{"product_id":"bayesian-risk-management-isbn-9781118708606","title":"Bayesian Risk Management","description":"\u003cb\u003eA risk measurement and management framework that takes model risk seriously\u003c\/b\u003e \u003cp\u003eMost 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. \u003ci\u003eBayesian Risk Management\u003c\/i\u003e 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. \u003c\/p\u003e\u003cul\u003e \u003cli\u003eRecognize the assumptions embodied in classical statistics\u003c\/li\u003e \u003cli\u003eQuantify model risk along multiple dimensions without backtesting\u003c\/li\u003e \u003cli\u003eModel time series without assuming stationarity\u003c\/li\u003e \u003cli\u003eEstimate state-space time series models online with simulation methods\u003c\/li\u003e \u003cli\u003eUncover uncertainty in workhorse risk and asset-pricing models\u003c\/li\u003e \u003cli\u003eEmbed Bayesian thinking about risk within a complex organization\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eIgnoring 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. \u003ci\u003eBayesian Risk Management\u003c\/i\u003e provides a roadmap to better risk management through more circumspect measurement, with comprehensive treatment of model uncertainty. \u003c\/p\u003e\u003cp\u003ePreface ix\u003c\/p\u003e \u003cp\u003eAcknowledgments xiii\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1 Models for Discontinuous Markets 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eRisk Models and Model Risk 2\u003c\/p\u003e \u003cp\u003eTime-Invariant Models and Crisis 3\u003c\/p\u003e \u003cp\u003eErgodic Stationarity in Classical Time Series Analysis 5\u003c\/p\u003e \u003cp\u003eRecalibration Does Not Overcome the Limits of a\u003c\/p\u003e \u003cp\u003eTime-Invariant Model 7\u003c\/p\u003e \u003cp\u003eBayesian Probability as a Means of Handling Discontinuity 8\u003c\/p\u003e \u003cp\u003eAccounting for Parameter and Model Uncertainty 9\u003c\/p\u003e \u003cp\u003eResponding to Changes in the Market Environment 12\u003c\/p\u003e \u003cp\u003eTime-Invariance and Objectivity 14\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart One Capturing Uncertainty in Statistical Models\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2 Prior Knowledge, Parameter Uncertainty, and Estimation 19\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eEstimation with Prior Knowledge: The Beta-Bernoulli Model 20\u003c\/p\u003e \u003cp\u003eEncoding Prior Knowledge in the Beta-Bernoulli Model 21\u003c\/p\u003e \u003cp\u003eImpact of the Prior on the Posterior Distribution 23\u003c\/p\u003e \u003cp\u003eShrinkage and Bias 24\u003c\/p\u003e \u003cp\u003eEfficiency 25\u003c\/p\u003e \u003cp\u003eHyperparameters and Sufficient Statistics 30\u003c\/p\u003e \u003cp\u003eConjugate Prior Families 31\u003c\/p\u003e \u003cp\u003ePrior Parameter Distributions as Hypotheses: The Normal Linear Regression Model 31\u003c\/p\u003e \u003cp\u003eClassical Analysis of the Normal Linear Regression Model 32\u003c\/p\u003e \u003cp\u003eEstimation 32\u003c\/p\u003e \u003cp\u003eHypothesis Testing 34\u003c\/p\u003e \u003cp\u003eBayesian Analysis of the Normal Linear Regression Model 35\u003c\/p\u003e \u003cp\u003eHypothesis Testing with Parameter Distributions 39\u003c\/p\u003e \u003cp\u003eComparison 41\u003c\/p\u003e \u003cp\u003eDecisions after Observing the Data: The Choice of Estimators 42\u003c\/p\u003e \u003cp\u003eDecisions and Loss 43\u003c\/p\u003e \u003cp\u003eLoss and Prior Information 44\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3 Model Uncertainty 47\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eBayesian Model Comparison 49\u003c\/p\u003e \u003cp\u003eBayes Factors 49\u003c\/p\u003e \u003cp\u003eMarginal Likelihoods 50\u003c\/p\u003e \u003cp\u003eParsimony 52\u003c\/p\u003e \u003cp\u003eBayes Factors versus Information Criteria 53\u003c\/p\u003e \u003cp\u003eBayes Factors