Financial Risk Management and Modeling (Risk, Systems and Decisions)

Risk Management and Modeling
	Book Synopsis
Contents
Corporate Risk Management and Hedge Accounting Under the Scope of IFRS 9
	1 Introduction
	2 Hedge Accounting with IFRS 9 and Corporate Risk Management: Toward a Greater Alignment
		2.1 Accounting for Financial Instruments with IFRS 9: Background Information
		2.2 Accounting for Financial Instruments with IFRS 9: The Case of Carbon Derivatives
	3 Methodology
		3.1 Assessment of Hedging Needs of EU ETS Power Firms
		3.2 Hedge Ratio Estimation
			3.2.1 Static Hedging and Estimation of Time Invariant Hedge Ratios
			3.2.2 Dynamic Hedging and Estimation of Time Varying Hedge Ratios
		3.3 Assessment of Hedging Effectiveness
	4 Empirical Results and Impact Assessment
		4.1 Values of Hedging Ratios
		4.2 Results of Hedging Effectiveness Assessment
		4.3 Effects of IFRS 9 Hedge Accounting on Financial Statements According to IFRS 7
	5 Conclusion
	References
Corporate Fraud Risk Management
	1 The Meaning and Nature of Corporate Fraud
	2 The Types of Corporate Fraud
		2.1 Assets Misappropriation
		2.2 Financial Statements Fraud
		2.3 Corruption
	3 The Impact of Corporate Fraud
	4 The Meaning of Risk
	5 Risk Assessment Versus Risk Management
	6 Designing Effective Fraud Prevention Strategies
		6.1 Understanding the Psychology of Fraud Criminals
		6.2 Designing Effective Anti-fraud Controls
		6.3 Maintaining a Sound Corporate Governance System
			6.3.1 Board of Directors and Corporate Fraud Risk Management
			6.3.2 External Auditors and Corporate Fraud Risk Management
			6.3.3 The Role of Internal Auditors and the Audit Committee in Fraud Risk Management
	7 Chapter Summary
	References
Leverage Financing and the Risk-Taking Behavior of Small Business Managers: What Happened After the Crisis?
	1 Introduction
	2 Literature Review
		2.1 SMEs Bank Financing
		2.2 Related Theories and Hypothesis Development
	3 Sample and Methodology
		3.1 Sample
		3.2 Methodology
			3.2.1 Model
			3.2.2 Measuring Risk-Taking Behavior
			3.2.3 Leverage
			3.2.4 Control Variables
			3.2.5 Growth Opportunities
	4 Summary Statistics and Results
		4.1 Summary Statistics
		4.2 Empirical Results
		4.3 Sensitivity Analysis
	5 Conclusion
	References
Credit Contagion Between Greece and Systemically Important Banks: Lessons for the Euro Area
	1 Introduction
	2 Sample Data Characteristics and Credit Risk Regimes
		2.1 CDS Data and Sub-Sampled Credit Regimes
		2.2 Stationarity Tests
		2.3 Cointegration Tests
	3 Modelling Contagion
	4 Discussion of Results
	5 Generalized Impulse Responses
	6 Summary and Concluding Remarks
	References
Cluster Analysis for Investment Funds Portfolio Optimisation: A Symbolic Data Approach
	1 Introduction
	2 Methodology
		2.1 The Symbolic Approach Based on Histogram-Valued Data and Clusters
		2.2 The Mean-CVaR Optimal Portfolio
	3 Application in Luxembourg Funds
		3.1 Data Collection and Risk Measures
		3.2 Constructing the Portfolio and Calculating the Efficient Portfolio
			3.2.1 Portfolio Selection from the Correlation Matrix
			3.2.2 Portfolio Selection Based on the Symbolic Data Approach
			3.2.3 Mean-CVaR Equal Weights Portfolios
			3.2.4 Optimal Weights Portfolios
			3.2.5 The Global Minimum CVaR Portfolio
			3.2.6 Back-Testing Procedure
	4 Conclusion
	Appendix
	References
Grey Incidence Analysis as a Tool in Portfolio Selection
