Table of Contents

Title Page

Dedication

Preface

About the Editors

Section One: Supervisory Risk Management

Chapter 1: Measuring Systemic Risk: Structural Approaches

Systemic Risk: Definitions
From Structural Models to Systemic Risk
Measuring Systemic Risk
Systemic Risk and Copula Models
Conclusions
References

Chapter 2: Supervisory Requirements and Expectations for Portfolio-Level Counterparty Credit Risk Measurement and Management

Introduction
Review of the Literature
Supervisory Requirements for CCR
Conceptual Issues in CCR: Risk versus Uncertainty
Conclusions
References

Chapter 3: Nonperforming Loans in the Bank Production Technology

Introduction
Selective Literature Review
Method
Empirical Application
Summary and Conclusion
Appendix 3.1 Bank Names and Type
References

Section Two: Risk Models and Measures

Chapter 4: A Practical Guide to Regime Switching in Financial Economics

A Brief Look at Markov Regime Switching in Academic Economics and Finance
Regime Switching and Interest Rate Processes
Regime Switching and Exchange Rates
Regime Switching, Stock Returns, and Asset Allocation
Single-Asset Markov Models
Two-State Estimation
Three-State Estimation
Markov Models for Multiple Assets
Practical Application of Regime Switching Models for Investment Purposes
Intuitive Appeal of Such Models
Implementation Challenges
Selecting the “Right” Model Structure
Calibrating the Selected Model Type to Suitable Data
Drawing the Right Conclusions from the Model
References

Chapter 5: Output Analysis and Stress Testing for Risk Constrained Portfolios

Introduction
Worst-Case Analysis
Stress Testing via Contamination
Conclusions and New Problems
References

Chapter 6: Risk Measures and Management in the Energy Sector

Introduction
Uncertainty Characterization via Scenarios
Measures of Risks
Case Studies
Summary
References

Section Three: Portfolio Management

Chapter 7: Portfolio Optimization: Theory and Practice

Static Portfolio Theory
Importance of Means
Stochastic Programming Approach to Asset Liability Management
Siemens InnoALM Pension Fund Model
Dynamic Portfolio Theory and Practice: The Kelly Capital Growth Approach
Transactions Costs
Some Great Investors
Appendix 7.1: Estimating Utility Functions and Risk Aversion
References

Chapter 8: Portfolio Optimization and Transaction Costs

Introduction
Literature Review on Transaction Costs
An LP Computable Risk Measure: The semi-MAD
Modeling Transaction Costs
Non-Unique Minimum Risk Portfolio
Experimental Analysis
Conclusions
Appendix
References

Chapter 9: Statistical Properties and Tests of Efficient Frontier Portfolios

Introduction
Notation and Setup
Distribution of Portfolio Weights
Empirical Study
Discussion and Concluding Remarks
References

Section Four: Credit Risk Modelling

Chapter 10: Stress Testing for Portfolio Credit Risk: Supervisory Expectations and Practices

Introduction and Motivation
Conceptual Issues in Stress Testing: Risk versus Uncertainty
The Function of Stress Testing
Supervisory Requirements and Expectations
Empirical Methodology: A Simple ST Example
Conclusion and Future Directions
References

Chapter 11: A Critique of Credit Risk Models with Evidence from Mid-Cap Firms

Introduction
Summary of Credit Model Methodologies
Our Empirical Methodology
Critique
Conclusions
References

Chapter 12: Predicting Credit Ratings Using a Robust Multicriteria Approach

Introduction
Credit Scoring and Rating
Multicriteria Methodology
Empirical Analysis
Conclusions and Future Perspectives
References

Section Five: Financial Markets

Chapter 13: Parameter Analysis of the VPIN (Volume-Synchronized Probability of Informed Trading) Metric

Introduction
Definition of VPIN
Computational Cost
Optimization of FPR
Uncertainty Quantification (UQ)
Conclusion
References

Chapter 14: Covariance Specification Tests for Multivariate GARCH Models1

Introduction
Covariance Specification Tests
Application of Covariance Specification Tests
Empirical Findings and Discussion
Conclusion
References

