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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.
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volatility models, predictive inference, squared return innovations, consecutive price increases, kth view, predictive covariance, diffuse improper, conditional variance parameters, predictive moments, unobserved volatility, unobserved volatilities, posterior kernel, equilibrium risk premiums, optimal portfolio weights, predictive mean, inverted gamma distribution, conditional posterior distribution, posterior inference, return predictability, cumulative excess return, posterior simulation, predictive distribution, volatility modeling, proposal density, prior covariance matrix Key Phrases - Capitalized Phrases (CAPs): (learn more)
Bayesian Numerical Computation, The Black-Litterman Portfolio Selection Framework, Multifactor Equity Risk Models, Bayesian Linear Regression Model, Markov Chain Monte Carlo, World Index, Alan Greenspan Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Surprise Me!
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