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 current machine learning-based methods, the framework presented here allows you to measure risk in a fully-Bayesian setting without losing the structure afforded by parametric risk and asset-pricing models.

- Recognize the assumptions embodied in classical statistics
- Quantify model risk along multiple dimensions without backtesting
- Model time series without assuming stationarity
- Estimate state-space time series models online with simulation methods
- Uncover uncertainty in workhorse risk and asset-pricing models
- Embed Bayesian thinking about risk within a complex organization

Ignoring uncertainty in risk modeling creates an illusion of mastery and fosters erroneous decision-making. Firms who ignore the many dimensions of model risk measure too little risk, and end up taking on too much. *Bayesian Risk Management* provides a roadmap to better risk management through more circumspect measurement, with comprehensive treatment of model uncertainty.

- Title Page
- Copyright
- Preface
- Acknowledgments
- Part One: Capturing Uncertainty in Statistical Models
- Part Two: Sequential Learning with Adaptive Statistical Models
- Part Three: Sequential Models of Financial Risk
- Part Four: Bayesian Risk Management
- References
- Index
- End User License Agreement