Chapter 15: Accounting Information in the Prediction of Securities Class Actions

Introduction
Literature Review
Methodology
Data
Results
Conclusions
References

About the Contributors

Chris Adcock
David E. Allen
Vassiliki Balla
Marida Bertocchi
Iain Clacher
Jitka Dupačová
Mark Freeman
Hirofumi Fukuyama
Rosella Giacometti
David Hillier
Michael Jacobs JR., Ph.D., CFA
Malcolm Kemp
Miloš Kopa
Gregory Koutmos
Raimund M. Kovacevic
Renata Mansini
Wlodzimierz Ogryczak
Georg Ch. Pflug
Robert J. Powell
Horst D. Simon
Abhay K. Singh
Jung Heon Song
M. Grazia Speranza
Maria Teresa Vespucci
William L. Weber
Kesheng Wu
Qi Zhang
William T. Ziemba
Constantin Zopounidis

Glossary

Index

End User License Agreement

List of Illustrations

Chapter 1: Measuring Systemic Risk: Structural Approaches

Figure 1.1 The distribution of the number of affected banks using the assumptions of Example 2. Left: the uncorrelated case (ρ = 0). Right: the weakly correlated case (ρ = 0.2).
Figure 1.2 The distribution of the number of affected banks using the assumptions of Example 2. Left: the medium correlated case (ρ = 5). Right: the highly correlated case (ρ = 0.8).
Figure 1.3 Left: All banks are independent, VaR_0.99 = 25; Right: All correlations are $c01-math-0108$ , VaR_0.99 = 29.
Figure 1.4 Left: All correlations are $c01-math-0109$ , VaR_0.99 = 41; Right: All correlations are $c01-math-0110$ , VaR_0.99 = 57.
Figure 1.5 The system consists of two independent subsystems with internal correlations $c01-math-0111$ . Left: $c01-math-0112$ , VaR_0.99 = 28; Right: $c01-math-0113$ , VaR_0.99 = 35.
Figure 1.6 The system consists of two independent subsystems with internal correlations $c01-math-0114$ . Left: $c01-math-0115$ , VaR_0.99 = 44; Right: One subsystem has $c01-math-0116$ , the other $c01-math-0117$ , VaR_0.99 = 32.

Chapter 2: Supervisory Requirements and Expectations for Portfolio-Level Counterparty Credit Risk Measurement and Management

Figure 2.1 Expected positive exposure for CCR.
Figure 2.2 Effective EE and effective EPE for CCR.
Figure 2.3 Stylized representation of economic capital.

Chapter 3: Nonperforming Loans in the Bank Production Technology

Figure 3.1 Actual, frontier, and optimal loans.
Figure 3.2 Actual, frontier, and optimal securities investments.
Figure 3.3 Actual, frontier, and optimal nonperforming loans.

Chapter 4: A Practical Guide to Regime Switching in Financial Economics

Figure 4.1 Standard Deviation of US Stock Returns 1871–2014.
Figure 4.2 Probability of being in a Bear State (60 Month Average).

Chapter 5: Output Analysis and Stress Testing for Risk Constrained Portfolios

Figure 5.1 Lower and upper bound for a concave optimal value function.
Figure 5.2 Comparison of minimal (CVaR(t)) value of mean-CVaR model with lower bound (LB), upper bound (UB) and the estimated upper bound (EUB).
Figure 5.3 Comparison of minimal mean loss with its lower bound (LB) and upper bound (UB).

Chapter 6: Risk Measures and Management in the Energy Sector

Figure 6.1 The spot switching model.
Figure 6.2 The spot switching model.
Figure 6.3 Scenario for CO₂ price. (EUA).
Figure 6.4 The hydro system.
Figure 6.5 Profit distribution for different risk aversion parameters using CVaR with α equal to 5 percent, budget = 3.84 G€ and a market electricity price of 106€/MWh.
Figure 6.6 Profit distribution for different risk aversion parameters using CVaR with α equal to 5 percent, budget = 14.5 G€ and a market electricity price of 106 €/MWh.
Figure 6.7 Percentage of new capacity installed for the case of budget = 14.5 G€, CVaR and ρ = 0.5.
Figure 6.8 Net present value of optimal expected profit when investment is in a single technology (only the available technologies are shown).

