Quantitative Financial Risk Management

CONSTANTIN ZOPOUNIDIS, PHD, is professor of Financial Engineering and Operations Research at Technical University of Crete in Greece and distinguished research professor at Audencia Nantes School of Management in France. EMILIOS GALARIOTIS, PHD (Dunelm), HDR, is professor of Finance at Audencia Nantes School of Management in France. He is the founder and director of the Centre for Financial and Risk Management and head of research in the area of finance, risk, and accounting performance at Audencia. He is also joint-Head of the Accounting and Finance Department.

• Preface xviiAbout the Editors xixSection One Supervisory Risk ManagementChapter 1 Measuring Systemic Risk: Structural Approaches 3Raimund M. Kovacevic and Georg Ch. PflugSystemic Risk: Definitions 4From Structural Models to Systemic Risk 6Measuring Systemic Risk 10Systemic Risk and Copula Models 15Conclusions 20References 20Chapter 2 Supervisory Requirements and Expectations for Portfolio-Level Counterparty Credit Risk Measurement and Management 22Michael Jacobs Jr., PhD, CFAIntroduction 22Review of the Literature 25Supervisory Requirements for CCR 26Conceptual Issues in CCR: Risk versus Uncertainty 41Conclusions 44References 44Chapter 3 Nonperforming Loans in the Bank Production Technology 46Hirofumi Fukuyama and William L. WeberIntroduction 46Selective Literature Review 47Method 51Empirical Application 57Summary and Conclusion 65Appendix 3.1 Bank Names and Type 66References 67Section Two Risk Models and MeasuresChapter 4 A Practical Guide to Regime Switching in Financial Economics 73Iain Clacher, Mark Freeman, David Hillier, Malcolm Kemp and Qi ZhangA Brief Look at Markov Regime Switching in Academic Economics and Finance 74Regime Switching and Interest Rate Processes 75Regime Switching and Exchange Rates 76Regime Switching, Stock Returns, and Asset Allocation 77Single-Asset Markov Models 79Two-State Estimation 82Three-State Estimation 84Markov Models for Multiple Assets 85Practical Application of Regime Switching Models for Investment Purposes 87Intuitive Appeal of Such Models 87Implementation Challenges 89Selecting the “Right" Model Structure 89Calibrating the Selected Model Type to Suitable Data 90Drawing the Right Conclusions from the Model 93References 95Chapter 5 Output Analysis and Stress Testing for Risk Constrained Portfolios 98Jitka Dupačová and Miloš KopaIntroduction 98Worst-Case Analysis 107Stress Testing via Contamination 110Conclusions and New Problems 122References 122Chapter 6 Risk Measures and Management in the Energy Sector 126Marida Bertocchi, Rosella Giacometti and Maria Teresa VespucciIntroduction 126Uncertainty Characterization via Scenarios 128Measures of Risks 132Case Studies 137Summary 147References 147Section Three Portfolio ManagementChapter 7 Portfolio Optimization: Theory and Practice 155William T. ZiembaStatic Portfolio Theory 155Importance of Means 163Stochastic Programming Approach to Asset Liability Management 167Siemens InnoALM Pension Fund Model 182Dynamic Portfolio Theory and Practice: The Kelly Capital Growth Approach 194Transactions Costs 199Some Great Investors 201Appendix 7.1: Estimating Utility Functions and Risk Aversion 206References 208Chapter 8 Portfolio Optimization and Transaction Costs 212Renata Mansini, Wlodzimierz Ogryczak and M. Grazia SperanzaIntroduction 212Literature Review on Transaction Costs 215An LP Computable Risk Measure: The Semi-MAD 221Modeling Transaction Costs 223Non-Unique Minimum Risk Portfolio 232Experimental Analysis 234Conclusions 237Appendix 238References 239Chapter 9 Statistical Properties and Tests of Efficient Frontier Portfolios 242c J AdcockIntroduction 242Notation and Setup 245Distribution of Portfolio Weights 247Empirical Study 255Discussion and Concluding Remarks 267References 268Section Four Credit Risk ModellingChapter 10 Stress Testing for Portfolio Credit Risk: Supervisory Expectations and Practices 273Michael Jacobs Jr.Introduction and Motivation 273Conceptual Issues in Stress Testing: Risk versus Uncertainty 276The Function of Stress Testing 277Supervisory Requirements and Expectations 280Empirical Methodology: A Simple ST Example 281Conclusion and Future Directions 291References 293Chapter 11 A Critique of Credit Risk Models with Evidence from Mid-Cap Firms 296David E. Allen, Robert J. Powell and Abhay K. SinghIntroduction 296Summary of Credit Model Methodologies 297Our Empirical Methodology 302Critique 303Conclusions 310References 310Chapter 12 Predicting Credit Ratings Using a Robust Multicriteria Approach 312Constantin ZopounidisIntroduction 312Credit Scoring and Rating 315Multicriteria Methodology 319Empirical Analysis 325Conclusions and Future Perspectives 330References 331Section Five Financial MarketsChapter 13 Parameter Analysis of the VPIN (Volume-Synchronized Probability of Informed Trading) Metric 337Jung Heon Song, Kesheng Wu and Horst D. SimonIntroduction 337Definition of VPIN 341Computational Cost 346Optimization of FPR 348Uncertainty Quantification (UQ) 353Conclusion 360References 362Chapter 14 Covariance Specification Tests for Multivariate GARCH Models 364Gregory KoutmosIntroduction 364Covariance Specification Tests 365Application of Covariance Specification Tests 367Empirical Findings and Discussion 368Conclusion 370References 370Chapter 15 Accounting Information in the Prediction of Securities Class Actions 372Vassiliki BallaIntroduction 372Literature Review 375Methodology 376Data 378Results 387Conclusions 394References 395About the Contributors 399Glossary 413Index 421