Financial Econometrics: From Basics to Advanced Modeling Techniques

by Svetlozar T. Rachev (Univ. of California, Santa Barbara); Stefan Mittnik (Univ. of Kiel, Germany); Frank J. Fabozzi (School of Management, Yale Univ.); Sergio M. Focardi; Teo Jaš; ić;

• ISBN13: 9780470121528
• ISBN10: 0470121521
• Format: eBook
• Copyright: 2007-03-01
• Publisher: Wiley

Summary

A comprehensive guide to financial econometrics Financial econometrics is a quest for models that describe financial time series such as prices, returns, interest rates, and exchange rates. In Financial Econometrics, readers will be introduced to this growing discipline and the concepts and theories associated with it, including background material on probability theory and statistics. The experienced author team uses real-world data where possible and brings in the results of published research provided by investment banking firms and journals. Financial Econometrics clearly explains the techniques presented and provides illustrative examples for the topics discussed. Svetlozar T. Rachev, PhD (Karlsruhe, Germany) is currently Chair-Professor at the University of Karlsruhe. Stefan Mittnik, PhD (Munich, Germany) is Professor of Financial Econometrics at the University of Munich. Frank J. Fabozzi, PhD, CFA, CFP (New Hope, PA) is an adjunct professor of Finance at Yale University's School of Management. Sergio M. Focardi (Paris, France) is a founding partner of the Paris-based consulting firm The Intertek Group. Teo Jasic, PhD, (Frankfurt, Germany) is a senior manager with a leading international management consultancy firm in Frankfurt.

Table of Contents

Preface

Abbreviations and Acronyms

About the Authors

Financial Econometrics: Scope and Methods

The Data Generating Process

Financial Econometrics at Work

Time Horizon of Models

Applications

Appendix: Investment Management Process

Concepts Explained in this Chapter (in order of presentation)

Review of Probability and Statistics

Concepts of Probability

Principles of Estimation

Bayesian Modeling

Information Structures

Filtration

Concepts Explained in this Chapter (in order of presentation)

Regression Analysis: Theory and Estimation

The Concept of Dependence

Regressions and Linear Models

Estimation of Linear Regressions

Sampling Distributions of Regressions

Determining the Explanatory Power of a Regression

Using Regression Analysis in Finance

Stepwise Regression

Nonnormality and Autocorrelation of the Residuals

Pitfalls of Regressions

Concepts Explained in this Chapter (in order of presentation)

Selected Topics in Regression Analysis

Categorical and Dummy Variables in Regression Models

Constrained Least Squares

The Method of Moments and its Generalizations

Concepts Explained in this Chapter (in order of presentation)

Regression Applications in Finance

Applications to the Investment Management Process

A Test of Strong-Form Pricing Efficiency

Tests of the CAPM

Using the CAPM to Evaluate Manager Performance: The Jensen Measure

Evidence for Multifactor Models

Benchmark Selection: Sharpe Benchmarks

Return-Based Style Analysis for Hedge Funds

Hedge Fund Survival

Bond Portfolio Applications

Concepts Explained in this Chapter (in order of presentation)

Modeling Univariate Time Series

Difference Equations

Terminology and Definitions

Stationarity and Invertibility of ARMA Processes

Linear Processes

Identification Tools

Concepts Explained in this Chapter (in order of presentation)

Approaches to ARIMA Modeling and Forecasting

Overview of Box-Jenkins Procedure

Identification of Degree of Differencing

Identification of Lag Orders

Model Estimation

Diagnostic Checking

Forecasting

Concepts Explained in this Chapter (in order of presentation)

Autoregressive Conditional Heteroskedastic Models

ARCH Process

GARCH Process

Estimation of the GARCH Models

Stationary ARMA-GARCH Models

Lagrange Multiplier Test

Variants of the GARCH Model

GARCH Model with Student's t-Distributed Innovations

Multivariate GARCH Formulations

Appendix: Analysis of the Properties of the GARCH(1,1) Model

Concepts Explained in this Chapter (in order of presentation)

Vector Autoregressive Models I

VAR Models Defined

Stationary Autoregressive Distributed Lag Models

Vector Autoregressive Moving Average Models

Forecasting with VAR Models

Appendix: Eigenvectors and Eigenvalues

Concepts Explained in this Chapter (in order of presentation)

Vector Autoregressive Models II

Estimation of Stable VAR Models

Estimating the Number of Lags

Autocorrelation and Distributional Properties of Residuals

VAR Illustration

Concepts Explained in this Chapter (in order of presentation)

Cointegration and State Space Models

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