	1 Introduction
	2 Literature Overview
	3 Methodology
	4 Empirical Analysis
		4.1 Data Description and Preparation
		4.2 Grey Incidence Analysis Initial Results
		4.3 Benchmark Strategies
			4.3.1 Equal Weights Strategy
			4.3.2 Random Weights Strategy 1
			4.3.3 Random Weights Strategy 2
			4.3.4 Moving Average Strategy
			4.3.5 Minimum Variance Strategy
			4.3.6 Data Envelopment Analysis Strategy
			4.3.7 Multiple Criteria Decision Making Strategy 1
			4.3.8 Multiple Criteria Decision Making Strategy 2
		4.4 Grey Results Based (GRD) Strategies
			4.4.1 Best GRD Strategy
			4.4.2 GRD Based Weights Strategy
			4.4.3 3 Best GRDs Strategy
			4.4.4 Return-GRD Strategy
			4.4.5 Risk-GRD Strategy
			4.4.6 Skewness-GRD Strategy
			4.4.7 Kurtosis-GRD Strategy
			4.4.8 Fuzzy-GRD Strategy
			4.4.9 DEA-GRD Strategy
			4.4.10 MCDM-GRD Strategy 1
			4.4.11 MCDM-GRD Strategy 2
		4.5 Comparisons of Results
	5 Discussion
	6 Conclusion
	A.1 Appendix
	References
Investors' Heterogeneity and Interactions: Toward New ModelingTools
	1 Introduction
	2 The Paradox of Efficiency
		2.1 Questioning Rationality
			2.1.1 Heuristics
			2.1.2 Neuroeconomics
		2.2 Price Predictability Relating Anomalies
	3 Behavioral Heterogeneity in Financial Markets
		3.1 Emotions, Moods, and Decisions
		3.2 The Role of Noise Traders
		3.3 Investor Sentiment and Financial Returns Dynamics
		3.4 Mimicking Behavior
	4 New Modeling Tools
		4.1 Switching Transition Regression Models
		4.2 Agent-Based Modeling
		4.3 Networks
		4.4 Combining Networks and Agent-Based Models in Financial Markets
	5 Conclusion
	References
On the Underestimation of Risk in Hedge Fund Performance Persistence: Geolocation and Investment Strategy Effects
	1 Introduction
	2 Performance Persistence
		2.1 Undefined Domiciles
			2.1.1 Short-Term Persistence
			2.1.2 Long-Term Persistence
		2.2 Defined Domiciles
	3 Data
		3.1 Database
		3.2 Descriptive Statistics
	4 Methods
	5 Empirical Results
		5.1 Non-Parametric Methods
			5.1.1 Domiciles and Investment Strategies
			5.1.2 Domiciles Combined with Investment Strategy
		5.2 Parametric Methods
			5.2.1 Non-Risk Adjusted
			5.2.2 Risk-Adjusted
	6 Conclusion
	References
Equal or Value Weighting? Implications for Asset-Pricing Tests
	1 Introduction
	2 Data Description and Methodology
	3 Identifying Differences in Performance of the Portfolios
		3.1 Performance Metrics
		3.2 Evaluating Portfolio Performance
			3.2.1 Comparing the Returns of the Portfolios
			3.2.2 Comparing the Risks of the Portfolios
			3.2.3 Comparing the Risk-Return Tradeoffs of the Portfolios
	4 Explaining Differences in Performance of the Portfolios
		4.1 Explaining Differences in Systematic Returns of Portfolios
		4.2 Explaining Differences in Alphas of Portfolios
	5 Implications of Weighting Method for Asset-Pricing Tests
		5.1 Testing the Unconditional Capital Asset Pricing Model
		5.2 Testing the Spanning Properties of the Stochastic Discount Factor
		5.3 Testing Relations Between Asset Returns and Asset Characteristics
		5.4 Testing Whether Idiosyncratic Risk Is Priced
	6 Conclusion
	Appendix 1: Stock Characteristics
		Size, Book, Book-to-Market
		Momentum and Reversal
		Liquidity
		Idiosyncratic Volatility
	Appendix 2: Resampling Procedures and Monotonicity Relation Tests
	Appendix 3: Robustness Tests
		Different Number of Stocks
		Different Stock Indexes
		Different Economic Conditions
