Bayesian Risk Management

<b>A risk measurement and management framework that takes model risk seriously</b> <p>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. <i>Bayesian Risk Management</i> 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. <ul> <li>Recognize the assumptions embodied in classical statistics</li> <li>Quantify model risk along multiple dimensions without backtesting</li> <li>Model time series without assuming stationarity</li> <li>Estimate state-space time series models online with simulation methods</li> <li>Uncover uncertainty in workhorse risk and asset-pricing models</li> <li>Embed Bayesian thinking about risk within a complex organization</li> </ul> <p>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. <i>Bayesian Risk Management</i> provides a roadmap to better risk management through more circumspect measurement, with comprehensive treatment of model uncertainty.