Bayesian Methods in Finance / Edition 1

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Recent years have seen an impressive growth in the variety andcomplexity of quantitative models and modeling techniques used infinance, particularly in portfolio and risk management. Whilecriticisms of the excessive reliance on quantitative modelsresurface with each turmoil in the financial markets, the focusshould be on employing techniques such that the likelihood ofextreme events as well as the uncertainty of the decision-makingenvironment are properly accounted for. Bayesian methods, coupledwith heavy-tailed distributional assumptions, provide onetheoretically sound avenue to achieve this goal.

Together with the ability to incorporate inform-ation fromdifferent sources and tackle complex estimation problems, dealingwith estimation uncertainty has been a driving factor behind theincreased popularity of Bayesian methods among academics andpractitioners alike.

The aim of Bayesian Methods in Finance is to provide an overviewof the theory of Bayesian methods and explain their real-worldapplications to financial modeling. While the principles andconcepts explained in the book can be used in financial modelingand decision making in general, the authors focus on portfoliomanagement and market risk management, since these are the areas infinance where Bayesian methods have had the greatest penetration todate.

Bayesian Methods in Finance offers both students of finance andpractitioners an invaluable resource in the form of a previouslyunavailable, highly accessible, unified look at the use of theBayesian methodology—as well as numerical computationalmethods—in financial models and asset management.

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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-Professorat the University of Karlsruhe in the School of Economics andBusiness Engineering; Professor Emeritus at the University ofCalifornia, Santa Barbara; and Chief-Scientist of FinAnalyticaInc.

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

Biliana S. Bagasheva, PhD, has research interests in the areasof risk management, portfolio construction, Bayesian methods, andfinancial econometrics. Currently, she is a consultant inLondon.

Frank J. Fabozzi, PhD, CFA, is Professor in the Practice ofFinance and Becton Fellow at Yale University's School of Managementand 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, PredicativeInference.

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 SelectionFramework.

Chapter 9. Market Efficiency and return Predictability.

Chapter 10. Volatility Models.

Chapter 11. Bayesian Estimation of ARCH-Type VolatilityModels.

Chapter 12. Bayesian Estimation of Stochastic VolatilityModels.

Chapter 13. Advanced Techniques for Bayesian PortfolioSelection.

Chapter 14. Multifactor Equity Risk Models.



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