پارسی   English   العربیه

Bayesian Methods in Finance


An accessible overview of the theory and practice of Bayesian Methods in Finance

This first-of-its-kind book explains and illustrates the fundamentals of the Bayesian methodology and their applications to finance in clear and accessible terms.

Bayesian Methods in Finance provides a unified examination of the use of Bayesian theory and practice in portfolio and risk management—explaining the concepts and techniques that can be applied to real-world financial problems.

This book is a guide to using Bayesian methods and, notably, the Markov Chain Monte Carlo toolbox to: incorporate prior views of an analyst or a fund manager into the asset allocation process; estimate and predict volatility; improve risk forecasts; and combine the conclusions of different models. Each application presentation begins with the basics, works through the traditional "frequentist" perspective, and then follows with the Bayesian treatment.

This invaluable resource presents a theoretically sound framework for combining various sources of information and a robust estimation setting that explicitly incorporates estimation risk, and brings within reach the flexibility to handle complex and realistic models.

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.


About the Authors.

Chapter 1. Introduction.

Chapter 2. The Bayesian Paradigm.

Chapter 3. Prior and Posterior Information, Predicative Inference.

Chapter 4. Bayesian Linear Regression Model.

Chapter 5. Bayesian Numerical Computation.

Chapter 6. Bayesian Framework for Portfolio Allocation.

Chapter 7. Prior Beliefs and Asset Pricing Models.

Chapter 8. The Black-Litterman Portfolio Selection Framework.

Chapter 9. Market Efficiency and return Predictability.

Chapter 10. Volatility Models.

Chapter 11. Bayesian Estimation of ARCH-Type Volatility Models.

Chapter 12. Bayesian Estimation of Stochastic Volatility Models.

Chapter 13. Advanced Techniques for Bayesian Portfolio Selection.

Chapter 14. Multifactor Equity Risk Models.