Financial econometrics : from basics to advanced modeling techniques Rachev, S. T.(著/文) - John Wiley & Sons
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Financial econometrics : from basics to advanced modeling techniques
- 初版年月日
- c2007-01
- 登録日
- 2017年5月19日
- 最終更新日
- 2017年5月19日
紹介
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.
目次
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 of presentation). 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 of presentation). CHAPTER 3: 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) . 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 of presentation). 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 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). 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 of presentation). CHAPTER 7: 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). CHAPTER 8: 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). 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 of presentation). 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 of presentation). CHAPTER 11: Cointegration and State Space Models. Cointegration. Error Correction Models. Theory and Methods of Estimation of Nonstationary VAR Models. State-Space Models. Concepts Explained in this Chapter (in order of presentation). CHAPTER 12: Robust Estimation. Robust Statistics. Robust Estimators of Regressions. Illustration: Robustness of the Corporate Bond Yield Spread Model. Concepts Explained in this Chapter (in order of presentation). CHAPTER 13: Principal Components Analysis and Factor Analysis. Factor Models. Principal Components Analysis. Factor Analysis. PCA and Factor Analysis Compared. Concepts Explained in this Chapter (in order of presentation). CHAPTER 14: Heavy-Tailed and Stable Distributions in Financial 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 of presentation). CHAPTER 15: ARMA and ARCH Models with Infinite-Variance Innovations. Infinite Variance Autoregressive Processes. Stable GARCH Models. Estimation for the Stable GARCH Model. Prediction of Conditional Densities. Concepts Explained in this Chapter (in order of presentation). APPENDIX: Monthly Returns for 20 Stocks: December 2000-November 2005. INDEX.
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