Chapter 7: Portfolio Optimization: Theory and Practice

Figure 7.1 Two efficient frontiers: (a) Markowitz mean-variance efficient frontier; (b) Tobin's risk-free asset and separation theorem.
Figure 7.2 Utility of wealth function for Donald Hausch.
Figure 7.3 Risk aversion functions for Donald Hausch (a) absolute risk aversion function estimated by the certainly equivalent method (1) and gain and loss equivalent method; (b) relative risk aversion function estimated by the certainly equivalent method (1) and gain and loss equivalent method (2).
Figure 7.4 Functional forms asset weights: (a) Quadratic monthly $c07-math-0101$ ; (b) Exp monthly $c07-math-0102$ ; (c) Special exp monthly $c07-math-0103$ ; (d) Neg power monthly $c07-math-0104$ .
Figure 7.5 Mean variance and second order stochastic dominance: (a) Dominance does not exist; (b) Dominance exists.
Figure 7.6 Mean percentage cash equivalent loss due to errors in inputs.
Figure 7.7 Average turnover for different percentage changes in means, variances, and covariances.
Figure 7.8 Optimal equity and bond allocations in period 1 of the InnoALM model.
Figure 7.9 The effects of state-dependent correlations: optimal weights conditional on quintiles of portfolio weights in periods 2 and 5 of the InnoALM model: (a) Stage 2; (b) Stage 5.
Figure 7.10 Two distributions with the same mean and variance.
Figure 7.11 Time and events in institutional and individual ALM models.
Figure 7.12 Asset weights over time with a stochastic control continuous time Merton type model.
Figure 7.13 Example portfolios: (a) Initial portfolios of the three strategies; (b) Contingent allocations at year one.
Figure 7.14 The optimal stochastic strategy dominates fixed mix.
Figure 7.15 Comparison of advantage of stochastic programming over fixed mix model in and out of sample: (a) In-sample; (b) Out-of-sample.
Figure 7.16 Layout of the Russell-Yasuda Kasai model.
Figure 7.17 Yasuda Kasai's asset liability decision process.
Figure 7.18 Emerging and developed market returns, 1900–2013
Figure 7.19 Real equity returns and per capita GDP, 1900–2013
Figure 7.20 Elements of InnoALM.
Figure 7.21 S&P500 index and U.S. government bond returns, 2000–2002.
Figure 7.22 Rolling correlations between U.S. equity and government bond returns.
Figure 7.23 Cumulative monthly returns for different strategies.
Figure 7.24 A typical full Kelly wealth path.
Figure 7.25 Final wealth trajectories: Ziemba-Hausch (1986) Model: (a) Highest; (b) Lowest.
Figure 7.26 Mutuel pools for 1983 Kentucky Derby.
Figure 7.27 Four Great Investors: (a) John Maynard Keynes, King's College, Cambridge, 1927–1945; (b) Bill Benter, 1989–1991; (c) Ed Thorp, Princeton Newport Hedge Fund 1969–1988; (d) Jim Simons, Renaissance Medallion Hedge Fund, 1993–2005.
Figure 7.28 The wealth paths and return distributions of Berkshire Hathaway, Quantum, Tiger, Windsor, the Ford Foundation, and the S&P500, 1985–2000: (a) Growth of assets, log scale, various high performing funds, 1985–2000. Source: Ziemba (2003); (b) Return distributions of all the funds, quarterly returns distribution, December 1985 to March 2000. Source: Ziemba (2005)
Figure 7.29 Medallion Fund, January 1993 to April 2005: (a) Rates of return in increasing order by month, %; (b) Wealth over time.
Figure 7.30 Riskiness as a percentage of maximum variance versus $c07-math-0199$ .

Chapter 8: Portfolio Optimization and Transaction Costs

Figure 8.1 Pure fixed cost structure (left-hand figure) and proportional cost with minimum charge structure (right-hand figure).
Figure 8.2 A convex piecewise linear cost function.
Figure 8.3 A concave piecewise linear cost function.

Chapter 10: Stress Testing for Portfolio Credit Risk: Supervisory Expectations and Practices

Figure 10.1 Historical charge-off rates. Reprinted by permission from Inanoglu, Jacobs, Liu, and Sickles (2013).
Figure 10.2 Distribution of VaR. Reprinted by permission from Inanoglu and Jacobs (2009).
Figure 10.3 Stylized representation of economic capital.
Figure 10.4 Correlations among risk types. Reprinted with permission from Inanoglu and Jacobs 2009.
Figure 10.5 Stressed regulatory capital example.
Figure 10.6 Bond return index return—Time series (January 2, 1997, to December 19, 2011).
Figure 10.7 Default rate data—Time series.
Figure 10.8 VIX and C&I charge-off rate—Time series.
Figure 10.9 Fama–French factors—Time series.
Figure 10.10 GDP, CPI, and oil prices—Time series.
Figure 10.11 Stress test results—Graphs of unexpected vs. expected loss in stagflation scenario.

