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Ism University of Management and Economics Master Of 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
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