considering exchange rate. Contrary to the Lee and Stewart (2010) this such factors as real GDP, study names US stock market index more influential than government deficit/GDP ratio, German one. government borrowing/GDP ratio, M2/GDP ratio, M1/GDP ratio, LTL/USD exchange rate, local real interest rate, expected inflation, US government bond yield, EU government bond yield, Germany DAX index, US SP500 index. 19. Hsing and Hsieh (2011) The investigation of the The sample of the analysis was The findings disclose that Poland stock market is positively Impacts of relationship of the period of Q1 2000 to Q2 impacted by industrial production and US and German Macroeconomic macroeconomic state 2010 employing GARCH and stock market indices and gets negative influence from Variables on the Stock indicators and Poland stock ARCH metric and various the government borrowing to GDP ratio, the real Treasury bill Market Index in Poland: market index economic determinants. rate, effective exchange rate, anticipated inflation and EU New Evidence government bond yield as well as quadratic ties with M2/GDP indicator when it exceeds the critical value estimated. 60 APPENDIX 2. The list of companies used in the research No. Ticker Name Core business sector Attributed List Market Total Months Average sector place Turnover of monthly (EUR) trading turnover (min 60) (EUR) (min 50k) 1. EEG1T Ekspress Media and publishing Consumer Main list Tallin 66.625.218 69 965.583 Grupp discretionary 2. OEG1T Olympic Organizing of casino Consumer Main list Tallin 536.120.825 75 7.148.278 Entertainment operations and hotel discretionary Group management 3. APG1L Apranga Retail trade of apparel Consumer Main list Vilnius 155.081.708 120 1.292.348 discretionary 4. BLT1T Baltika Clothing retail Consumer Main list Tallin 206.333.819 120 1.719.448 discretionary 5. SFG1T Silvano Production and sale of Consumer Main list Tallin 182.655.239 120 1.522.127 Fashion Group women's lingerie discretionary 6. TKM1T Tallinna Wholesale and resale Consumer Main list Tallin 231.398.526 120 1.928.321 Kaubamaja of goods discretionary 7. IVL1L Invalda Investment Financials Main list Vilnius 95.714.914 120 797.624 8. SAB1L Šiaulių bankas Banking activities Financials Main list Vilnius 189.477.066 120 1.578.976 9. TPD1T Trigon Property development Financials Secondary Tallin 10.358.801 120 86.323 Property list Development 10. UKB1L Ūkio bankas Banking activities Financials Main list Vilnius 406.902.933 120 3.390.858 11. GUB1L Gubernija Production of beer and Food and Secondary Vilnius 9.083.448 100 90.834 soft drinks beverages list 12. BAL1R Latvijas Production of alcoholic Food and Secondary Riga 27.872.895 120 232.274 balzams beverages beverages list 13. PZV1L Pieno Manufacture of milk and Food and Main list Vilnius 87.210.210 120 726.752 žvaigždės dairy products beverages 14. RSU1L Rokiškio sūris Dairy products Food and Main list Vilnius 185.684.147 120 1.547.368 beverages 15. VLP1L Vilkyškių Milk procurement, Food and Main list Vilnius 12.727.397 80 159.092 pieninė processing