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Much research into financial contagion and systematic risks has been motivated by the finding that cross-market correlations (resp. coexceedances) between asset returns increase significantly during crisis periods. Is this increase due to an exogenous shock common to all markets (interdependence) or due to certain types of transmission of shocks between markets (contagion)?
Darolles and Gourieroux explain that an attempt to convey contagion and causality in a static framework can be flawed due to identification problems; they provide a more precise definition of the notion of shock to strengthen the solution within a dynamic framework.
This book covers the standard practice for defining shocks in SVAR models, impulse response functions, identitification issues, static and dynamic models, leading to the challenges of measurement of systematic risk and contagion, with interpretations of hedge fund survival and market liquidity risks
- Features the standard practice of defining shocks to models to help you to define impulse response and dynamic consequences
- Shows that identification of shocks can be solved in a dynamic framework, even within a linear perspective
- Helps you to apply the models to portfolio management, risk monitoring, and the analysis of financial stability
Upper-division undergraduates, graduate students, and researchers working on market linkages, pricing and risk management in financial markets and industries.
<li>1. Contagion and Causality in Static Models<ul><li>Abstract</li><li>1.1 Linear dependence in a static model</li><li>1.2 Nonlinear dependence in a static model</li><li>1.3 Model with exogenous switching regimes</li><li>1.4 Chapter 1 highlights</li><li>1.5 Appendices</li></ul></li>
<li>2. Contagion in Structural VARMA Models<ul><li>Abstract</li><li>2.1 Shocks in a dynamic model</li><li>2.2 A vector autoregressive moving average (VARMA) model with independent errors</li><li>2.3 Non-fundamentalness</li><li>2.4 Chapter 2 highlights</li><li>2.5 Appendices</li></ul></li>
<li>3. Common Frailty versus Contagion in Linear Dynamic Models<ul><li>Abstract</li><li>3.1 Linear dynamic model with common factor and contagion</li><li>3.2 Observable versus latent factors</li><li>3.3 Shocks, impulse response functions and stress</li><li>3.4 Constrained models and misspecification</li><li>3.5 The literature</li><li>3.6 Chapter 3 highlights</li><li>3.7 Appendices</li></ul></li>
<li>4. Applications of Linear Dynamic Models<ul><li>Abstract</li><li>4.1 Portfolio management</li><li>4.2 Contagion among banks</li><li>4.3 Chapter 4 highlights</li><li>4.4 Appendices</li></ul></li>
<li>5. Common Frailty and Contagion in Nonlinear Dynamic Models<ul><li>Abstract</li><li>5.1 Specifications</li><li>5.2 Stochastic volatility model</li><li>5.3 Application to portfolio management</li><li>5.4 Chapter 5 highlights</li><li>5.5 Appendices</li></ul></li>
<li>6. An Application of Nonlinear Dynamic Models: The Hedge Fund Survival<ul><li>Abstract</li><li>6.1 HF liquidation data</li><li>6.2 Dynamic Poisson model</li><li>6.3 Results</li><li>6.4 Stress-tests</li><li>6.5 Chapter 6 highlights</li><li>6.6 Appendices</li></ul></li>
- No. of pages:
- © ISTE Press - Elsevier 2015
- 19th August 2015
- ISTE Press - Elsevier
- Hardcover ISBN:
- eBook ISBN:
Serge Darolles is Professor of Finance at Paris–Dauphine University in France, and member of the Quantitative Management Initiative (QMI) scientific committee. His research interests include financial econometrics, liquidity and hedge fund analysis. He has written numerous articles, which have been published in academic journals.
Professor of Finance, Paris–Dauphine University, France
Christian Gourieroux is Professor at the University of Toronto in Canada, and Chair of the Finance Laboratory at the Center for Research in Economics and Statistics (CREST) in Paris.
Professor, Dept of Economics, University of Toronto, Canada
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