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Bayesian Methods in Finance provides a detailed overview of the theory of Bayesian methods and explains their real-world applications to financial modeling. While the principles and concepts explained throughout 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.

A Probability Metrics Approach to Financial Risk Measures relates the field of probability metrics and risk measures to one another and applies them to finance for the first time.

• Helps to answer the question: which risk measure is best for a given problem?
• Finds new relations between existing classes of risk measures
• Describes applications in finance and extends them where possible
• Presents the theory of probability metrics in a more accessible form which would be appropriate for non-specialists in the field
• Applications include optimal...

In this book, we consider the static problem of portfolio selection in highly volatile markets. From the point of view of risk forecasting, we focus on expected tail loss (ETL) and the more general family of spectral risk measures when the underlying distribution is heavy-tailed. From an optimization perspective, we concentrate on objectives of reward-risk ratio type and how the optimization problem can be simplified depending on the properties of the risk and the reward measures. Finally, we explore the impact of a risk forecasting model, that is a combination of a distributional assumption...