Systematic Risk of Cdos and CDO Arbitrage
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Basel Violations, Volatility Model Variants and Value at Risk: Optimization of Performance Deviations in Banks
Economics and Business Letters 10(3), 240-248, 2021 Basel violations, volatility model variants and value at risk: Optimization of performance deviations in banks Shahid Anjum1,* • Naveeda Qaseem2 1Universiti Teknologi Brunei, Brunei Darussalam 2University of Westminster, United Kingdom Received: 26 October 2020 Revised: 12 February 2021 Accepted: 14 February 2021 Abstract Basel penalties originate from VaR violations, based on the type of variance models and the estimation of VaR thresholds employed by the bank, which leads to excess regulatory capital charge held and can affect banks’ risk taking ability and thus its profitability. Different approaches are used to evaluate the performance of variance models and forecasts of the VaR thresholds i.e. hypothesis tests, back-testing procedures and Basel Accord regulatory calculations for penalty zones. A multi-criteria performance measure named as analytical hierarchy process has been introduced in this study. The approach helps in evaluating and selection of the optimal internal model based on performance evaluation techniques objectively which, in turn, may help in the selection of best volatility internal model for reduction in the VaR violations and thus leaving more capital in the hands of the banks for profitable ventures. Keywords: volatility models; value at risk estimation and performance measures; multivariate techniques; multi-criteria decision making JEL Classification Codes: G21, K23, M16, N20, O16 1. Introduction Volatility is the focus of market risk measurement, management and reporting exercises. Measuring the variance or volatility of the market portfolio is at the heart of measuring VaR threshold for the capital charge of market risk (Bhandari; 2010; Dardac et el.; 2011 & Mehta et el.; 2012) and thus banks’ compliance with Basel regulations (Anjum; 2018). -
The Effect of Systematic Risk Factors on the Performance of the Malaysia Stock Market
57 Proceedings of International Conference on Economics 2017 (ICE 2017) PROCEEDINGS ICE 2017 P57 – P68 ISBN 978-967-0521-99-2 THE EFFECT OF SYSTEMATIC RISK FACTORS ON THE PERFORMANCE OF THE MALAYSIA STOCK MARKET Shameer Fahmi, Caroline Geetha, Rosle Mohidin Faculty of Business, Economics and Accountancy, Universiti Malaysia Sabah ABSTRACT Malaysia has undergone a tremendous transformation in its economic due to the New Economic This has created instability on the changes in the macroeconomic fundamental, called systematic risk. The study aims to determine the effect of the systematic risk towards the performance of the stock returns. The paper aims to use the arbitrage pricing theory (APT) framework to relate the systematic risk with the performance of stock return. The variables chosen to represent the systematic risk were variables that were in line with the transformation policy implemented. The independent variables chosen were interest rate, inflation rate due to the removal of fuel subsidy and the implementation of good and service tax, exchange rate which is influenced by the inflow of foreign direct investment, crude oil price that determines the revenue of the country being an oil exporter and industrial production index that reflect political as well as business news meanwhile the dependent variable is stock returns. All the macroeconomic variables will be regressed with the lagged 2 of its own variable to obtain the residuals. The residuals will be powered by two to obtain the variance which represents the risk of each variable. The variables were run for unit root to determine the level of stationarity. This was followed by the establishment of long run relationship using Johansen Cointegration and short run relationship using Vector Error Correction Modeling. -
Point-In-Time Versus Through-The-Cycle Ratings 1
Point-in-Time versus Through-the-Cycle Ratings 1 Authors: Scott D. Aguais, Lawrence R. Forest, Jr., Elaine Y. L. Wong, Diana Diaz-Ledezma 2 1 The authors would like to acknowledge the many Basel and credit risk related discussions they have had with various members of the Barclays Risk Management Team over the last year. The authors also want to thank, Tim Thompson, Julian Shaw and Brian Ranson for helpful comments. Moodys|KMV also deserves thanks for providing an historical data set of five-year KMV EDF term structures for all of their counterparties. Finally, we thank Zoran Stanisavljevic, for his un-ending credit data management support for our credit research and modelling efforts. All errors remain the responsibility of the authors. The views and opinions expressed in this chapter are those of the authors and do not necessarily reflect the views and opinions of Barclays Bank PLC. 2 Scott Aguais is Director and Head of Credit Risk Methodology. Lawrence Forest and Elaine Wong are Associate Directors, and Diana Diaz-Ledezma is a Manager, all on the Credit Risk Methodology Team. All four work at Barclays Capital, the Investment Banking unit of Barclays. Draft – For Discussion Purposes Only I. Introduction: To qualify for the advanced internal ratings-based (AIRB) status under the Basel II Capital Accord, “internationally active banks” (“banks”) must document the validity not only of their probability of default (PD) estimates but also of the Loss Given Default (LGD) and Exposure at Default (EAD) measures that they plan to use as alternatives to the foundation internal ratings-based (FIRB) assumptions. -
