LEADER 05739nam 2200781 450 001 9910810481303321 005 20230912131745.0 010 $a1-118-74750-X 010 $a1-118-86478-6 010 $a1-118-74745-3 035 $a(CKB)3710000000466341 035 $a(EBL)1964094 035 $a(SSID)ssj0001543355 035 $a(PQKBManifestationID)16135767 035 $a(PQKBTitleCode)TC0001543355 035 $a(PQKBWorkID)14642203 035 $a(PQKB)11198298 035 $a(PQKBManifestationID)14475120 035 $a(PQKBWorkID)13718317 035 $a(PQKB)23574278 035 $a(DLC) 2015015965 035 $a(Au-PeEL)EBL4038314 035 $a(CaPaEBR)ebr11092764 035 $a(CaONFJC)MIL824843 035 $a(OCoLC)907811653 035 $a(Au-PeEL)EBL1964094 035 $a(CaSebORM)9781118708606 035 $a(MiAaPQ)EBC4038314 035 $a(MiAaPQ)EBC1964094 035 $a(EXLCZ)993710000000466341 100 $a20160223h20152015 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aBayesian risk management $ea guide to model risk and sequential learning in financial markets /$fMatt Sekerke 205 $a1st edition 210 1$aHoboken, New Jersey :$cWiley,$d2015. 210 4$dİ2015 215 $a1 online resource (238 p.) 225 1 $aWiley Finance Series 300 $aDescription based upon print version of record. 311 $a1-118-70860-1 320 $aIncludes bibliographical references and index. 327 $aCover; 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 327 $aChapter 2 Prior Knowledge, Parameter Uncertainty, and EstimationEstimation 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 327 $aHypothesis Testing with Parameter DistributionsComparison; 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 327 $aPart 2 Sequential Learning with Adaptive Statistical ModelsChapter 4 Introduction to Sequential Modeling; Sequential Bayesian Inference; Achieving Adaptivity via Discounting; Discounting in the Beta-Bernoulli Model; Discounting in the Linear Regression Model; Comparison with the Time-Invariant Case; Accounting for Uncertainty in Sequential Models; Chapter 5 Bayesian Inference in State-Space Time Series Models; State Space Models of Time Series; The Filtering Problem; The Smoothing Problem; Dynamic Linear Models; General Form; Polynomial Trend Components; Seasonal Components 327 $aRegression ComponentsBuilding DLMs with Components; Recursive Relationships in the DLM; Filtering Recursion; Smoothing Recursion; Predictive Distributions and Forecasting; Variance Estimation; Univariate Case; Multivariate Case; Sequential Model Comparison; Chapter 6 Sequential Monte Carlo Inference; Nonlinear and Non-Normal Models; Gibbs Sampling; Forward-Filtering Backward-Sampling; State Learning with Particle Filters; The Particle Set; A First Particle Filter: The Bootstrap Filter; The Auxiliary Particle Filter; Joint Learning of Parameters and States; The Liu-West Filter 327 $aImproving Efficiency with Sufficient Statistics 330 $aA risk measurement and management framework that takes model risk seriously Most financial risk models assume the future will look like the past, but effective risk management depends on identifying fundamental changes in the marketplace as they occur. Bayesian Risk Management details a more flexible approach to risk management, and provides tools to measure financial risk in a dynamic market environment. This book opens discussion about uncertainty in model parameters, model specifications, and model-driven forecasts in a way that standard statistical risk measurement does not. And unlike cu 410 0$aWiley finance series. 606 $aFinance$xMathematical models 606 $aFinancial risk management$xMathematical models 606 $aBayesian statistical decision theory 615 0$aFinance$xMathematical models. 615 0$aFinancial risk management$xMathematical models. 615 0$aBayesian statistical decision theory. 676 $a332/.041501519542 700 $aSekerke$b Matt$01649931 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910810481303321 996 $aBayesian risk management$93999025 997 $aUNINA