Bayesian methods in finance /

Bayesian methods in finance /

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Bibliographic Details
Imprint:Hoboken, N.J. : Wiley, c2008.
Description:xviii, 329 p. : ill., charts ; 24 cm.
Language:English
Series:The Frank J. Fabozzi series
Frank J. Fabozzi series.
Subject:
Finance -- Mathematical models.
Bayesian statistical decision theory.
Bayesian statistical decision theory.
Finance -- Mathematical models.
Format: Print Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/6825611
Hidden Bibliographic Details
Other authors / contributors:Rachev, S. T. (Svetlozar Todorov)
ISBN:0471920835 (hbk.)
9780471920830 (hbk.)
Notes:Includes bibliographical references (p. 298-309) and index.
20110407
Summary:"The aim of Bayesian Methods in Finance is to provide an overview of the theory of Bayesian methods and explain their real-world applications to financial modeling. While the principles and concepts explained in 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." "Bayesian Methods in Finance offers both students of finance and practitioners an invaluable resource in the form of a previously unavailable, highly accessible, unified look at the use of the Bayesian methodology - as well as numerical computational methods - in financial models and asset management."--BOOK JACKET.
Table of Contents:
  • Preface
  • About the Author's
  • Chapter 1. Introduction
  • A Few Notes on Notation
  • Overview
  • Chapter 2. The Bayesian Paradigm
  • The Likelihood Function
  • The Poisson Distribution Likelihood Function
  • The Normal Distribution Likelihood Function
  • The Bayes' Theorem
  • Bayes' Theorem and Model Selection
  • Bayes' Theorem and Classification
  • Bayesian Inference for the Binomial Probability
  • Summary
  • Chapter 3. Prior and Posterior Information, Predictive Inference
  • Prior Information
  • Informative Prior Elicitation
  • Noninformative Prior Distributions
  • Conjugate Prior Distributions
  • Empirical Bayesian Analysis
  • Posterior Inference
  • Posterior Point Estimates
  • Bayesian Intervals
  • Bayesian Hypothesis Comparison
  • Bayesian Predictive Inference
  • Illustration: Posterior Trade-off and the Normal Mean Parameter
  • Summary
  • Appendix. Definitions of Some Univariate and Multivariate Statistical Distributions
  • The Univariate Normal Distribution
  • The Univariate Student's t-Distribution
  • The Inverted x[superscript 2] Distribution
  • The Multivariate Normal Distribution
  • The Multivariate Student's t-Distribution
  • The Wishart Distribution
  • The Inverted Wishart Distribution
  • Chapter 4. Bayesian Linear Regression Model
  • The Univariate Linear Regression Model
  • Bayesian Estimation of the Univariate Regression Model
  • Illustration: The Univariate Linear Regression Model
  • The Multivariate Linear Regression Model
  • Diffuse Improper Prior
  • Summary
  • Chapter 5. Bayesian Numerical Computation
  • Monte Carlo Integration
  • Algorithms for Posterior Simulation
  • Rejection Sampling
  • Importance Sampling
  • MCMC Methods
  • Linear Regression with Semiconjugate Prior
  • Approximation Methods: Logistic Regression
  • The Normal Approximation
  • The Laplace Approximation
  • Summary
  • Chapter 6. Bayesian Framework For Portfolio Allocation
  • Classical Portfolio Selection
  • Portfolio Selection Problem Formulations
  • Mean-Variance Efficient Frontier
  • Illustration: Mean-Variance Optimal Portfolio with Portfolio Constraints
  • Bayesian Portfolio Selection
  • Prior Scenario 1. Mean and Covariance with Diffuse (Improper) Priors
  • Prior Scenario 2. Mean and Covariance with Proper Priors
  • The Efficient Frontier and the Optimal Portfolio
  • Illustration: Bayesian Portfolio Selection
  • Shrinkage Estimators
  • Unequal Histories of Returns
  • Dependence of the Short Series on the Long Series
  • Bayesian Setup
  • Predictive Moments
  • Summary
  • Chapter 7. Prior Beliefs and Asset Pricing Models
  • Prior Beliefs and Asset Pricing Models
  • Preliminaries
  • Quantifying the Belief About Pricing Model Validity
  • Perturbed Model
  • Likelihood Function
  • Prior Distributions
  • Posterior Distributions
  • Predictive Distributions and Portfolio Selection
  • Prior Parameter Elicitation
  • Illustration: Incorporating Confidence about the Validity of an Asset Pricing Model
  • Model Uncertainty
  • Bayesian Model Averaging
  • Illustration: Combining Inference from the CAPM and the Fama and French Three-Factor Model
  • Summary
  • Appendix A. Numerical Simulation of the Predictive Distribution
  • Sampling from the Predictive Distribution
  • Appendix B. Likelihood Function of a Candidate Model
  • Chapter 8. The Black-Litterman Portfolio Selection Framework
  • Preliminaries
  • Equilibrium Returns
  • Investor Views
  • Distributional Assumptions