and beverages realization of dairy products 16. ZMP1L Žemaitijos Manufacture of various Food and Secondary Vilnius 37.237.778 120 310.315 pienas dairy products beverages list 17. RKB1R Rīgas kuģu Engineering, Industrials Secondary Riga 20.641.050 120 172.009 būvētava constructing and list shipbuilding 18. ARC1T Arco Vara Property development, Industrials Main list Tallin 139.274.981 67 2.078.731 services, construction 19. JRV1T Järvevana Construction Industrials Secondary Tallin 293.187.033 120 2.443.225 list 20. NCN1T Nordecon Construction and Industrials Main list Tallin 143.659.047 80 1.795.738 engineering 21. PTR1L Panevėžio Construction and Industrials Main list Vilnius 178.581.033 120 1.488.175 statybos design trestas 22. LSC1R Latvijas Cargo shipping Industrials Main list Riga 102.926.649 120 810.446 kuģniecība 23. LJL1L Lietuvos jūrų Maritime transport Industrials Secondary Vilnius 27.666.174 120 230.551 laivininkystė list 24. LLK1L Limarko Transportation of cargo Industrials Secondary Vilnius 14.228.488 120 118.571 laivininkystės by water (sea) list kompanija transport 25. TAL1T Tallink Grupp Maritime transportation Industrials Main list Tallin 893.883.733 85 10.516.27 9 26. DPK1R Ditton Manufacturing of Manufacturing Secondary Riga 14.073.731 120 117.281 pievadķēžu vehicle components list rūpnīca 27. DKR1L Dvarčionių Manufacture of ceramic Manufacturing Secondary Vilnius 16.640.650 120 138.672 keramika products list 28. GRG1L Grigiškės Production of sanitary Manufacturing Main list Vilnius 25.440.577 120 212.005 61 No. Ticker Name Core business sector Attributed List Market Total Months Average sector place Turnover of monthly (EUR) trading turnover (min 60) (EUR) (min 50k) and household paper products and corrugated cardboard and packaging from it. 29. KBL1L Klaipėdos Manufacture of Manufacturing Secondary Vilnius 6.346.074 120 52.884 baldai furniture list 30. LME1R Liepājas Ferrous metalurgy Manufacturing Secondary Riga 23.183.601 120 193.197 metalurgs list 31. LNS1L Linas Production and sales of Manufacturing Secondary Vilnius 11.707.616 120 97.563 textile items list 32. SKN1T Skano Group Production of Manufacturing Main list Tallin 4.865.382 64 76.022 fibreboards 33. SNG1L Snaigė Manufacturing of Manufacturing Secondary Vilnius 154.536.108 120 1.287.801 household refrigerators, list freezers and their spare parts 34. UTR1L Utenos Production of knitwear Manufacturing Main list Vilnius 19.914.588 120 165.955 trikotažas and textile 35. VSS1R Valmieras Production of glass Manufacturing Secondary Riga 42.111.014 120 350.925 stikla šķiedra fibre list 36. VBL1L Vilniaus baldai Worldwide Flat Pack Manufacturing Main list Vilnius 14.749.475 120 122.912 honeycomb Furniture Production 37. KNF1L Klaipėdos Export and import of oil Natural Secondary Vilnius 81.851.599 120 682.097 nafta products resources list 38. GZE1R Latvijas Gāze Sale of natural gas Natural Secondary Riga 51.749.115 120 431.243 resources list 39. LDJ1L Lietuvos dujos Import and sale of Natural Main list Vilnius 51.183.991 120 426.533 natural gas