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9780471784500

Financial Econometrics : From Basics to Advanced Modeling Techniques

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  • ISBN13:

    9780471784500

  • ISBN10:

    0471784508

  • Edition: 1st
  • Format: Hardcover
  • Copyright: 2013-08-12
  • Publisher: Wiley

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Supplemental Materials

What is included with this book?

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

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