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eISBN : 9780470249246

Contents :
Preface

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 ?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 t and O
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, ?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 ?

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

Language : English

Location : Nice Library

Material : Electronic

Statement : Présent

Owner : Bibliothèque