Includes bibliographical references and index.

Print version record and CIP data provided by publisher.

Models for discontinuous markets -- Capturing uncertainty in statistical models -- Prior knowledge, parameter uncertainty, and estimation -- Model uncertainty -- Sequential learning with adaptive statistical models -- Introduction to sequential modeling -- Bayesian inference in state-space time series models -- Sequential Monte Carlo inference -- Sequential models of financial risk -- Volatility modeling -- Asset-pricing models and hedging -- Bayesian risk management -- From risk measurement to risk management.

A risk measurement and management framework that takes model risk seriously Most financial risk models assume the future will look like the past, but effective risk management depends on identifying fundamental changes in the marketplace as they occur. Bayesian Risk Management details a more flexible approach to risk management, and provides tools to measure financial risk in a dynamic market environment. This book opens discussion about uncertainty in model parameters, model specifications, and model-driven forecasts in a way that standard statistical risk measurement does not. And unlike cu.

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