versus Likelihood Ratios 54\u003c\/p\u003e \u003cp\u003eModels as Nuisance Parameters 55\u003c\/p\u003e \u003cp\u003eThe Space of Models 56\u003c\/p\u003e \u003cp\u003eMixtures of Models 58\u003c\/p\u003e \u003cp\u003eUncertainty in Pricing Models 58\u003c\/p\u003e \u003cp\u003eFront-Office Models 59\u003c\/p\u003e \u003cp\u003eThe Statistical Nature of Front-Office Models 61\u003c\/p\u003e \u003cp\u003eA Note on Backtesting 62\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart Two Sequential Learning with Adaptive Statistical Models\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4 Introduction to Sequential Modeling 67\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSequential Bayesian Inference 68\u003c\/p\u003e \u003cp\u003eAchieving Adaptivity via Discounting 71\u003c\/p\u003e \u003cp\u003eDiscounting in the Beta-Bernoulli Model 73\u003c\/p\u003e \u003cp\u003eDiscounting in the Linear Regression Model 77\u003c\/p\u003e \u003cp\u003eComparison with the Time-Invariant Case 81\u003c\/p\u003e \u003cp\u003eAccounting for Uncertainty in Sequential Models 83\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5 Bayesian Inference in State-Space Time Series Models 87\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eState-Space Models of Time Series 88\u003c\/p\u003e \u003cp\u003eThe Filtering Problem 90\u003c\/p\u003e \u003cp\u003eThe Smoothing Problem 91\u003c\/p\u003e \u003cp\u003eDynamic Linear Models 94\u003c\/p\u003e \u003cp\u003eGeneral Form 94\u003c\/p\u003e \u003cp\u003ePolynomial Trend Components 95\u003c\/p\u003e \u003cp\u003eSeasonal Components 96\u003c\/p\u003e \u003cp\u003eRegression Components 98\u003c\/p\u003e \u003cp\u003eBuilding DLMs with Components 98\u003c\/p\u003e \u003cp\u003eRecursive Relationships in the DLM 99\u003c\/p\u003e \u003cp\u003eFiltering Recursion 99\u003c\/p\u003e \u003cp\u003eSmoothing Recursion 102\u003c\/p\u003e \u003cp\u003ePredictive Distributions and Forecasting 104\u003c\/p\u003e \u003cp\u003eVariance Estimation 105\u003c\/p\u003e \u003cp\u003eUnivariate Case 106\u003c\/p\u003e \u003cp\u003eMultivariate Case 107\u003c\/p\u003e \u003cp\u003eSequential Model Comparison 108\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6 Sequential Monte Carlo Inference 111\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eNonlinear and Non-Normal Models 113\u003c\/p\u003e \u003cp\u003eGibbs Sampling 113\u003c\/p\u003e \u003cp\u003eForward-Filtering Backward-Sampling 114\u003c\/p\u003e \u003cp\u003eState Learning with Particle Filters 116\u003c\/p\u003e \u003cp\u003eThe Particle Set 117\u003c\/p\u003e \u003cp\u003eA First Particle Filter: The Bootstrap Filter 117\u003c\/p\u003e \u003cp\u003eThe Auxiliary Particle Filter 119\u003c\/p\u003e \u003cp\u003eJoint Learning of Parameters and States 120\u003c\/p\u003e \u003cp\u003eThe Liu-West Filter 122\u003c\/p\u003e \u003cp\u003eImproving Efficiency with Sufficient Statistics 124\u003c\/p\u003e \u003cp\u003eParticle Learning 125\u003c\/p\u003e \u003cp\u003eSequential Model Comparison 126\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart Three Sequential Models of Financial Risk\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7 Volatility Modeling 131\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSingle-Asset Volatility 132\u003c\/p\u003e \u003cp\u003eClassical Models with Conditional Volatility 132\u003c\/p\u003e \u003cp\u003eRolling-Window-Based Methods 133\u003c\/p\u003e \u003cp\u003eGARCH Models 136\u003c\/p\u003e \u003cp\u003eBayesian Models 138\u003c\/p\u003e \u003cp\u003eVolatility Modeling with the DLM 139\u003c\/p\u003e \u003cp\u003eState-Space Models of Stochastic Volatility 140\u003c\/p\u003e \u003cp\u003eComparison 141\u003c\/p\u003e \u003cp\u003eVolatility for Multiple Assets 144\u003c\/p\u003e \u003cp\u003eEWMA and Inverted-Wishart Estimates 144\u003c\/p\u003e \u003cp\u003eDecompositions of the Covariance Matrix 148\u003c\/p\u003e \u003cp\u003eTime-Varying Correlations 149\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8 Asset-Pricing Models and Hedging 155\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDerivative Pricing in the Schwartz Model 156\u003c\/p\u003e \u003cp\u003eState Dynamics 157\u003c\/p\u003e \u003cp\u003eDescribing Futures Prices as a Function of Latent Factors 157\u003c\/p\u003e \u003cp\u003eContinuous- and Discrete-Time Factor Dynamics 158\u003c\/p\u003e \u003cp\u003eModel-Implied Prices and the Observation Equation 161\u003c\/p\u003e \u003cp\u003eOnline State-Space Model Estimates of Derivative Prices 162\u003c\/p\u003e \u003cp\u003eEstimation with the Liu-West Filter 163\u003c\/p\u003e \u003cp\u003ePrior Information 165\u003c\/p\u003e \u003cp\u003eEstimation Results 166\u003c\/p\u003e \u003cp\u003eEstimation Results with Discounting 176\u003c\/p\u003e \u003cp\u003eHedging with the Time-Varying Schwartz Model 188\u003c\/p\u003e \u003cp\u003eConnection with Term-Structure Models 190\u003c\/p\u003e \u003cp\u003eModels for Portfolios of Assets 191\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart Four Bayesian Risk Management\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9 From Risk Measurement to Risk Management 195\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eResults 195\u003c\/p\u003e \u003cp\u003eTime Series Analysis without Time-Invariance 196\u003c\/p\u003e \u003cp\u003ePreserving Prior Knowledge 196\u003c\/p\u003e \u003cp\u003eInformation Transmission and Loss 198\u003c\/p\u003e \u003cp\u003eBayesian State-Space Models of Time Series 199\u003c\/p\u003e \u003cp\u003eReal-Time Metrics for Model Risk 200\u003c\/p\u003e \u003cp\u003eAdaptive Estimates without Recalibration 202\u003c\/p\u003e \u003cp\u003ePrior Information as an Instrument of Corporate Governance 204\u003c\/p\u003e \u003cp\u003eReferences 207\u003cbr\u003e \u003cbr\u003e Index 213 \u003c\/p\u003e \u003cp\u003e\u003cb\u003eMATT SEKERKE\u003c\/b\u003e 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.\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eA Risk Measurement and Management Framework that Takes Model Risk Seriously\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWhy do risk models break down? The answer may lie in the way that statistical methods are conventionally used to draw inferences about market conditions and inform risk-taking behavior. \u003ci\u003eBayesian Risk Management \u003c\/i\u003eenables a discussion on the way standard statistical methods overlook uncertainty in model specifications, model parameters, and model-driven forecasts. In a simple and direct way, Bayesian methods are used throughout the book to: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eRecognize the assumptions embodied in classical statistics\u003c\/li\u003e \u003cli\u003eQuantify model risk along multiple dimensions\u003c\/li\u003e \u003cli\u003eModel time series without assuming continuity between past and future\u003c\/li\u003e \u003cli\u003eAdjust time-series estimates to maintain forecast accuracy\u003c\/li\u003e \u003cli\u003eUncover uncertainty in workhorse risk and asset-pricing models\u003c\/li\u003e \u003cli\u003eAchieve decentralized control of risk-taking in complex organizations\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003eFor firms in financial services and other industries operating in a dynamic environment of incomplete information, \u003ci\u003eBayesian Risk Management\u003c\/i\u003e provides a thought-provoking challenge to the prevailing wisdom about the uses and limitations of statistical risk modeling.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47988794360037,"sku":"NP9781118708606","price":95.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781118708606.jpg?v=1761781614","url":"https:\/\/k12savings.com\/es\/products\/bayesian-risk-management-isbn-9781118708606","provider":"K12savings","version":"1.0","type":"link"}