Bayesian Methods in Finance / Edition 1by Svetlozar T. Rachev, John S. J. Hsu, Biliana S. Bagasheva, Frank J. Fabozzi
Pub. Date: 02/08/2008
Recent years have seen an impressive growth in the variety and complexity of quantitative models and modeling techniques used in finance, particularly in portfolio and risk management. While criticisms of the excessive reliance on quantitative models resurface with each turmoil in the financial markets, the focus should be on employing techniques such that the… See more details below
Recent years have seen an impressive growth in the variety and complexity of quantitative models and modeling techniques used in finance, particularly in portfolio and risk management. While criticisms of the excessive reliance on quantitative models resurface with each turmoil in the financial markets, the focus should be on employing techniques such that the likelihood of extreme events as well as the uncertainty of the decision-making environment are properly accounted for. Bayesian methods, coupled with heavy-tailed distributional assumptions, provide one theoretically sound avenue to achieve this goal.
Together with the ability to incorporate inform-ation from different sources and tackle complex estimation problems, dealing with estimation uncertainty has been a driving factor behind the increased popularity of Bayesian methods among academics and practitioners alike.
The aim of Bayesian Methods in Finance is to provide an overview of the theory of Bayesian methods and explain their real-world applications to financial modeling. While the principles and concepts explained in the book can be used in financial modeling and decision making in general, the authors focus on portfolio management and market risk management, since these are the areas in finance where Bayesian methods have had the greatest penetration to date.
Bayesian Methods in Finance offers both students of finance and practitioners an invaluable resource in the form of a previously unavailable, highly accessible, unified look at the use of the Bayesian methodology—as well as numerical computational methods—in financial models and asset management.
Table of Contents
About the Authors xvii
CHAPTER 1 Introduction 1
CHAPTER 2 The Bayesian Paradigm 6
CHAPTER 3 Prior and Posterior Information, Predictive Inference 22
CHAPTER 4 Bayesian Linear Regression Model 43
CHAPTER 5 Bayesian Numerical Computation 61
CHAPTER 6 Bayesian Framework For Portfolio Allocation 92
CHAPTER 7 Prior Beliefs and Asset Pricing Models 118
CHAPTER 8 The Black-Litterman Portfolio Selection Framework 141
CHAPTER 9 Market Efficiency and Return Predictability 162
CHAPTER 10 Volatility Models 185
CHAPTER 11 Bayesian Estimation of ARCH-Type Volatility Models 202
CHAPTER 12 Bayesian Estimation of Stochastic Volatility Models 229
CHAPTER 13 Advanced Techniques for Bayesian Portfolio Selection 247
CHAPTER 14 Multifactor Equity Risk Models 280
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