resources 40. VNF1R Ventspils nafta Transit of crude oil and Natural Main list Riga 64.410.593 120 536.755 petroleum products resources 41. GRD1R Grindeks Pharmaceuticals Pharmaceuti- Main list Riga 100.264.885 120 835.541 manufacturing cals 42. OLF1R Olainfarm Pharmaceuticals Pharmaceuti- Main list Riga 34.881.491 120 290.679 cals 43. SAN1L Sanitas Manufacture of Pharmaceuti- Main list Vilnius 92.765.310 120 773.044 pharmaceutical cals preparations 44. HAE1T Harju Elekter Designing, production Telecommuni- Main list Tallin 98.519.134 120 820.993 and marketing of cations various electrical engineering and telecommunication systems. 45. SAF1R SAF Tehnika R&D, manufacturing Telecommuni- Main list Riga 72.014.176 104 692.444 and sale of cations telecommunications equipment 46. TEO1L TEO LT Telecommunications Telecommuni- Main list Vilnius 638.457.108 120 5.320.476 cations 47. TVEAT Tallinna Vesi Water supply and Utilities Main list Tallin 404.167.205 91 4.441.398 wastewater collection and treatment services 62 APPENDIX 3. Dispersion of sectors’ returns APPENDIX 4. The histograms of sectors’ returns 63 APPENDIX 5. The Spearman’s rank and Kendall’s tau correlations among economic factors and system i Y and Y Spearman’s PRODUC- SENTI- rank BOND(-1) INFL(-1) OIL SP DAX(-1) EURUSD(-1) EURIBOR(-1) TION MENT correlation BOND(-1) 1,000000 INFL(-1) 0,002911 1,000000 OIL 0,023340 -0,012009 1,000000 PRODUCTION 0,014725 0,118330 0,032898 1,000000 SENTIMENT 0,031207 0,016072 0,127219 0,149445 1,000000 SP -0,055602 0,028400 0,100706 0,145484 0,108852 1,000000 DAX(-1) 0,061899 0,046343 0,368234 0,034216 0,253856 0,060555 1,000000 EURUSD(-1) -0,094421 0,128496 0,080467 -0,095566 -0,032804 0,018439 0,221476 1,000000 EURIBOR(-1) 0,321987 -0,064442 -0,010481 -0,050499 -0,100584 -0,080539 0,071357 0,065869 1,000000 -0,199777 -0,073974 0,066651 0,131893 0,350545 0,423682 0,292212 0,039218 -0,089473 Y1 -0,128715 -0,143512 0,118897 0,204628 0,321049 0,394012 0,251712 0,003767 -0,036853 Y2 -0,193152 -0,030357 0,007883 0,066349 0,350322 0,410113 0,330260 -0,013649 -0,058146 Y3 -0,262064 0,080687 0,098328 -0,037761 0,419658 0,473961 0,209874 0,143479 -0,008791 Y4 -0,214413 0,085879 0,077895 0,019876 0,417397 0,312263 0,347890 -0,003934 -0,072400 Y5 -0,193741 0,018245 0,011730 0,065051 0,226532 0,294617 0,218331 0,009197 -0,085093 Y6 -0,109556 -0,110455 0,186233 0,091815 0,363167 0,342268 0,270107 -0,006116 -0,029384 Y7 -0,096990 -0,072827 0,055152 0,114486 0,448461 0,419318 0,269326 0,154094 0,129989 Y8 -0,148814 -0,126191 0,098336 0,128795 0,413041 0,416834 0,105542 -0,047890 -0,109763 Y9 -0,174184 -0,028406 0,228915 0,053194 -0,046840 0,214588 0,199490 0,030005 -0,185603 Kendall’s tau PRODUC- SENTI- BOND(-1) INFL(-1) OIL SP DAX(-1) EURUSD(-1) EURIBOR(-1) correlation TION MENT BOND(-1) 1,000000 INFL(-1) 0,005505 0,996089 OIL 0,011879 -0,007968 0,999565 PRODUCTION 0,011734 0,070694 0,017963 0,999421 SENTIMENT 0,018688 0,012748 0,091844 0,099812 0,998262 SP -0,040707 0,021730 0,064030 0,094886 0,062726 1,000000 DAX(-1) 0,043025 0,034188 0,253513 0,024772 