Hong Leong Finance Chooses Kamakura's Suite of Solutions For
Press Contacts: 2222 Kalakaua Avenue, 14th Floor Martin Zorn Honolulu, Hawaii 96815, USA President and COO Telephone 808 791 9888 1-808-791-9888, extension 8700 Fax 808 791 9898 [email protected] www.kamakuraco.com www.kamakuraco.com www.kris-online.com For Immediate Release Hong Leong Finance Singapore Chooses Kamakura’s Suite of Solutions for Asset and Liabilities Management, Funds Transfer Pricing, and Liquidity Management SINGAPORE, Nov 14, 2017 - Hong Leong Finance has signed an agreement to use Kamakura Corporation’s set of solutions for its balance sheet management, funds transfer pricing, and liquidity management processes. The Singapore-listed finance company, part of the Hong Leong Group Singapore, made its decision after doing a comprehensive upgrade exercise involving its risk management processes. “Hong Leong Finance was clear in its risk vision, and wanted to incorporate an integrated framework for balance sheet management, funds transfer pricing and liquidity management initiatives,” said Dr. Clement Ooi, Kamakura’s EVP and Managing Director of Asia-Pacific Operations. “They wanted to load the data once, and reconcile the data once. Kamakura Corporation’s solution fit the bill perfectly, providing the company with an accurate assessment of transaction cashflows that can then be used for gap, duration, and mismatch management, as well providing a crystal-clear understanding of the margins associated with transactions. The company now has an integrated, holistic computation engine—the platform upon which it will build its integrated risk framework. The company will be one of the first to generate both historical and forward-looking views of its balance sheet and gain a transaction-level understanding of profit margins. -
Is the Insurance Industry Systemically Risky?
Is the Insurance Industry Systemically Risky? Viral V Acharya and Matthew Richardson1 1 Authors are at New York University Stern School of Business. E-mail: [email protected], [email protected]. Some of this essay is based on “Systemic Risk and the Regulation of Insurance Companies”, by Viral V Acharya, John Biggs, Hanh Le, Matthew Richardson and Stephen Ryan, published as Chapter 9 in Regulating Wall Street: The Dodd-Frank Act and the Architecture of Global Finance, edited by Viral V Acharya, Thomas Cooley, Matthew Richardson and Ingo Walter, John Wiley & Sons, November 2010. [Type text] Is the Insurance Industry Systemically Risky? i VIRAL V ACHARYA AND MATTHEW RICHARDSON I. Introduction As a result of the financial crisis, the Congress passed the Dodd-Frank Wall Street Reform and Consumer Protection Act and it was signed into law by President Obama on July 21, 2010. The Dodd-Frank Act did not create a new direct regulator of insurance but did impose on non-bank holding companies, possibly insurance entities, a major new and unknown form of regulation for those deemed “Systemically Important Financial Institutions” (SIFI) (sometimes denoted “Too Big To Fail” (TBTF)) or, presumably any entity which regulators believe represents a “contingent liability” for the federal government, in the event of severe stress or failure. In light of the financial crisis, and the somewhat benign changes to insurance regulation contained in the Dodd-Frank Act (regulation of SIFIs aside), how should a modern insurance regulatory structure be designed to deal with systemic risk? The economic theory of regulation is very clear. -
Multi-Factor Models and the Arbitrage Pricing Theory (APT)
Multi-Factor Models and the Arbitrage Pricing Theory (APT) Econ 471/571, F19 - Bollerslev APT 1 Introduction The empirical failures of the CAPM is not really that surprising ,! We had to make a number of strong and pretty unrealistic assumptions to arrive at the CAPM ,! All investors are rational, only care about mean and variance, have the same expectations, ... ,! Also, identifying and measuring the return on the market portfolio of all risky assets is difficult, if not impossible (Roll Critique) In this lecture series we will study an alternative approach to asset pricing called the Arbitrage Pricing Theory, or APT ,! The APT was originally developed in 1976 by Stephen A. Ross ,! The APT starts out by specifying a number of “systematic” risk factors ,! The only risk factor in the CAPM is the “market” Econ 471/571, F19 - Bollerslev APT 2 Introduction: Multiple Risk Factors Stocks in the same industry tend to move more closely together than stocks in different industries ,! European Banks (some old data): Source: BARRA Econ 471/571, F19 - Bollerslev APT 3 Introduction: Multiple Risk Factors Other common factors might also affect stocks within the same industry ,! The size effect at work within the banking industry (some old data): Source: BARRA ,! How does this compare to the CAPM tests that we just talked about? Econ 471/571, F19 - Bollerslev APT 4 Multiple Factors and the CAPM Suppose that there are only two fundamental sources of systematic risks, “technology” and “interest rate” risks Suppose that the return on asset i follows the -