Chapter 12: Predicting Credit Ratings Using a Robust Multicriteria Approach

Figure 12.1 The process for developing credit-rating models.
Figure 12.2 Piecewise linear modeling of a marginal value function.
Figure 12.3 Marginal value functions.

Chapter 13: Parameter Analysis of the VPIN (Volume-Synchronized Probability of Informed Trading) Metric

Figure 13.1 E-mini S&P 500s VPIN metric on May 6.
Figure 13.2 Idealized trading model.
Figure 13.3 Flowchart of how a VPIN event is classified.
Figure 13.4 Time (seconds) needed to construct volume bars with different nominal prices.
Figure 13.5 MADS algorithm.
Figure 13.6 Pseudocode of VNS.
Figure 13.7 Semilog of Sobol indices of the four parameters.

Chapter 15: Accounting Information in the Prediction of Securities Class Actions

Figure 15.1 Majority voting rule.
Figure 15.2 Number of SCAs per year.

List of Tables

Chapter 1: Measuring Systemic Risk: Structural Approaches

Table 1.1 Conditional VaR and correlation for example 1

Chapter 3: Nonperforming Loans in the Bank Production Technology

Table 3.1 Trends in the Configuration of Japanese Banking Industry
Table 3.2 Descriptive Statistics for 101 Banks over 5 Years
Table 3.3 Estimates of $c03-math-0078$
Table 3.4 Price Estimates and Inefficiency
Table 3.5 Average Optimal Quantities Allowing Time Substitution (std. dev.) (trillions of yen)

Chapter 4: A Practical Guide to Regime Switching in Financial Economics

Table 4.1 Conditional and Unconditional Asset Returns Descriptive Statistics
Table 4.2 Conditional and Unconditional Asset Returns Descriptive Statistics
Table 4.3 Asset Returns Descriptive Statistics
Table 4.4 Asset Returns Descriptive Statistics (State 1)
Table 4.5 Asset Returns Descriptive Statistics (State 2)

Chapter 5: Output Analysis and Stress Testing for Risk Constrained Portfolios

Table 5.1 Descriptive Statistics and the Additional Scenario of Returns of 8 European Stock Indexes and of the Risk-Free Asset

Chapter 6: Risk Measures and Management in the Energy Sector

Table 6.1 Alternative Values of “Estimated Price over Price in Year 0” Ratio (Coal Price in Year 0: 115 €/t (12.3 €/MWh)
Table 6.2 Alternative Values of “Estimated Price over Price in Year 0” Ratio (Gas Price in Year 0: 0.3 €/Nm³ (31.3 €/MWh)
Table 6.3 Alternative Values of “Estimated Price over Price in Year 0” Ratio (Nuclear Fuel Price in Year 0: 2100 €/kg (2.21 €/MWh)
Table 6.4 Hydro Basin Data: Capacity, Initial and Minimum Final Storage Volumes
Table 6.5 Hydro Arc Data: Energy Coefficient and Capacity
Table 6.6 Certainty Equivalent with Two Sources of Stochasticity
Table 6.7 Power Plants Owned by the Power Producer in Year 0
Table 6.8 Technology Mix Obtained with CVaR and Different Risk Aversion Parameters—Budget: 3.84 G€
Table 6.9 Technology Mix Obtained with CVaR and Different Risk Aversion Parameters—Budget: 14.5 G€

Chapter 7: Portfolio Optimization: Theory and Practice

Table 7.1 Optimal Portfolio Weights for Alternative Utility Functions and $c07-math-0105$
Table 7.2 Average Ratio of CEL for Errors in Means, Variances, and Covariances
Table 7.3 Russell Business Engineering Models
Table 7.4 Dimensions of a Typical Implemented Problem
Table 7.5 Expected Allocations for Initialization Period: INI (100 million yen: percentages by account)
Table 7.6 Expected Allocations in the End Effects Period
Table 7.7 Asset Structure of European Pension Funds
Table 7.8 Means, Standard Deviations, and Correlations Assumptions Based on 1970–2000 Data
Table 7.9 Statistical Properties of Asset Returns
Table 7.10 Optimal Initial Asset Weights at Stage 1 (in %)
Table 7.11 Expected Terminal Wealth, Expected Reserves, and Probabilities of Shortfalls with a Target Wealth, W_t = 206.1
Table 7.12 Final Wealth Statistics by Kelly Fraction: Ziemba-Hausch (1986) Model Kelly Fractions
Table 7.13 Comparison of Ordinary and Symmetric Downside Sharpe Yearly Performance Measures, Monthly Data, and Arithmetic Means
Table 7.14 Medallion Fund Net Returns, %, January 1993 to April 2005

