Financial Econometrics / Edition 1

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Overview

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.

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Product Details

  • ISBN-13: 9780471784500
  • Publisher: Wiley
  • Publication date: 12/8/2006
  • Series: Frank J. Fabozzi Series , #150
  • Edition number: 1
  • Pages: 576
  • Product dimensions: 6.14 (w) x 9.21 (h) x 1.25 (d)

Meet the Author

Svetlozar (Zari) T. Rachev completed his Ph.D. Degree in1979 from Moscow State (Lomonosov) University, and his Doctor ofScience Degree in 1986 from Steklov Mathematical Institute inMoscow. Currently he is Chair-Professor in Statistics, Econometricsand Mathematical Finance at the University of Karlsruhe in theSchool of Economics and Business Engineering. He is also ProfessorEmeritus at the University of California, Santa Barbara in theDepartment of Statistics and Applied Probability. He has publishedseven monographs, eight handbooks and special-edited volumes, andover 250 research articles. Professor Rachev is cofounder of BravoRisk Management Group specializing in financial risk-managementsoftware. Bravo Group was recently acquired by FinAnalytica forwhich he currently serves as Chief-Scientist.

Stefan Mittnik studied at the Technical UniversityBerlin, Germany, the University of Sussex, England, and atWashington University in St. Louis, where he received his doctoratedegree in economics. He is now Professor of Financial Econometricsat the University of Munich, Germany, and research director at theIfo Institute for Economic Research in Munich. Prior to joining theUniversity of Munich he taught at SUNYStony Brook, New York, theUniversity of Kiel, Germany, and held several visiting positions,including that of Fulbright Distinguished Chair at WashingtonUniversity in St. Louis. His research focuses on financialeconometrics, risk management, and portfolio optimization. Inaddition to purely academic interests, Professor Mittnik directsthe risk management program at the Center for Financial Studies inFrankfurt, Germany, and is co-founder of the Institut fürQuantitative Finanzanalyse (IQF) in Kiel, where he now chairs thescientific advisory board.

Frank J. Fabozzi is an Adjunct Professor of Finance andBecton Fellow in the School of Management at Yale University. Priorto joining the Yale faculty, he was a Visiting Professor of Financein the Sloan School at MIT. Professor Fabozzi is a Fellow of theInternational Center for Finance at Yale University and on theAdvisory Council for the Department of Operations Research andFinancial Engineering at Princeton University. He is the editor ofThe Journal of Portfolio Management and an associate editorof the The Journal of Fixed Income. He earned a doctorate ineconomics from the City University of New York in 1972. In 2002Professor Fabozzi was inducted into the Fixed Income AnalystsSociety’s Hall of Fame. He earned the designation ofChartered Financial Analyst and Certified Public Accountant. He hasauthored and edited numerous books in finance.

Sergio Focardi is a partner of The Intertek Group and amember of the Editorial Board of the Journal of PortfolioManagement. He is the (co-) author of numerous articles andbooks on financial modeling and risk management, including the CFAInstitute’s recent monograph Trends in QuantitativeFinance (co-authors Fabozzi and Kolm) and the award-winningbooks Financial Modeling of the Equity Market (co-authorsFabozzi and Kolm, Wiley) and The Mathematics of FinancialModeling and Investment Management (co-author Fabozzi, Wiley).Mr. Focardi has implemented long-short portfolio constructionapplications based on dynamic factor analysis and conducts researchin the econometrics of large equity portfolios and the modeling ofregime changes. He holds a degree in Electronic Engineering fromthe University of Genoa and a postgraduate degree in Communicationsfrom the Galileo Ferraris Electrotechnical Institute (Turin).

Teo Jasic earned his doctorate (Dr.rer.pol.) in economicsfrom the University of Karlsruhe in 2006. He also holds an MScdegree from the National University of Singapore and a Dipl.-Ing.degree from the University of Zagreb. Currently, he is aPostdoctoral Research Fellow at the Chair ofStatistics,Econometrics and Mathematical Finance at the University ofKarlsruhe in the School of Economics and Business Engineering. Heis also a senior manager in Financial & Risk Management Groupof a leading international management consultancy firm inFrankfurt, Germany. His current professional and research interestsare in the areas of asset management, risk management, andfinancial forecasting. Dr. Jasic has published more than a dozenresearch papers in internationally refereed journals.

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Table of Contents

Preface.

Abbreviations and Acronyms.

About the Authors.

CHAPTER 1: 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 ofpresentation).

CHAPTER 2: Review of Probability and Statistics.

Concepts of Probability.

Principles of Estimation.

Bayesian Modeling.

Appendix A: Information Structures.

Appendix B: Filtration.

Concepts Explained in this Chapter (in order ofpresentation).

CHAPTER 3: Regression Analysis: Theory andEstimation.

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).

CHAPTER 4: 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 ofpresentation).

CHAPTER 5: 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 JensenMeasure.

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 ofpresentation).

CHAPTER 6: 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 ofpresentation).

CHAPTER 7: Approaches to ARIMA Modeling andForecasting.

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 ofpresentation).

CHAPTER 8: Autoregressive Conditional HeteroskedasticModels.

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-DistributedInnovations.

Multivariate GARCH Formulations.

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

Concepts Explained in this Chapter (in order ofpresentation).

CHAPTER 9: 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 ofpresentation).

CHAPTER 10: 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 ofpresentation).

CHAPTER 11: Cointegration and State Space Models.

Cointegration.

Error Correction Models.

Theory and Methods of Estimation of Nonstationary VARModels.

State-Space Models.

Concepts Explained in this Chapter (in order ofpresentation).

CHAPTER 12: Robust Estimation.

Robust Statistics.

Robust Estimators of Regressions.

Illustration: Robustness of the Corporate Bond Yield SpreadModel.

Concepts Explained in this Chapter (in order ofpresentation).

CHAPTER 13: Principal Components Analysis and FactorAnalysis.

Factor Models.

Principal Components Analysis.

Factor Analysis.

PCA and Factor Analysis Compared.

Concepts Explained in this Chapter (in order ofpresentation).

CHAPTER 14: Heavy-Tailed and Stable Distributions inFinancial Econometrics.

Basic Facts and Definitions of Stable Distributions.

Properties of Stable Distributions.

Estimation of the Parameters of the Stable Distribution.

Applications to German Stock Data.

Appendix: Comparing Probability Distributions.

Concepts Explained in this Chapter (in order ofpresentation).

CHAPTER 15: ARMA and ARCH Models with Infinite-VarianceInnovations.

Infinite Variance Autoregressive Processes.

Stable GARCH Models.

Estimation for the Stable GARCH Model.

Prediction of Conditional Densities.

Concepts Explained in this Chapter (in order ofpresentation).

APPENDIX: Monthly Returns for 20 Stocks: December2000–November 2005.

INDEX.

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