Bayesian Methods in Finance

Beschreibung

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.
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.

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Svetlozar T. Rachev

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