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Bayesian Methods in Finance By Svetlozar T. Rachev, John S. J. Hsu, Biliana S. Bagasheva, Frank J. Fabozzi
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- Sales Rank: #1496527 in Books
- Published on: 2008-02-08
- Original language: English
- Number of items: 1
- Dimensions: 9.30" h x 1.20" w x 6.40" l, 1.21 pounds
- Binding: Hardcover
- 329 pages
From the Inside Flap
Recent years have seen an impressive growth in the variety and complexity of quantitative models and modeling techniques used in finance, particularly in portfolio and risk management. While criticisms of the excessive reliance on quantitative models resurface with each turmoil in the financial markets, the focus should be on employing techniques such that the likelihood of extreme events as well as the uncertainty of the decision-making environment are properly accounted for. Bayesian methods, coupled with heavy-tailed distributional assumptions, provide one theoretically sound avenue to achieve this goal.
Together with the ability to incorporate inform-ation from different sources and tackle complex estimation problems, dealing with estimation uncertainty has been a driving factor behind the increased popularity of Bayesian methods among academics and practitioners alike.
The aim of Bayesian Methods in Finance is to provide an overview of the theory of Bayesian methods and explain their real-world applications to financial modeling. While the principles and concepts explained in 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.
Bayesian Methods in Finance offers both students of finance and practitioners an invaluable resource in the form of a previously unavailable, highly accessible, unified look at the use of the Bayesian methodology—as well as numerical computational methods—in financial models and asset management.
From the Back Cover
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.
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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