Bayesian Methods in Finance

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.