Book description
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.
Table of contents
- Cover image
- Title page
- Table of Contents
- Copyright page
- Introduction to the Series
- Contents of the Handbook
- Preface
-
Chapter 1: Heavy Tails in Finance for Independent or Multifractal Price Increments
- Abstract
- 1 Introduction: A path that led to model price by Brownian motion (Wiener or fractional) of a multifractal trading time
- 2 Background: the Bernoulli binomial measure and two random variants: shuffled and canonical
- 3 Definition of the two-valued canonical multifractals
- 4 The limit random variable Ω = µ ([0,1]), its distribution and the star functionalequation
- 5 The function τ(q): motivation and form of the graph
- 6 When u > 1, the moment > 1, the moment EΩq diverges if q exceeds a critical exponent qcritsatisfying τ(q) = 0; Ω follows a power-law distribution of exponent qcrit
- 7 The quantity α: the original Hölder exponent and beyond
- 8 The full function f(α) and the function ρ(α)
- 9 The fractal dimension D = τ′(1) = 2[– pu log2 u – (1 – p)v log2 v] and multifractal concentration
- 10 A noteworthy and unexpected separation of roles, between the “dimensionspectrum” and the total mass Ω; the former is ruled by the accessible α forwhich f(α) > 0, the latter, by the inaccessible ) > 0, the latter, by the inaccessible α for which f(α) < 0) < 0
- 11 A broad form of the multifractal formalism that allows α < 0 and < 0 and f(α) < 0) < 0
- Acknowledgments
- Chapter 2: Financial Risk and Heavy Tails
-
Chapter 3: Modeling Financial Data with Stable Distributions
- Abstract
- 1 Basic facts about stable distribution
- 2 Appropriateness of stable models
- 3 Computation, simulation, estimation and diagnostics
- 4 Applications to financial data
- 5 Multivariate stable distributions
- 6 Multivariate computation, simulation, estimation and diagnostics
- 7 Multivariate application
- 8 Classes of multivariate stable distributions
- 9 Operator stable distributions
- 10 Discussion
- Chapter 4: Statistical issues in modeling multivariate stable portfolios
- Chapter 5: Jump-diffusion models
- Chapter 6: Hyperbolic Processes in Finance
-
Chapter 7: Stable Modeling of Market and Credit Value at Risk
- Abstract
- 1 Introduction
- 2 “Normal” modeling of VaR
- 3 A finance-oriented description of stable distributions
- 4 VaR estimates for stable distributed financial returns
- 5 Stable modeling and risk assessment for individual credit returns
- 6 Portfolio credit risk for independent credit returns
- 7 Stable modeling of portfolio risk for symmetric dependent credit returns
- 8 Stable modeling of portfolio risk for skewed dependent credit returns
- 9 One-factor model of portfolio credit risk
- 10 Credit risk evaluation for portfolio assets
- 11 Portfolio credit risk
- 12 Conclusions
- Appendix A Stable modeling of credit returns in figures
- Appendix B Tables
- Appendix C OLS credit risk evaluation for portfolio assets in figures
- Appendix D GARCH credit risk evaluation for portfolio assets in figures
- Acknowledgments
- Chapter 8: Modelling dependence with copulas and applications to risk management
- Chapter 9: Prediction of Financial Downside-Risk with Heavy-Tailed Conditional Distributions
- Chapter 10: Stable Non-Gaussian Models for Credit Risk Management
- Chapter 11: Multifactor stochastic variance models in risk management Maximum entropy approach and Lévy processes*
- Chapter 12: Modelling the Term Structure of Monetary Rates*
- Chapter 13: Asset Liability Management: A Review and Some New Results in the Presence of Heavy Tails
-
Chapter 14: Portfolio Choice Theory With Non-Gaussian Distributed Returns
- Abstract
- 1 Introduction
- 2 Choices determined by a finite number of parameters
- 3 The asymptotic distributional classification of portfolio choices
- 4 A first comparison between the normal multivariate distributional assumption and the stable sub-Gaussian one
- 5 Conclusions
- Acknowledgment
- Appendix A: Proofs
- Appendix B: Tables
- Chapter 15: Portfolio Modeling With Heavy Tailed Random Vectors
- Chapter 16: Long Range Dependence in Heavy Tailed Stochastic Processes
- Author Index
- Subject Index
Product information
- Title: Handbook of Heavy Tailed Distributions in Finance
- Author(s):
- Release date: March 2003
- Publisher(s): North Holland
- ISBN: 9780080557731
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