Bayesian Risk Management : A Guide to Model Risk and Sequential Learning in Financial Markets

Cover -- Title Page -- Copyright -- Contents -- Preface -- Acknowledgments -- Chapter 1 Models for Discontinuous Markets -- Risk Models and Model Risk -- Time-Invariant Models and Crisis -- Ergodic Stationarity in Classical Time Series Analysis -- Recalibration Does Not Overcome the Limits of a Time-Invariant Model -- Bayesian Probability as a Means of Handling Discontinuity -- Accounting for Parameter and Model Uncertainty -- Responding to Changes in the Market Environment -- Time-Invariance and Objectivity -- Part 1 Capturing Uncertainty in Statistical Models -- Chapter 2 Prior Knowledge, Parameter Uncertainty, and Estimation -- Estimation with Prior Knowledge: The Beta-Bernoulli Model -- Encoding Prior Knowledge in the Beta-Bernoulli Model -- Impact of the Prior on the Posterior Distribution -- Shrinkage and Bias -- Efficiency -- Hyperparameters and Sufficient Statistics -- Conjugate Prior Families -- Prior Parameter Distributions as Hypotheses: The Normal Linear Regression Model -- Classical Analysis of the Normal Linear Regression Model -- Estimation -- Hypothesis Testing -- Bayesian Analysis of the Normal Linear Regression Model -- Hypothesis Testing with Parameter Distributions -- Comparison -- Decisions after Observing the Data: The Choice of Estimators -- Decisions and Loss -- Loss and Prior Information -- Chapter 3 Model Uncertainty -- Bayesian Model Comparison -- Bayes Factors -- Marginal Likelihoods -- Parsimony -- Bayes Factors versus Information Criteria -- Bayes Factors versus Likelihood Ratios -- Models as Nuisance Parameters -- The Space of Models -- Mixtures of Models -- Uncertainty in Pricing Models -- Front-Office Models -- The Statistical Nature of Front-Office Models -- A Note on Backtesting -- Part 2 Sequential Learning with Adaptive Statistical Models -- Chapter 4 Introduction to Sequential Modeling.