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Bayesian Methods in Finance

ISBN-10: 0471920835
ISBN-13: 9780471920830
Edition: 2008
List price: $95.00 Buy it from $71.58
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Description: An accessible overview of the theory and practice of Bayesian methods in finance Bayesian Methods in Finance explains and illustrates the foundations of the Bayesian methodology in clear and accessible terms. It provides a unified examination of  More...

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Book details

List price: $95.00
Copyright year: 2008
Publisher: John Wiley & Sons, Incorporated
Publication date: 2/8/2008
Binding: Hardcover
Pages: 329
Size: 6.50" wide x 9.00" long x 1.00" tall
Weight: 1.188
Language: English

An accessible overview of the theory and practice of Bayesian methods in finance Bayesian Methods in Finance explains and illustrates the foundations of the Bayesian methodology in clear and accessible terms. It provides a unified examination of the use of the Bayesian theory and practice to analyze and evaluate asset management. With this book as their guide, readers will learn how to use Bayesian methods, and notably, the Markov Chain Monte Carlo toolbox, to incorporate the prior views of a fund manager into the asset allocation process, estimate and predict volatility, improve risk forecasts, calculate option prices, and combine the conclusions of different models. Bayesian Methods in Finance clearly shows readers how to apply this approach to the world of investment management, risk management, asset pricing, and corporate finance. Svetlozar T. Rachev, PhD, DrSci (Karlsruhe, Germany) is Chair-Professor at the University of Karlsruhe in the School of Economics and Business Engineering and Chief Scientist of FinAnalytica Inc. John S.J. Hsu, PhD (Santa Barbara, CA) is Associate Professor of Statistics and Applied Probability at the University of California, Santa Barbara. Biliana S. Bagasheva (Santa Barbara, CA) is currently a PhD candidate at the Department of Statistics and Applied Probability, University of California, Santa Barbara. Frank J. Fabozzi, PhD, CFA, CFP (New Hope, PA) is Adjunct Professor of Finance at Yale University's School of Management.

Svetlozar T. Rachev, PhD, Doctor of Science, is Chair-Professor at the University of Karlsruhe in the School of Economics and Business Engineering; Professor Emeritus at the University of California, Santa Barbara; and Chief-Scientist of FinAnalytica Inc. John S. J. Hsu, PhD, is Professor of Statistics and Applied Probability at the University of California, Santa Barbara. Biliana S. Bagasheva, PhD, has research interests in the areas of risk management, portfolio construction, Bayesian methods, and financial econometrics. Currently, she is a consultant in London. Frank J. Fabozzi, PhD, CFA, is Professor in the Practice of Finance and Becton Fellow at Yale University's School of Management and the Editor of the Journal of Portfolio Management.

Douglas J. Lucas is Executive Director and Head of CDO Research at UBS. He is also Chairman of The Bond Market Association's CDO Research Committee and ranked top three in CDO research in the Institutional Investor's fixed income analyst survey. Lucas has been involved in the CDO market for nearly two decades, having developed Moody's rating methodology for CDOs in 1989.LAURIE S. GOODMAN, PhD, is Managing Director and co-Head of Global Fixed Income Research at UBS. She manages U.S. Securitized Products and Treasury/Agency/Derivatives Research. Goodman has worked on Wall Street for over twenty years and is well regarded by the investor community, having won more #1 slots on the Institutional Investor All-American Fixed Income Team than any other analyst.FRANK J. FABOZZI, PhD, CFA, CPA, is an Adjunct Professor of Finance and Becton Fellow at Yale University's School of Management and a Fellow of the International Center for Finance. Fabozzi is the Editor of the Journal of Portfolio Management.

Preface
About the Author's
Introduction
A Few Notes on Notation
Overview
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
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
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
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
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
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
Mean and Covariance with Diffuse (Improper) Priors
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
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
Numerical Simulation of the Predictive Distribution
Sampling from the Predictive Distribution
Likelihood Function of a Candidate Model
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
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
Vector Autoregressive Setup
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?
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
Griddy Gibbs Sampler
Drawing from the Conditional Posterior Distribution of [nu]
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
Kalman Filtering and Smoothing
The Kalman Filter Algorithm
The Smoothing Algorithm
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
Some Risk Measures Employed in Portfolio Construction
CVaR Optimization
A Brief Overview of Copulas
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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