LEADER 05584nam 2200793Ia 450 001 9910817059303321 005 20200520144314.0 010 $a9786611217266 010 $a9781119202141 010 $a1119202140 010 $a9781281217264 010 $a1281217263 010 $a9780470249246 010 $a0470249242 035 $a(CKB)1000000000414804 035 $a(EBL)331607 035 $a(OCoLC)211911037 035 $a(SSID)ssj0000109307 035 $a(PQKBManifestationID)11135765 035 $a(PQKBTitleCode)TC0000109307 035 $a(PQKBWorkID)10045733 035 $a(PQKB)10491474 035 $a(MiAaPQ)EBC331607 035 $a(Au-PeEL)EBL331607 035 $a(CaPaEBR)ebr10225368 035 $a(CaONFJC)MIL121726 035 $a(OCoLC)72655606 035 $a(FINmELB)ELB177444 035 $a(FR-PaCSA)88944042 035 $a(FRCYB88944042)88944042 035 $a(Perlego)2751765 035 $a(EXLCZ)991000000000414804 100 $a20070403d2008 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aBayesian methods in finance /$fSvetlozar T. Rachev ... [et al.] 205 $a1st ed. 210 $aHoboken, N.J. $cWiley$dc2008 215 $a1 online resource (351 p.) 225 1 $aThe Frank J. Fabozzi series 300 $aDescription based upon print version of record. 311 08$a9780471920830 311 08$a0471920835 320 $aIncludes bibliographical references (p. 298-309) and index. 327 $aBayesian Methods in Finance; Contents; Preface; About the Authors; CHAPTER 1 Introduction; A FEW NOTES ON NOTATION; OVERVIEW; CHAPTER 2 The Bayesian Paradigm; THE LIKELIHOOD FUNCTION; THE BAYES' THEOREM; SUMMARY; CHAPTER 3 Prior and Posterior Information, Predictive Inference; PRIOR INFORMATION; POSTERIOR INFERENCE; BAYESIAN PREDICTIVE INFERENCE; ILLUSTRATION: POSTERIOR TRADE-OFF AND THENORMAL MEAN PARAMETER; SUMMARY; APPENDIX: DEFINITIONS OF SOME UNIVARIATE AND MULTIVARIATE STATISTICAL DISTRIBUTIONS; CHAPTER 4 Bayesian Linear Regression Model; THE UNIVARIATE LINEAR REGRESSION MODEL 327 $aTHE MULTIVARIATE LINEAR REGRESSION MODELSUMMARY; CHAPTER 5 Bayesian Numerical Computation; MONTE CARLO INTEGRATION; ALGORITHMS FOR POSTERIOR SIMULATION; APPROXIMATION METHODS: LOGISTIC REGRESSION; SUMMARY; CHAPTER 6 Bayesian Framework for Portfolio Allocation; CLASSICAL PORTFOLIO SELECTION; BAYESIAN PORTFOLIO SELECTION; SHRINKAGE ESTIMATORS; UNEQUAL HISTORIES OF RETURNS; SUMMARY; CHAPTER 7 Prior Beliefs and Asset Pricing Models; PRIOR BELIEFS AND ASSET PRICING MODELS; MODEL UNCERTAINTY; SUMMARY; APPENDIX A: NUMERICAL SIMULATION OF THE PREDICTIVE DISTRIBUTION 327 $aAPPENDIX B: LIKELIHOOD FUNCTION OF A CANDIDATE MODELCHAPTER 8 The Black-Litterman Portfolio Selection Framework; PRELIMINARIES; COMBINING MARKET EQUILIBRIUM AND INVESTOR VIEWS; THE CHOICE OF ? AND ?; THE OPTIMAL PORTFOLIO ALLOCATION; INCORPORATING TRADING STRATEGIES INTO THE BLACK-LITTERMAN MODEL; ACTIVE PORTFOLIO MANAGEMENT AND THE BLACK-LITTERMAN MODEL; COVARIANCE MATRIX ESTIMATION; SUMMARY; CHAPTER 9 Market Efficiency and Return Predictability; TESTS OF MEAN-VARIANCE EFFICIENCY; INEFFICIENCY MEASURES IN TESTING THE CAPM; TESTING THE APT; RETURN PREDICTABILITY 327 $aILLUSTRATION: PREDICTABILITY AND THE INVESTMENT HORIZONSUMMARY; APPENDIX: VECTOR AUTOREGRESSIVE SETUP; CHAPTER 10 Volatility Models; GARCH MODELS OF VOLATILITY; STOCHASTIC VOLATILITY MODELS; ILLUSTRATION: FORECASTING VALUE-AT-RISK; AN ARCH-TYPE MODEL OR A STOCHASTIC VOLATILITY MODEL?; WHERE DO BAYESIAN METHODS FIT?; CHAPTER 11 Bayesian Estimation of ARCH-Type Volatility Models; BAYESIAN ESTIMATION OF THE SIMPLE GARCH(1,1) MODEL; MARKOV REGIME-SWITCHING GARCH MODELS; SUMMARY; APPENDIX: GRIDDY GIBBS SAMPLER; CHAPTER 12 Bayesian Estimation of Stochastic Volatility Models 327 $aPRELIMINARIES OF SV MODEL ESTIMATIONTHE SINGLE-MOVE MCMC ALGORITHM FOR SV MODEL ESTIMATION; THE MULTIMOVE MCMC ALGORITHM FOR SV MODEL ESTIMATION; JUMP EXTENSION OF THE SIMPLE SV MODEL; VOLATILITY FORECASTING AND RETURN PREDICTION; SUMMARY; APPENDIX: KALMAN FILTERING AND SMOOTHING; CHAPTER 13 Advanced Techniques for Bayesian Portfolio Selection; DISTRIBUTIONAL RETURN ASSUMPTIONS ALTERNATIVE TO NORMALITY; PORTFOLIO SELECTION IN THE SETTING OF NONNORMALITY: PRELIMINARIES; MAXIMIZATION OF UTILITY WITH HIGHER MOMENTS; EXTENDING THE BLACK-LITTERMAN APPROACH: COPULA OPINION POOLING 327 $aEXTENDING THE BLACK-LITTERMAN APPROACH: STABLE DISTRIBUTION 330 $aBayesian Methods in Finance provides a detailed overview of the theory of Bayesian methods and explains their real-world applications to financial modeling. While the principles and concepts explained throughout the book can be used in financial modeling and decision making in general, the authors focus on portfolio management and market risk management-since these are the areas in finance where Bayesian methods have had the greatest penetration to date. 410 0$aFrank J. Fabozzi series. 606 $aFinance$xMathematical models 606 $aBayesian statistical decision theory 615 0$aFinance$xMathematical models. 615 0$aBayesian statistical decision theory. 676 $a332 676 $a332.01519542 701 $aRachev$b S. T$g(Svetlozar Todorov)$059738 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910817059303321 996 $aBayesian methods in finance$94126207 997 $aUNINA