  • Combining Market Equilibrium and Investor Views
  • The Choice of [tau] and [Omega]
  • The Optimal Portfolio Allocation
  • Illustration: Black-Litterman Optimal Allocation
  • Incorporating Trading Strategies into the Black-Litterman Model
  • Active Portfolio Management and the Black-Litterman Model
  • Views on Alpha and the Black-Litterman Model
  • Translating a Qualitative View into a Forecast for Alpha
  • Covariance Matrix Estimation
  • Summary
  • Chapter 9. Market Efficiency and Return Predictability
  • Tests of Mean-Variance Efficiency
  • Inefficiency Measures in Testing the CAPM
  • Distributional Assumptions and Posterior Distributions
  • Efficiency under Investment Constraints
  • Illustration: The Inefficiency Measure, [Delta superscript R]
  • Testing the APT
  • Distributional Assumptions, Posterior and Predictive Distributions
  • Certainty Equivalent Returns
  • Return Predictability
  • Posterior and Predictive Inference
  • Solving the Portfolio Selection Problem
  • Illustration: Predictability and the Investment Horizon
  • Summary
  • Appendix. Vector Autoregressive Setup
  • Chapter 10. Volatility Models
  • Garch Models of Volatility
  • Stylized Facts about Returns
  • Modeling the Conditional Mean
  • Properties and Estimation of the GARCH(1,1) Process
  • Stochastic Volatility Models
  • Stylized Facts about Returns
  • Estimation of the Simple SV Model
  • 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
  • Distributional Setup
  • Mixture of Normals Representation of the Student's t-Distribution
  • GARCH(1,1) Estimation Using the Metropolis-Hastings Algorithm
  • Illustration: Student's t GARCH(1,1) Model
  • Markov Regime-switching GARCH Models
  • Preliminaries
  • Prior Distributional Assumptions
  • Estimation of the MS GARCH(1,1) Model
  • Sampling Algorithm for the Parameters of the MS GARCH(1,1) Model
  • Illustration: Student's t MS GARCH(1,1) Model
  • Summary
  • Appendix. Griddy Gibbs Sampler
  • Drawing from the Conditional Posterior Distribution of [nu]
  • Chapter 12. Bayesian Estimation of Stochastic Volatility Models
  • Preliminaries of SV Model Estimation
  • Likelihood Function
  • The Single-Move MCMC Algorithm for SV Model Estimation
  • Prior and Posterior Distributions
  • Conditional Distribution of the Unobserved Volatility
  • Simulation of the Unobserved Volatility
  • Illustration
  • The Multimove MCMC Algorithm for SV Model Estimation
  • Prior and Posterior Distributions
  • Block Simulation of the Unobserved Volatility
  • Sampling Scheme
  • Illustration
  • Jump Extension of the Simple SV Model
  • Volatility Forecasting and Return Prediction
  • Summary
  • Appendix. Kalman Filtering and Smoothing
  • The Kalman Filter Algorithm
  • The Smoothing Algorithm
  • Chapter 13. Advanced Techniques for Bayesian Portfolio Selection
  • Distributional Return Assumptions Alternative to Normality
  • Mixtures of Normal Distributions
  • Asymmetric Student's t-Distributions
  • Stable Distributions
  • Extreme Value Distributions
  • Skew-Normal Distributions
  • The Joint Modeling of Returns
  • Portfolio Selection in the Setting of Nonnormality: Preliminaries
  • Maximization of Utility with Higher Moments
  • Coskewness
  • Utility with Higher Moments
  • Distributional Assumptions and Moments
  • Likelihood, Prior Assumptions, and Posterior Distributions
  • Predictive Moments and Portfolio Selection
  • Illustration: HLLM's Approach
  • Extending The Black-Litterman Approach: Copula Opinion Pooling
  • Market-Implied and Subjective Information
  • Views and View Distributions
  • Combining the Market and the Views: The Marginal Posterior View Distributions
  • Views Dependence Structure: The Joint Posterior View Distribution
  • Posterior Distribution of the Market Realizations
  • Portfolio Construction
  • Illustration: Meucci's Approach
  • Extending The Black-Litterman Approach:Stable Distribution
  • Equilibrium Returns Under Nonnormality
  • Summary
  • Appendix A. Some Risk Measures Employed in Portfolio Construction
  • Appendix B. CVaR Optimization
  • Appendix C. A Brief Overview of Copulas
  • Chapter 14. Multifactor Equity Risk Models
  • Preliminaries
  • Statistical Factor Models
  • Macroeconomic Factor Models
  • Fundamental Factor Models
  • Risk Analysis Using a Multifactor Equity Model
  • Covariance Matrix Estimation
  • Risk Decomposition
  • Return Scenario Generation
  • Predicting the Factor and Stock-Specific Returns
  • Risk Analysis in a Scenario-Based Setting
  • Conditional Value-at-Risk Decomposition
  • Bayesian Methods for Multifactor Models
  • Cross-Sectional Regression Estimation
  • Posterior Simulations
  • Return Scenario Generation
  • Illustration
  • Summary
  • References
  • Index
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