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Informationen zum Autor MATT SEKERKE is an economic consultant based in New York whose work focuses on the financial services industry and the application of advanced quantitative modeling techniques o financial data. He holds a BA in economics and mathematics from The Johns Hopkins University, an MA in history from The Johns Hopkins University, and an MBA in econometrics and statistics, analytic finance, and entrepreneurship from The University of Chicago Booth School of Business. He is also a CFA charterholder, a certified Financial Risk Manager, and a certified Energy Risk Professional. Klappentext A risk measurement and management framework that takes model risk seriouslyMost 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 organizationIgnoring 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. Zusammenfassung 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. Inhaltsverzeichnis Preface ix Acknowledgments xiii Chapter 1 Models for Discontinuous Markets 1 Risk Models and Model Risk 2 Time-Invariant Models and Crisis 3 Ergodic Stationarity in Classical Time Series Analysis 5 Recalibration Does Not Overcome the Limits of a Time-Invariant Model 7 Bayesian Probability as a Means of Handling Discontinuity 8 Accounting for Parameter and Model Uncertainty 9 Responding to Changes in the Market Environment 12 Time-Invariance and Objectivity 14 Part One Capturing Uncertainty in Statistical Models Chapter 2 Prior Knowledge, Parameter Uncertainty, and Estimation 19 Estimation with Prior Knowledge: The Beta-Bernoulli Model 20 Encoding Prior Knowledge in the Beta-Bernoulli Model 21 Impact of the Prior on the Posterior Distribution 23 Shrinkage and Bias 24 Efficiency 25 Hyperparameters and Sufficient Statistics 30 Conjugate Prior Families 31 Prior Parameter Distributions as Hypotheses: The Normal Linear Regression Model 31 Classical Analysis of the Normal Linear Regression Model 32 Estimation 32 Hypothesis Testing 34 Bayesian Analysis of the Normal Linear Regression Model 35 Hypothesis Testing with Parameter Distributions 39 Comparison 41 Decisions after Observing the Data: The Choice of Estimators 42 Decisions and Loss 43