0,168767 0,045343 1,000000 EURUSD(-1) -0,072287 0,077358 0,056497 -0,066493 -0,012603 0,009126 0,147327 0,999421 EURIBOR(-1) 0,222512 -0,046212 -0,005650 -0,039693 -0,068086 -0,056207 0,049544 0,051572 0,999855 -0,128205 -0,046642 0,042491 0,090110 0,250305 0,297680 0,200488 0,027595 -0,060806 Y1 -0,089621 -0,097436 0,062515 0,138950 0,221490 0,272772 0,166789 -0,001221 -0,022222 Y2 -0,123810 -0,022711 0,001954 0,049573 0,258120 0,285470 0,233211 -0,013919 -0,036874 Y3 -0,178999 0,053968 0,065934 -0,023199 0,298657 0,334799 0,144811 0,091575 -0,008059 Y4 -0,147741 0,058852 0,050305 0,015873 0,298168 0,207814 0,237607 -0,002686 -0,045177 Y5 -0,130647 0,018803 0,006838 0,046642 0,156044 0,205372 0,147253 0,000733 -0,053480 Y6 -0,078388 -0,074481 0,134310 0,054945 0,256166 0,230281 0,179976 -0,001221 -0,022711 Y7 -0,068132 -0,051038 0,036142 0,070085 0,328938 0,295238 0,178510 0,098413 0,079365 Y8 -0,094994 -0,075946 0,075702 0,088645 0,298657 0,295726 0,069597 -0,038828 -0,065690 Y9 -0,124298 -0,011477 0,159707 0,039316 -0,026618 0,137485 0,133089 0,013919 -0,122344 64 system i APPENDIX 6. 5th quantile regressions used for estimating 5% VaR, dependent variable Y and Y PRODUC- Pseudo R- Adjusted Quasi-LR p-value Variable C BOND(-1) INFL(-1) OIL SENTIMENT SP DAX(-1) EURUSD(-1) EURIBOR(-1) TION squared R-squared statistic (Quasi-LR) VaR -0,08239 -0,05028 -0,00951 0,00260 -0,00261 0,00978 0,00095 0,00003 -0,09064 -0,02604 0,587096 0,552687 172,8223 0,000000 (system) Std. Error 0,00840 0,05444 0,01479 0,00208 0,00253 0,00607 0,00018 0,00003 0,33851 0,06041 p-value 0,01235 0,35775 0,52154 0,21537 0,30386 0,10965 0,00000 0,28739 0,78941 0,66733 VaR (Y1) -0,14374 -0,00624 -0,02638 -0,00004 -0,00539 -0,00373 0,00077 0,00004 0,88562 -0,13787 0,406725 0,357286 29,85740 0,000464 Std. Error 0,01792 0,09434 0,03144 0,00220 0,00513 0,01313 0,00038 0,00006 0,55893 0,08850 p-value 0,00000 0,94738 0,40339 0,99865 0,29566 0,77690 0,04487 0,48767 0,11600 0,12222 VaR (Y2) -0,15446 -0,14503 -0,03234 -0,00209 -0,00873 0,02401 0,00201 0,00006 0,03606 0,09823 0,505244 0,464014 54,19576 0,000000 Std. Error 0,01753 0,08556 0,03117 0,00365 0,00504 0,01180 0,00050 0,00005 0,57054 0,12678 p-value 23,33485 0,09294 0,30176 0,56768 0,08593 0,04427 0,00011 0,27186 0,94972 0,44012 VaR (Y3) -0,10160 -0,19514 0,03486 0,00142 -0,00311 0,01832 0,00097 0,00001 -0,06114 0,18822 0,510560 0,469773 62,11557 0,000000 Std. Error 0,01333 0,06909 0,02850 0,00213 0,00339 0,00719 0,00035 0,00004 0,38147 0,12373 p-value 0,00000 0,00564 0,22397 0,50506 0,36071 0,01228 0,00658 0,81749 0,87296 0,13114 VaR (Y4) -0,10577 -0,12609 0,00057 -0,00153 -0,00219 0,01552 0,00050 0,00005 0,20161 -0,00325 0,339289 0,284230 22,51915 0,007371 Std. Error 0,00871 0,05691 0,02161 0,00199 0,00288 0,00648 0,00022 0,00003 0,35372 0,05601 p-value 0,00000 0,02882 0,97882 0,44240 0,44806 0,01841 0,02534 0,12605 0,56987 0,95377 VaR (Y5) -0,18100 0,01658 -0,04303 0,00052 -0,01372 0,01216 0,00138 0,00007 0,84477 -0,07347 0,518341 0,478203 51,40148 0,000000 Std. Error 0,02229 0,09142 0,03954 0,00353 