Bayesian Methods in Finance provides a detailed overview of the theory of Bayesian methods and explains their real-world applications to financial modeling. While the principles and concepts explained throughout the book can be used in financial modeling and decision making in general, the authors focus on portfolio management and market risk managementsince these are the areas in finance where Bayesian methods have had the greatest penetration to date.

Svetlozar T. Rachev, PhD, Doctor of Science, is Chair-Professor at the University of Karlsruhe in the School of Economics and Business Engineering; Professor Emeritus at the University of California, Santa Barbara; and Chief-Scientist of FinAnalytica Inc.

John S. J. Hsu, PhD, is Professor of Statistics and Applied Probability at the University of California, Santa Barbara.

Biliana S. Bagasheva, PhD, has research interests in the areas of risk management, portfolio construction, Bayesian methods, and financial econometrics. Currently, she is a consultant in London.

Frank J. Fabozzi, PhD, CFA, is Professor in the Practice of Finance and Becton Fellow at Yale University's School of Management and the Editor of the Journal of Portfolio Management.

Preface xv

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

References 298

Index 311