The Handbooks in Finance are intended to be a definitive source for comprehensive and accessible information in the field of finance. Each individual volume in the series should present an accurate self-contained survey of a sub-field of finance, suitable for use by finance and economics professors and lecturers, professional researchers, graduate students and as a teaching supplement. The goal is to have a broad group of outstanding volumes in various areas of finance. The Handbook of Heavy Tailed Distributions in Finance is the first handbook to be published in this series.
This volume presents current research focusing on heavy tailed distributions in finance. The contributions cover methodological issues, i.e., probabilistic, statistical and econometric modelling under non- Gaussian assumptions, as well as the applications of the stable and other non -Gaussian models in finance and risk management.
Preface (S.T. Rachev). Heavy Tails in Finance for Independent or Multifractal Price Increments (B. Mandelbrot). Financial Risk and Heavy Tails (B. Bradley, M. Taqqu). Modelling Financial Data with Stable Distribution (J.P. Nolan). Statistical Issues in Modelling Multivariate Stable Portfolios (T.J. Kozubowski et al.). Jump Diffusion Models (W. Runggaldier). Hyperbolic Processes in Finance (B.M. Bibby, M. Sorensen). Stable Modelling of Market and Credit Value at Risk (S.T. Rachev et al.). Modelling Dependence with Copulas and Applications to Risk Management (P. Embrechts et al.). Prediction of Financial Downside-Risk with Heavy Tailed Conditional Distributions (S. Mittnik, M. Paolella). Stable Non-Gaussian Models for Credit Risk Management (B. Martin et al.). Multifactor Stochastic Variance VaR Model: Application to Market Risk Management (A. Levin, A. Tchernitser). Modelling the Term Structure of Monetary Rates (L. Izzi). Asset Liability Management: A Review and Some New Results in the Presence of Heavy Tails (S.T. Rachev et al.). Portfolio Choice Theory with Non-Gaussian Distributed Returns (S. Ortobelli et al.). Portfolio Modelling with Heavy Tailed Random Vectors (M.M Meerschaert, H.-P. Scheffler). Long Range Dependence in Heavy Tailed Stochastic Processes (B. Racheva-Lotova, G. Samorodnitsky).
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- © North Holland 2003
- 5th March 2003
- North Holland
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@qu:...Sixteen papers survey theoretical and practical aspects of heavy tailed distributions in finance. @source:Journal of Economics Literature