Bayesian risk management: a guide to model risk and sequential learning in financial markets Sekerke, Matt

Table of Contents:

Chapter 1. Models for Discontinuous Markets

Part 1. Capturing Uncertainty in Statistical Models
Chapter 2. Prior Knowledge, Parameter Uncertainty, and Estimation
Chapter 3. Model Uncertainty

Part 2. Sequential Learning with Adaptive Statistical Models
Chapter 4. Introduction to Sequential Modeling
Chapter 5. Bayesian Inference in State-Space Time Series Models
Chapter 6. Sequential Monte Carlo Inference

Part 3. Sequential Models of Financial Risk
Chapter 7. Volatility Modeling
Chapter 8. Asset-Pricing Models and Hedging

Part 4. Bayesian Risk Management
Chapter 9. From Risk Measurement to Risk Management

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.

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