		Bias in Computed Returns
	References
Bank Failure Prediction: A Comparison of Machine Learning Approaches
	1 Introduction
	2 Literature Review
	3 Methodologies
		3.1 Logistic Regression
		3.2 Support Vector Machines
		3.3 Naive Bayes
		3.4 Gradient Boosting
		3.5 Random Forest
		3.6 Artificial Neural Networks
	4 Data and Sample Construction
		4.1 Data and Variables
		4.2 Model Validation Procedure and Performance Measures
	5 Results
		5.1 Results by Variables' Settings
		5.2 Results by Classification Method
	6 Conclusions
	References
From Calendar to Economic Time. Deciphering the Arrival of Events in Irregularly Spaced Time
	1 Introduction
	2 Market Efficiency and High Frequency Trading
		2.1 Intraday Market Frictions I: Liquidity and the Cost of Immediacy
		2.2 Intraday Market Frictions II: Information and Adverse Selection
		2.3 A more Detailed View: The Role of Time
	3 Economic Time Modelling: Deciphering the Arrival of Events
		3.1 Arrival of Events: Asymmetry and Non-Linearity
		3.2 Arrival Time and Agent Types
		3.3 Multiple Marks and Multivariate
		3.4 Arrival Time of Marks: Intraday Price Discovery and Beyond
			3.4.1 Initial Approaches
			3.4.2 Marks and Pricing
			3.4.3 Marks, Pricing and Beyond
	4 Conclusions
	References
Climate Change and Financial Risk
	1 Introduction: The Short History of Climate Change Related Risks for the Financial Sector
		1.1 The Early Days of Climate Change in Finance
		1.2 An Important Issue for `Responsible Investors'
		1.3 The Acceleration
		1.4 Climate Policy Meets Finance
	2 Climate Change as a `New' Source of Financial Risk
		2.1 New Types of Financial Risks
		2.2 Transition Risks
		2.3 Physical Risks
		2.4 Are Those New Types of Risk Priced by Markets?
			2.4.1 Unprecedented Phenomena
			2.4.2 Radical Uncertainty
			2.4.3 Non-normal Probability Distributions
			2.4.4 Bounded Rationality
			2.4.5 Discrepancy in Time Horizons
			2.4.6 Climate Change Inefficiently Priced by Markets
	3 The Approaches to Manage Climate-Related Financial Risks
		3.1 Materialisation Channels of Climate-Related Financial Risks
		3.2 Climate Scenario Analysis
	4 Climate Change Risks and Financial Regulation
		4.1 Reporting and Disclosure of Climate-Related Risks
			4.1.1 The Article 173 of the French Energy Transition Act
			4.1.2 Mark Carney's Speech on the `Tragedy of the horizon'
		4.2 Beyond Reporting, Towards an Enhanced Prudential Framework
			4.2.1 The European Commission Sustainable Finance Action Plan
	5 Conclusion
	References
The Curious Case of Herding: Theories and Risk
	1 Introduction
	2 Herding: Mimetic Behaviour in Sociology Economics and Finance
		2.1 From Sociology to Financial Markets: In a Nutshell
		2.2 Herding: Definitions and Theories
			2.2.1 Definitions of Herding
			2.2.2 Theories of Herding
	3 Measures of Herding and Empirical Evidence
		3.1 General Evidence and the Experimental Approach
		3.2 Institutional Herding: Measures and Evidence
			3.2.1 Institutional Herding: Methodological Approaches
			3.2.2 Institutional Herding: Empirical Evidence
		3.3 Aggregate Herding: Measures and Evidence
			3.3.1 Aggregate Herding: Main methodological approaches.
			3.3.2 Aggregate Herding: Empirical Evidence
	4 Herding: The Measurement Problem and Why It Is So Important
		4.1 Herding and Risk: Why Does It Matter?
		4.2 Herding: The Measurement Problem
	5 Conclusion: The Road So Far and Limitations
	References
Index