Chapter 8: Portfolio Optimization and Transaction Costs

Table 8.1 State of the Art on Portfolio Optimization with Transaction Costs
Table 8.2 Rates of Return (in %) for Three Assets under Three Scenarios
Table 8.3 Transaction Costs Applied by Banks Operating in Italy
Table 8.4 Computational Results: Pure Proportional Cost Structure with c_j = 0.25 percent for all j ∈ N
Table 8.5 Computational Results: Pure Fixed Cost Structure with f_j = 10 euros for all j ∈ N
Table 8.6 Computational Results: Proportional Cost with Minimum Charge Structure with Fixed Cost Equal to 10 euros for a Minimum Charge up to 4,000 euros and 0.25% Proportional Cost for Larger Values of Capital

Chapter 9: Statistical Properties and Tests of Efficient Frontier Portfolios

Table 9.1 Descriptive Statistics
Table 9.2 Covariance/Correlation Matrix
Table 9.3 Univariate Confidence Limits for Tangency Portfolio Weights
Table 9.4 Likelihood Ratio and Z-tests for Univariate Tangency Portfolio Weights
Table 9.5 Robustness of 99% Confidence Intervals for the Univariate Tangency Portfolio Weights
Table 9.6 Simultaneous 95 and 99% Confidence Intervals for Tangency Portfolio Weights
Table 9.7 Likelihood Ratio and Chi-Squared Tests for a Multivariate Tangency Portfolio
Table 9.8 Likelihood Ratio Tests for Minimum Variance Portfolios
Table 9.9 Properties of Estimate Risk Appetite Corresponding to a Target Expected Return
Table 9.10 Effect of Variability in Risk Appetite on Portfolio Weights

Chapter 10: Stress Testing for Portfolio Credit Risk: Supervisory Expectations and Practices

Table 10.1 Bond Return Index Return—Summary Statistics and Correlations (January 2, 1997, to December 19, 2011)
Table 10.2 Moody's Credit Ratings Migration Matrix
Table 10.3 Default Rate Data—Summary Statistics
Table 10.4 Risk Factor Data—Summary Statistics
Table 10.5 Stress Testing Example Regression Results
Table 10.6 Stress Test Results of Alternative Scenarios

Chapter 11: A Critique of Credit Risk Models with Evidence from Mid-Cap Firms

Table 11.1 Mapping Ratings
Table 11.2 Results from Ratings-Based Models
Table 11.3 Results from Accounting-Based Models
Table 11.4 Results from Structural Credit Models
Table 11.5 Summary of the Strengths and Weaknesses of Each Model

Chapter 12: Predicting Credit Ratings Using a Robust Multicriteria Approach

Table 12.1 Sample Composition (Number of Observations) by Year, Country, and Business Sector
Table 12.2 Percentage of Sample Observations in Each Risk Category
Table 12.3 Averages of Independent Variables by Rating Group
Table 12.4 Trade-offs (in %) of the Attributes
Table 12.5 Classification Accuracies (in %) for the Holdout Samples

Chapter 13: Parameter Analysis of the VPIN (Volume-Synchronized Probability of Informed Trading) Metric

Table 13.1 The 10 Parameter Combinations That Produced the Smallest Average False Positive Rate α
Table 13.2 Non-VNS Optimal Parameter Sets
Table 13.3 VNS Optimal Parameter Sets
Table 13.4 Optimization Results with Different Starting Points
Table 13.5 VNS Optimization Results with Different Starting Points
Table 13.6 Parameter Bounds Using Five Sampled Points, with τ as the Controllable Input
Table 13.7 Joint Sobol Sensitivity Indices
Table 13.8 Parameter Bounds with π as Controllable Input (5 Sampled Points)
Table 13.9 Sobol Sensitivity Indices
Table 13.10 Parameter Bounds with π as Controllable Input (7 Sampled Points)
Table 13.11 Sobol Sensitivity Indices
Table 13.12 Parameter Bounds with π as Controllable Input (7 Sampled Points)
Table 13.13 Sobol Sensitivity Indices

