Bayesian Methods in Finance / Edition 1

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An accessible overview of the theory and practice of Bayesian methods in finance Bayesian Methods in Finance explains and illustrates the foundations of the Bayesian methodology in clear and accessible terms. It provides a unified examination of the use of the Bayesian theory and practice to analyze and evaluate asset management. With this book as their guide, readers will learn how to use Bayesian methods, and notably, the Markov Chain Monte Carlo toolbox, to incorporate the prior views of a fund manager into the asset allocation process, estimate and predict volatility, improve risk forecasts, calculate option prices, and combine the conclusions of different models. Bayesian Methods in Finance clearly shows readers how to apply this approach to the world of investment management, risk management, asset pricing, and corporate finance.

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Product Details

  • ISBN-13: 9780471920830
  • Publisher: Wiley
  • Publication date: 2/8/2008
  • Series: Frank J. Fabozzi Series , #153
  • Edition number: 1
  • Pages: 329
  • Product dimensions: 6.40 (w) x 9.17 (h) x 1.16 (d)

Meet 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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Table of Contents


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.



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