Financial Econometrics From Basics to Advanced Modeling Techniques

by Rachev, Svetlozar T.; Mittnik, Stefan; Fabozzi, Frank J.; Focardi, Sergio M.; Ja?ić; , Teo

• ISBN13: 9780471784500
• ISBN10: 0471784508
• Edition: 1st
• Format: Hardcover
• Copyright: 2006-12-15
• 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.

Author Biography

Svetlozar T. Rachev, PhD, 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.

Stefan Mittnik is Professor of Financial Econometrics at the University of Munich, Research Director at the Ifo Institute for Economic Research in Munich, and Director of the Risk Management Program at the Center for Financial Studies in Frankfurt, Germany.

Frank J. Fabozzi, PhD, CFA, CFP, is an Adjunct Professor of Finance at Yale University's School of Management and the Editor of the Journal of Portfolio Management.

Sergio M. Focardi is a founding partner of the Paris-based consulting firm, The Intertek Group.

Teo Jasic, PhD, is a senior manager with a leading international management consultancy firm in Frankfurt, Germany, and a Post-Doctoral Research Fellow at the Chair of Statistics, Econometrics, and Mathematical Finance at the University of Karlsruhe, Germany.

Table of Contents

Preface	xi
Abbreviations and Acronyms	xv
About the Authors	xix
Financial Econometrics: Scope and Methods	1	(24)
The Data Generating Process	3	(4)
Financial Econometrics at Work	7	(3)
Time Horizon of Models	10	(2)
Applications	12	(4)
Appendix: Investment Management Process	16	(6)
Concepts Explained in this Chapter (in order of presentation)	22	(3)
Review of Probability and Statistics	25	(54)
Concepts of Probability	25	(33)
Principles of Estimation	58	(11)
Bayesian Modeling	69	(3)
Appendix A: Information Structures	72	(2)
Appendix B: Filtration	74	(1)
Concepts Explained in this Chapter (in order of presentation)	75	(4)
Regression Analysis: Theory and Estimation	79	(48)
The Concept of Dependence	79	(6)
Regressions and Linear Models	85	(5)
Estimation of Linear Regressions	90	(6)
Sampling Distributions of Regressions	96	(1)
Determining the Explanatory Power of a Regression	97	(2)
Using Regression Analysis in Finance	99	(15)
Stepwise Regression	114	(7)
Nonnormality and Autocorrelation of the Residuals	121	(2)
Pitfalls of Regressions	123	(2)
Concepts Explained in this Chapter (in order of presentation)	125	(2)
Selected Topics in Regression Analysis	127	(42)
Categorical and Dummy Variables in Regression Models	127	(24)
Constrained Least Squares	151	(12)
The Method of Moments and its Generalizations	163	(4)
Concepts Explained in this Chapter (in order of presentation)	167	(2)
Regression Applications in Finance	169	(32)
Applications to the Investment Management Process	169	(5)
A Test of Strong-Form Pricing Efficiency	174	(1)
Tests of the CAPM	175	(4)
Using the CAPM to Evaluate Manager Performance: The Jensen Measure	179	(1)
Evidence for Multifactor Models	180	(4)
Benchmark Selection: Sharpe Benchmarks	184	(2)
Return-Based Style Analysis for Hedge Funds	186	(5)
Hedge Fund Survival	191	(1)
Bond Portfolio Applications	192	(7)
Concepts Explained in this Chapter (in order of presentation)	199	(2)
Modeling Univariate Time Series	201	(40)
Difference Equations	201	(6)
Terminology and Definitions	207	(7)
Stationarity and Invertibility of ARMA Processes	214	(5)
Linear Processes	219	(4)
Identification Tools	223	(16)
Concepts Explained in this Chapter (in order of presentation)	239	(2)
Approaches to ARIMA Modeling and Forecasting	241	(38)
Overview of Box-Jenkins Procedure	242	(2)
Identification of Degree of Differencing	244	(6)
Identification of Lag Orders	250	(3)
Model Estimation	253	(9)
Diagnostic Checking	262	(9)
Forecasting	271	(6)
Concepts Explained in this Chapter (in order of presentation)	277	(2)
Autoregressive Conditional Heteroskedastic Models	279	(42)
ARCH Process	280	(4)
GARCH Process	284	(5)
Estimation of the GARCH Models	289	(4)
Stationary ARMA-GARCH Models	293	(1)
Lagrange Multiplier Test	294	(4)
Variants of the GARCH Model	298	(1)
GARCH Model with Student's t-Distributed Innovations	299	(15)
Multivariate GARCH Formulations	314	(2)
Appendix: Analysis of the Properties of the GARCH(1,1) Model	316	(3)
Concepts Explained in this Chapter (in order of presentation)	319	(2)
Vector Autoregressive Models I	321	(22)
VAR Models Defined	321	(13)
Stationary Autoregressive Distributed Lag Models	334	(1)
Vector Autoregressive Moving Average Models	335	(3)
Forecasting with VAR Models	338	(1)
Appendix: Eigenvectors and Eigenvalues	339	(2)
Concepts Explained in this Chapter (in order of presentation)	341	(2)
Vector Autoregressive Models II	343	(30)
Estimation of Stable VAR Models	343	(14)
Estimating the Number of Lags	357	(2)
Autocorrelation and Distributional Properties of Residuals	359	(1)
VAR Illustration	360	(12)
Concepts Explained in this Chapter (in order of presentation)	372	(1)
Cointegration and State Space Models	373	(34)
Cointegration	373	(8)
Error Correction Models	381	(4)
Theory and Methods of Estimation of Nonstationary VAR Models	385	(13)
State-Space Models	398	(6)
Concepts Explained in this Chapter (in order of presentation)	404	(3)
Robust Estimation	407	(22)
Robust Statistics	407	(10)
Robust Estimators of Regressions	417	(4)
Illustration: Robustness of the Corporate Bond Yield Spread Model	421	(7)
Concepts Explained in this Chapter (in order of presentation)	428	(1)
Principal Components Analysis and Factor Analysis	429	(36)
Factor Models	429	(7)
Principal Components Analysis	436	(14)
Factor Analysis	450	(11)
PCA and Factor Analysis Compared	461	(3)
Concepts Explained in this Chapter (in order of presentation)	464	(1)
Heavy-Tailed and Stable Distributions in Financial Econometrics	465	(30)
Basic Facts and Definitions of Stable Distributions	468	(7)
Properties of Stable Distributions	475	(4)
Estimation of the Parameters of the Stable Distribution	479	(6)
Applications to German Stock Data	485	(2)
Appendix: Comparing Probability Distributions	487	(7)
Concepts Explained in this Chapter (in order of presentation)	494	(1)
ARMA and ARCH Models with Infinite-Variance Innovations	495	(22)
Infinite Variance Autoregressive Processes	495	(6)
Stable GARCH Models	501	(6)
Estimation for the Stable GARCH Model	507	(6)
Prediction of Conditional Densities	513	(3)
Concepts Explained in this Chapter (in order of presentation)	516	(1)
Appendix Monthly Returns for 20 Stocks: December 2000--November 2005	517	(8)
Index	525