Bayesian methods in finance / Svetlozar T. Rachev ... [et al.].

Format:

Book

Contributor:

Rachev, S. T. (Svetlozar Todorov)

Series:

Frank J. Fabozzi series.

The Frank J. Fabozzi series

Language:

English

Subjects (All):

Finance--Mathematical models.

Finance.

Bayesian statistical decision theory.

Physical Description:

1 online resource (351 p.)

Edition:

1st ed.

Place of Publication:

Hoboken, N.J. : Wiley, c2008.

Language Note:

English

Summary:

Bayesian 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.

Contents:

Bayesian 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

THE 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

APPENDIX B: LIKELIHOOD FUNCTION OF A CANDIDATE MODELCHAPTER 8 The Black-Litterman Portfolio Selection Framework; PRELIMINARIES; COMBINING MARKET EQUILIBRIUM AND INVESTOR VIEWS; THE CHOICE OF τ AND ω; 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

ILLUSTRATION: 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

PRELIMINARIES 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

EXTENDING THE BLACK-LITTERMAN APPROACH: STABLE DISTRIBUTION

Notes:

Description based upon print version of record.

Includes bibliographical references (p. 298-309) and index.

Description based on metadata supplied by the publisher and other sources.

ISBN:

9786611217266

9781119202141

1119202140

9781281217264

1281217263

9780470249246

0470249242

OCLC:

211911037