0,00535 0,01524 0,00050 0,00007 0,59330 0,13811 p-value 0,00000 0,85642 0,27893 0,88303 0,01168 0,42667 0,00731 0,33319 0,15737 0,59583 VaR (Y6) -0,08933 -0,05355 -0,05264 -0,00196 -0,00178 0,00539 0,00084 0,00005 0,34767 0,05967 0,283897 0,224222 18,85109 0,026489 Std. Error 0,01487 0,07361 0,02516 0,00299 0,00411 0,01023 0,00035 0,00004 0,40699 0,07427 p-value 0,00000 0,46848 0,03876 0,51278 0,66568 0,59966 0,01806 0,19089 0,39485 0,42343 VaR (Y7) -0,10188 -0,06246 0,00031 -0,00246 -0,00413 0,00486 0,00150 0,00008 0,49597 0,06329 0,521576 0,481708 70,89228 0,000000 Std. Error 0,01499 0,06481 0,02292 0,00210 0,00444 0,00727 0,00035 0,00006 0,41564 0,06236 p-value 0,00000 0,33736 0,98921 0,24548 0,35332 0,50501 0,00004 0,19422 0,23538 0,31239 VaR (Y8) -0,09853 0,01295 -0,04736 -0,00039 -0,00085 0,01581 0,00080 -0,00001 0,24946 -0,07936 0,374102 0,321944 36,02471 0,000039 Std. Error 0,02272 0,09669 0,02875 0,00394 0,00568 0,01346 0,00051 0,00005 0,52402 0,09949 p-value 0,00003 0,89374 0,10243 0,92107 0,88172 0,24256 0,11873 0,91698 0,63500 0,42681 65 PRODUC- Pseudo R- Adjusted Quasi-LR p-value Variable C BOND(-1) INFL(-1) OIL SENTIMENT SP DAX(-1) EURUSD(-1) EURIBOR(-1) TION squared R-squared statistic (Quasi-LR) VaR (Y9) -0,11273 0,01546 -0,06318 0,00015 -0,00330 -0,00496 0,00057 0,00003 0,72271 -0,08321 0,281137 0,201263 16,52551 0,056686 Std. Error 0,01351 0,08169 0,03544 0,00215 0,00450 0,00900 0,00028 0,00004 0,49346 0,07639 p-value 0,00000 0,85039 0,07838 0,94582 0,46572 0,58296 0,04525 0,50364 0,14690 0,27927 system APPENDIX 7. 5th quantile regressions used for estimating 5% CoVaR, dependent variable Y PRODUC- Pseudo R- Adjusted Quasi-LR p-value Variable C Y1 BOND(-1) INFL(-1) OIL SENTIMENT SP DAX(-1) EURUSD(-1) EURIBOR(-1) TION squared R-squared statistic (Quasi-LR) Coefficient -0,04474 0,43287 -0,03948 -0,01515 0,00134 -0,00290 0,00818 0,00065 0,00002 -0,01735 0,02414 0,74077 0,71654 208,636 0,00000 Std. Error 0,00539 0,07372 0,03031 0,01041 0,00115 0,00178 0,00400 0,00016 0,00002 0,15942 0,04894 p-value 0,00000 0,00000 0,19561 0,14850 0,24459 0,10638 0,04344 0,00008 0,36506 0,91352 0,62286 PRODUC- Pseudo R- Adjusted Quasi-LR p-value Variable C Y2 BOND(-1) INFL(-1) OIL SENTIMENT SP DAX(-1) EURUSD(-1) EURIBOR(-1) TION squared R-squared statistic (Quasi-LR) Coefficient -0,04822 0,39102 0,02840 0,00320 0,00102 -0,00036 0,00220 0,00041 -0,00001 0,25195 -0,04992 0,75436 0,73140 183,4337 0,00000 Std. Error 0,00476 0,05068 0,03395 0,01154 0,00101 0,00158 0,00362 0,00012 0,00002 0,17476 0,03435 p-value 0,00000 0,00000 0,40463 0,78233 0,31209 0,81964 0,54393 0,00129 0,66418 0,15230 0,14905 PRODUC- Pseudo R- Adjusted Quasi-LR p-value Variable C Y3 BOND(-1) INFL(-1) OIL SENTIMENT SP DAX(-1) EURUSD(-1) EURIBOR(-1) TION squared R-squared statistic (Quasi-LR) Coefficient -0,08159 0,09480 -0,03007 -0,02290 0,00163 -0,00372 0,00602 0,00096 0,00004 -0,10256 0,00633 0,59558 0,55779 86,21395 0,00000 Std. Error 0,01028 0,15661 0,05673 0,01543 0,00201 0,00321 0,00722 0,00023 0,00003 0,31706 0,07102 p-value 0,00000 0,54626 0,59715 