Systematic Risk Influences a Large Number of Assets. a Significant Political Event, for Example, Could Affect Several of the Assets in Your Portfolio
Let's take a look at the two basic types of risk: Systematic Risk - Systematic risk influences a large number of assets. A significant political event, for example, could affect several of the assets in your portfolio. It is virtually impossible to protect yourself against this type of risk. Unsystematic Risk - Unsystematic risk is sometimes referred to as "specific risk". This kind of risk affects a very small number of assets. An example is news that affects a specific stock such as a sudden strike by employees. Diversification is the only way to protect yourself from unsystematic risk. What is 'Systematic Risk'? The risk inherent to the entire market or an entire market segment. Systematic risk, also known as “undiversifiable risk,” “volatility” or “market risk,” affects the overall market, not just a particular stock or industry. This type of risk is both unpredictable and impossible to completely avoid. It cannot be mitigated through diversification, only through hedging or by using the right asset allocation strategy. For example, putting some assets in bonds and other assets in stocks can mitigate systematic risk because an interest rate shift that makes bonds less valuable will tend to make stocks more valuable, and vice versa, thus limiting the overall change in the portfolio’s value from systematic changes. Interest rate changes, inflation, recessions and wars all represent sources of systematic risk because they affect the entire market. Systematic risk underlies all other investment risks. The Great Recession provides a prime example of systematic risk. Anyone who was invested in the market in 2008 saw the values of their investments change because of this market-wide economic event, regardless of what types of securities they held. -
The Volatility of Industrial Stock Returns and an Empirical Test of Arbitrage Pricing Theory
Rev. Integr. Bus. Econ. Res. Vol 4(2) 254 The Volatility of Industrial Stock Returns and An Empirical Test of Arbitrage Pricing Theory Bahri Accounting Department of Politeknik Negeri Ujung Pandang ABSTRACT This study focused on the investigation of the sensitivity of industrial stock returns and empirical test of the model of arbitrage pricing theory (APT). Three issues were the basis of this study. The first issue was the difference in industrial stock returns volatility. The second issue was concerning the validity and robustness of CAPM as well as APT models. And, the third issue was the time-varying volatility with the phenomenon of structural breaks. This study used a nested model approach with the second-pass regression. Method of estimation using ordinary least squares method, ARCH/GARCH method, and the dummy variable method. This study used secondary data for the observation period January 2003 to December 2010 in the Indonesian stock market. This investigation produced three empirical findings. First, there was no difference in sensitivity of industrial stock returns among sectors, based on systematic risk factors. Second, the testing of CAPM and APT multifactor models revealed inconsistent results in testing industrial stocks. But the model of CAPM seemed more valid and robust than APT multifactor was. Finally, there were differences in the risk premium for systematic risks caused by the structural break during the financial crisis in 2008. Keywords: Arbitrage pricing theory, industrial stock return volatility, systematic risk factors, structural-breaks 1. INTRODUCTION Investments are current expenditures of investors to expect a return in the future. There is a variety of investment options for investors; one of them is an investment in common stock. -
Modern Portfolio Theory
Modern portfolio theory “Portfolio analysis” redirects here. For theorems about where Rp is the return on the port- the mean-variance efficient frontier, see Mutual fund folio, Ri is the return on asset i and separation theorem. For non-mean-variance portfolio wi is the weighting of component analysis, see Marginal conditional stochastic dominance. asset i (that is, the proportion of as- set “i” in the portfolio). Modern portfolio theory (MPT), or mean-variance • Portfolio return variance: analysis, is a mathematical framework for assembling a P σ2 = w2σ2 + portfolio of assets such that the expected return is maxi- Pp P i i i w w σ σ ρ , mized for a given level of risk, defined as variance. Its key i j=6 i i j i j ij insight is that an asset’s risk and return should not be as- where σ is the (sample) standard sessed by itself, but by how it contributes to a portfolio’s deviation of the periodic returns on overall risk and return. an asset, and ρij is the correlation coefficient between the returns on Economist Harry Markowitz introduced MPT in a 1952 [1] assets i and j. Alternatively the ex- essay, for which he was later awarded a Nobel Prize in pression can be written as: economics. P P 2 σp = i j wiwjσiσjρij , where ρij = 1 for i = j , or P P 1 Mathematical model 2 σp = i j wiwjσij , where σij = σiσjρij is the (sam- 1.1 Risk and expected return ple) covariance of the periodic re- turns on the two assets, or alterna- MPT assumes that investors are risk averse, meaning that tively denoted as σ(i; j) , covij or given two portfolios that offer the same expected return, cov(i; j) . -
