Bayesian Risk Management: A Guide to Model Risk and Sequential Learning in Financial Markets

A Risk Measurement and Management Framework that Takes Model Risk Seriously

Why do risk models break down? The answer may lie in the way that statistical methods are conventionally used to draw inferences about market conditions and inform risk-taking behavior. Bayesian Risk Management enables a discussion on the way standard statistical methods overlook uncertainty in model specifications, model parameters, and model-driven forecasts. In a simple and direct way, Bayesian methods are used throughout the book to:

• Recognize the assumptions embodied in classical statistics
• Quantify model risk along multiple dimensions
• Model time series without assuming continuity between past and future
• Adjust time-series estimates to maintain forecast accuracy
• Uncover uncertainty in workhorse risk and asset-pricing models
• Achieve decentralized control of risk-taking in complex organizations

For firms in financial services and other industries operating in a dynamic environment of incomplete information, Bayesian Risk Management provides a thought-provoking challenge to the prevailing wisdom about the uses and limitations of statistical risk modeling.