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Bayesian Risk Management

Book Description

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 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.

Table of Contents

  1. Title Page
  2. Copyright
  3. Preface
  4. Acknowledgments
    1. Chapter 1: Models for Discontinuous Markets
      1. Risk Models and Model Risk
      2. Time-Invariant Models and Crisis
      3. Bayesian Probability as a Means of Handling Discontinuity
      4. Time-Invariance and Objectivity
  5. Part One: Capturing Uncertainty in Statistical Models
    1. Chapter 2: Prior Knowledge, Parameter Uncertainty, and Estimation
      1. Estimation with Prior Knowledge: The Beta-Bernoulli Model
      2. Prior Parameter Distributions as Hypotheses: The Normal Linear Regression Model
      3. Decisions after Observing the Data: The Choice of Estimators
    2. Chapter 3: Model Uncertainty
      1. Bayesian Model Comparison
      2. Models as Nuisance Parameters
      3. Uncertainty in Pricing Models
      4. A Note on Backtesting
  6. Part Two: Sequential Learning with Adaptive Statistical Models
    1. Chapter 4: Introduction to Sequential Modeling
      1. Sequential Bayesian Inference
      2. Achieving Adaptivity via Discounting
      3. Accounting for Uncertainty in Sequential Models
    2. Chapter 5: Bayesian Inference in State-Space Time Series Models
      1. State-Space Models of Time Series
      2. Dynamic Linear Models
      3. Recursive Relationships in the DLM
      4. Variance Estimation
      5. Sequential Model Comparison
    3. Chapter 6: Sequential Monte Carlo Inference
      1. Nonlinear and Non-Normal Models
      2. State Learning with Particle Filters
      3. Joint Learning of Parameters and States
      4. Sequential Model Comparison
  7. Part Three: Sequential Models of Financial Risk
    1. Chapter 7: Volatility Modeling
      1. Single-Asset Volatility
      2. Volatility for Multiple Assets
    2. Chapter 8: Asset-Pricing Models and Hedging
      1. Derivative Pricing in the Schwartz Model
      2. Online State-Space Model Estimates of Derivative Prices
      3. Models for Portfolios of Assets
  8. Part Four: Bayesian Risk Management
    1. Chapter 9: From Risk Measurement to Risk Management
      1. Results
      2. Prior Information as an Instrument of Corporate Governance
  9. References
  10. Index
  11. End User License Agreement