Types of Risk - Systematic and Unsystematic Risk in Finance Post: Gaurav Akrani
Types of Risk - Systematic and Unsystematic Risk in Finance Post: Gaurav Akrani. Date: 1/25/2012. Comments (3). Label: Finance. Types of risk First let's revise the simple meaning of two words, viz., types and risk. In general and in context of this finance article, 1. Types mean different classes or various forms / kinds of something or someone. 2. Risk implies the extent to which any chosen action or an inaction that may lead to a loss or some unwanted outcome. The notion implies that a choice may have an influence on the outcome that exists or has existed. However, in financial management, risk relates to any material loss attached to the project that may affect the productivity, tenure, legal issues, etc. of the project. In finance, different types of risk can be classified under two main groups, viz., The meaning of systematic and unsystematic risk in finance: 1. Systematic risk is uncontrollable by an organization and macro in nature. 2. Unsystematic risk is controllable by an organization and micro in nature. A. Systematic Risk Systematic risk is due to the influence of external factors on an organization. Such factors are normally uncontrollable from an organization's point of view. It is a macro in nature as it affects a large number of organizations operating under a similar stream or same domain. It cannot be planned by the organization. The types of systematic risk are depicted and listed below. 1. Interest rate risk Interest-rate risk arises due to variability in the interest rates from time to time. It particularly affects debt securities as they carry the fixed rate of interest. -
Ratings‐Based Regulation and Systematic Risk Incentives
Comments Welcome Ratings-Based Regulation and Systematic Risk Incentives by Giuliano Iannotta Department of Economics and Business Administration Universitá Cattolica Email: [email protected] and George Pennacchi Department of Finance University of Illinois Email: [email protected] First Draft: August 2011 This Draft: September 2014 Abstract When capital regulation is based on credit ratings, our model shows that a financial institution raises its shareholder value by selecting similarly-rated loans and bonds with the highest systematic risk. This moral hazard occurs if loan and bond credit spreads incorporate systematic risk premia not reflected in credit ratings. Our empirical evidence confirms that similarly-rated bonds have significantly greater credit spreads when their issuers have a higher systematic risk or “debt beta.” Moreover if a financial institution chooses higher-yielding, but equivalently-rated, bonds, its systematic risk and fair capital requirements rise by an economically significant amount. Our theory provides an explanation for prior research documenting that banks and insurance companies took excessive systematic risks. An earlier version of this paper was titled “Bank Regulation, Credit Ratings, and Systematic Risk.” Valuable comments were provided by Tobias Berg, Timotej Homar, Christine Parlour, Andrea Resti, Francesco Saita, João Santos, Andrea Sironi, René Stulz, Andrew Winton and participants of the 2011 International Risk Management Conference, the 2011 Bank of Finland Future of Risk Management -
Systemic Risk and Insurance Regulation †
risks Article Systemic Risk and Insurance Regulation † Fabiana Gómez 1,* and Jorge Ponce 2 1 Department of Accounting and Finance, University of Bristol, Bristol BS8 1TH, UK 2 Banco Central del Uruguay, Montevideo 11100, Uruguay; [email protected] * Correspondence: [email protected]; Tel.: +44-117-39-41485 † The views in this paper are those of the authors and not of the Institutions to which they are affiliated. Received: 25 June 2018; Accepted: 25 July 2018; Published: 27 July 2018 Abstract: This paper provides a rationale for the macro-prudential regulation of insurance companies, where capital requirements increase in their contribution to systemic risk. In the absence of systemic risk, the formal model in this paper predicts that optimal regulation may be implemented by capital regulation (similar to that observed in practice, e.g., Solvency II ) and by actuarially fair technical reserve. However, these instruments are not sufficient when insurance companies are exposed to systemic risk: prudential regulation should also add a systemic component to capital requirements that is non-decreasing in the firm’s exposure to systemic risk. Implementing the optimal policy implies separating insurance firms into two categories according to their exposure to systemic risk: those with relatively low exposure should be eligible for bailouts, while those with high exposure should not benefit from public support if a systemic event occurs. Keywords: insurance companies; systemic risk; optimal regulation JEL Classification: G22, G28 1. Introduction Insurance firms play an important role in the economy as providers of protection against financial and economic risks. In recent years, their contribution to systemic risk has increased.