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Bayesian Methods in Finance
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"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 management - since these are the areas in finance where Bayesian methods have had the greatest penetration to date.
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Table of Contents

Preface. 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. References. Index.

About the Author

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.

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