0,14078 0,41912 0,24848 0,40594 0,00006 0,17319 0,74697 0,92912 PRODUC- Pseudo R- Adjusted Quasi-LR p-value Variable C Y4 BOND(-1) INFL(-1) OIL SENTIMENT SP DAX(-1) EURUSD(-1) EURIBOR(-1) TION squared R-squared statistic (Quasi-LR) Coefficient -0,06618 0,45274 0,01716 -0,05271 0,00229 0,00198 0,00447 0,00074 -0,00002 0,08848 0,00113 0,65516 0,62294 106,8204 0,00000 Std. Error 0,00790 0,12952 0,05188 0,01526 0,00163 0,00254 0,00692 0,00018 0,00003 0,25753 0,06429 p-value 0,00000 0,00069 0,74152 0,00079 0,16419 0,43659 0,51985 0,00010 0,47770 0,73184 0,98604 PRODUC- Pseudo R- Adjusted Quasi-LR p-value Variable C Y5 BOND(-1) INFL(-1) OIL SENTIMENT SP DAX(-1) EURUSD(-1) EURIBOR(-1) TION squared R-squared statistic (Quasi-LR) Coefficient -0,05791 0,27905 -0,06951 -0,02209 0,00015 -0,00119 0,00845 0,00054 0,00001 0,30975 -0,03864 0,71100 0,68399 118,2858 0,00000 Std. Error 0,00635 0,07728 0,04167 0,01476 0,00119 0,00182 0,00500 0,00018 0,00002 0,21976 0,04282 p-value 0,00000 0,00047 0,09819 0,13740 0,90084 0,51582 0,09380 0,00285 0,72379 0,16158 0,36889 66 PRODUC- Pseudo R- Adjusted Quasi-LR p-value Variable C Y6 BOND(-1) INFL(-1) OIL SENTIMENT SP DAX(-1) EURUSD(-1) EURIBOR(-1) TION squared R-squared statistic (Quasi-LR) Coefficient -0,06149 0,63327 -0,03963 -0,01852 -0,00036 0,00004 0,01152 0,00085 0,00003 0,03816 0,03860 0,65662 0,62453 115,3247 0,00000 Std. Error 0,00881 0,14292 0,03178 0,01576 0,00192 0,00222 0,00610 0,00020 0,00003 0,24733 0,06344 p-value 0,00000 0,00002 0,21518 0,24262 0,85170 0,98622 0,06159 0,00004 0,38286 0,87767 0,54412 PRODUC- Pseudo R- Adjusted Quasi-LR p-value Variable C Y7 BOND(-1) INFL(-1) OIL SENTIMENT SP DAX(-1) EURUSD(-1) EURIBOR(-1) TION squared R-squared statistic (Quasi-LR) Coefficient -0,07964 0,20410 -0,13967 -0,01563 0,00266 -0,00310 0,01400 0,00054 0,00000 0,02867 0,01143 0,61212 0,57587 97,79166 0,00000 Std. Error 0,00902 0,08772 0,05208 0,01507 0,00203 0,00279 0,00664 0,00022 0,00003 0,35047 0,08449 p-value 0,00000 0,02186 0,00848 0,30205 0,19265 0,26927 0,03715 0,01332 0,99831 0,93496 0,89263 PRODUC- Pseudo R- Adjusted Quasi-LR p-value Variable C Y8 BOND(-1) INFL(-1) OIL SENTIMENT SP DAX(-1) EURUSD(-1) EURIBOR(-1) TION squared R-squared statistic (Quasi-LR) Coefficient -0,06615 0,36726 -0,06149 0,00126 0,00253 -0,00306 0,00984 0,00062 0,00005 -0,08831 0,04192 0,66495 0,63364 117,456 0,000000 Std. Error 0,00711 0,10733 0,03729 0,01350 0,00160 0,00247 0,00615 0,00018 0,00002 0,21167 0,05259 p-value 0,00000 0,00088 0,10206 0,92584 0,11642 0,21965 0,11280 0,00098 0,03423 0,67736 0,42716 PRODUC- Pseudo R- Adjusted Quasi-LR p-value Variable C Y9 BOND(-1) INFL(-1) OIL SENTIMENT SP DAX(-1) EURUSD(-1) EURIBOR(-1) TION squared R-squared statistic (Quasi-LR) Coefficient -0,08181 0,16664 -0,01504 -0,00264 0,00286 -0,00198 0,00803 0,00085 0,00004 -0,06234 -0,06340 0,66295 0,62082 85,57239 0,000000 Std. Error 0,00982 0,19004 0,05510 0,01787 0,00214 0,00278 0,00743 0,00022 0,00004 0,35393 0,06124 p-value 0,00000 0,38317 0,78561 0,88277 0,18611 0,48021 0,28318 0,00021 0,30100 0,86062 0,30366 67