Chapter 14: Covariance Specification Tests for Multivariate GARCH Models1

Table 14.1 Specification Tests for Standardized Residuals
Table 14.2 Covariance Specification Tests

Chapter 15: Accounting Information in the Prediction of Securities Class Actions

Table 15.1 Description and Number of Filings by Sector
Table 15.2 Summary of Training and Validation Samples
Table 15.3 Descriptive Statistics for the Two Groups and Nonparametric Test
Table 15.4 Pearson Correlation among the Variables
Table 15.5 Final Set of Input Variables
Table 15.6 Coefficients of LA
Table 15.7 Coefficients of DA and SVMs
Table 15.8 Weights of Variables in the UTADIS and MHDIS
Table 15.9 Summary of results
Table 15.10 Accounting Information

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Library of Congress Cataloging-in-Publication Data

Zopounidis, Constantin.

Quantitative financial risk management: theory and practice / Constantin Zopounidis, Emilios Galariotis.

pages cm. – (The Frank J. Fabozzi series)

Includes index.

ISBN 978-1-118-73818-4 (hardback)

1. Financial risk management. I. Galariotis, Emilios. II. Title.

HD61.Z67 2015

332–dc23

Preface

The book Quantitative Financial Risk Management: Theory and Practice provides an invaluable forum for creative and scholarly work on financial risk management, risk models, portfolio management, credit risk modeling, portfolio management, and financial markets throughout the world.

Quantitative financial risk management consists of economics, accounting, statistics, econometrics, mathematics, stochastic processes, and computer science and technology. The tools of financial management are more frequently being applied to manage, monitor, and measure risk, especially in the context of globalization, market volatility, and economic crisis.

The main objectives of this book are to advance knowledge related to risk management and portfolio optimization, as well as to generate theoretical knowledge with the aim of promoting research within various sectors wherein financial markets operate. Chapters will relate to one of these areas, will have a theoretical and/or empirical problem orientation, and will demonstrate innovation in theoretical and empirical analyses, methodologies, and applications.

We would like to thank the assistant editors Georgios Manthoulis and Stavroula Sarri for their invaluable help. We extend appreciation to the authors and referees of these chapters, and to the editors at John Wiley & Sons, Inc., for their assistance in producing this book.

The editors,
Constantin Zopounidis
Emilios Galariotis

About the Editors

Constantin Zopounidis is professor of Financial Engineering and Operations Research at Technical University of Crete in Greece, distinguished research professor at Audencia Nantes, School of Management (EQUIS, AMBA, AACSB) in France, senior academician of the Royal Academy of Doctors and the Royal Academy of Economics and Financial Sciences of Spain, and elected president of the Financial Engineering and Banking Society (FEBS).

His research interests include financial engineering, financial risk management, and multiple-criteria decision making. He has edited and authored more than 70 books in international publishers and more than 450 research papers in scientific journals, edited volumes, conference proceedings, and encyclopedias in the areas of finance, accounting, operations research, and management science. Prof. Zopounidis is editor-in-chief and member of the editorial board of several international journals. In recognition of his scientific work, he has received several awards from international research societies.

Emilios Galariotis is professor of Finance at Audencia Nantes School of Management (AMBA, EQUIS, AACSB) in France. He is the founder and director of the Centre for Financial and Risk Management (CFRM) and head of research in the area of Finance, Risk, and Accounting Performance at Audencia.

His academic career started at Durham University and head of research in the area of Finance, Risk, and Accounting Performance as well as co-chair of the department of Accounting and Finance at Audencia. UK. There, beyond his academic role. His academic career started at Durham University, UK (Top 100 in the world, 3rd oldest in England), he was also director of Specialized Finance Masters Programs. His research interests include behavioral finance and market efficiency, contrarian and momentum investment strategies, and liquidity.

His work has been published in quality refereed journals, such as (to mention only the most recent) the European Journal of Operational Research, the Journal of Banking and Finance, as well as the Wiley Encyclopedia of Management. Professor Galariotis is associate editor and member of the editorial board of several international journals, and member of the board of directors of the Financial Engineering and Banking Society and distinguished researcher at various research centers.

Section OneSupervisory Risk Management

Section One
Supervisory Risk Management