The basics of financial econometrics : tools, concepts, and asset management applications / Frank J. Fabozzi, Sergio M. Focardi, Svetlozar T. Rachev, Bala G. Arshanapalli, with the assistance of Markus Hochstotter.
"An accessible guide to the growing field of financial econometrics ."--Provided by publisher.
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Full Text (via Wiley) |
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| Format: | eBook |
| Language: | English |
| Published: |
Hoboken, New Jersey :
John Wiley & Sons, Inc.,
[2014]
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| Series: | Frank J. Fabozzi series.
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Table of Contents:
- Simple Linear Regression
- Multiple Linear Regression
- Building and Testing a Multiple Linear Regression Model
- Introduction to Time Series Analysis
- Regression Models with Categorical Variables
- Quantile Regressions
- Robust Regressions
- Autoregressive Moving Average Models
- Cointegration
- Autoregressive Heteroscedasticity Model and Its Variants
- Factor Analysis and Principal Components Analysis
- Model Estimation
- Model Selection
- Formulating and Implementing Investment Strategies Using Financial Econometrics
- Appendix A: Descriptive Statistics
- Appendix B: Continuous Probability Distributions Commonly Used in Financial Econometrics
- Appendix C: Inferential Statistics
- Appendix D: Fundamentals of Matrix Algebra
- Appendix E: Model Selection Criterion: AIC and BIC
- Appendix F: Robust Statistics.
- Machine generated contents note: ch. 1 Introduction
- Financial Econometrics at Work
- Data Generating Process
- Applications of Financial Econometrics to Investment Management
- Key Points
- ch. 2 Simple Linear Regression
- Role of Correlation
- Regression Model: Linear Functional Relationship between Two Variables
- Distributional Assumptions of the Regression Model
- Estimating the Regression Model
- Goodness-of-Fit of the Model
- Two Applications in Finance
- Linear Regression of a Nonlinear Relationship
- Key Points
- ch. 3 Multiple Linear Regression
- Multiple Linear Regression Model
- Assumptions of the Multiple Linear Regression Model
- Estimation of the Model Parameters
- Designing the Model
- Diagnostic Check and Model Significance
- Applications to Finance
- Key Points
- ch. 4 Building and Testing a Multiple Linear Regression Model
- Problem of Multicollinearity
- Model Building Techniques
- Testing the Assumptions of the Multiple Linear Regression Model
- Key Points
- ch. 5 Introduction to Time Series Analysis
- What Is a Time Series-- Decomposition of Time Series
- Representation of Time Series with Difference Equations
- Application: The Price Process
- Key Points
- ch. 6 Regression Models with Categorical Variables
- Independent Categorical Variables
- Dependent Categorical Variables
- Key Points
- ch. 7 Quantile Regressions
- Limitations of Classical Regression Analysis
- Parameter Estimation
- Quantile Regression Process
- Applications of Quantile Regressions in Finance
- Key Points
- ch. 8 Robust Regressions
- Robust Estimators of Regressions
- Illustration: Robustness of the Corporate Bond Yield Spread Model
- Robust Estimation of Covariance and Correlation Matrices
- Applications
- Key Points
- ch. 8 Autoregressive Moving Average Models
- Autoregressive Models
- Moving Average Models
- Autoregressive Moving Average Models
- ARMA Modeling to Forecast S&P 500 Weekly Index Returns
- Vector Autoregressive Models
- Key Points
- ch. 10 Cointegration
- Stationary and Nonstationary Variables and Cointegration
- Testing for Cointegration
- Key Points
- ch. 11 Autoregressive Heteroscedasticity Model and Its Variants
- Estimating and Forecasting Volatility
- ARCH Behavior
- GARCH Model
- What Do ARCH/GARCH Models Represent-- Univariate Extensions of GARCH Modeling
- Estimates of ARCH/GARCH Models
- Application of GARCH Models to Option Pricing
- Multivariate Extensions of ARCH/GARCH Modeling
- Key Points
- ch. 12 Factor Analysis and Principal Components Analysis
- Assumptions of Linear Regression
- Basic Concepts of Factor Models
- Assumptions and Categorization of Factor Models
- Similarities and Differences between Factor Models and Linear Regression
- Properties of Factor Models
- Estimation of Factor Models
- Principal Components Analysis
- Differences between Factor Analysis and PCA
- Approximate (Large) Factor Models
- Approximate Factor Models and PCA
- Key Points
- ch. 13 Model Estimation
- Statistical Estimation and Testing
- Estimation Methods
- Least-Squares Estimation Method
- Maximum Likelihood Estimation Method
- Instrumental Variables
- Method of Moments
- M-Estimation Method and M-Estimators
- Key Points
- ch. 14 Model Selection
- Physics and Economics: Two Ways of Making Science
- Model Complexity and Sample Size
- Data Snooping
- Survivorship Biases and Other Sample Defects
- Model Risk
- Model Selection in a Nutshell
- Key Points
- ch. 15 Formulating and Implementing Investment Strategies Using Financial Econometrics
- Quantitative Research Process
- Investment Strategy Process
- Key Points
- Appendix A Descriptive Statistics
- Basic Data Analysis
- Measures of Location and Spread
- Multivariate Variables and Distributions
- Appendix B Continuous Probability Distributions Commonly Used in Financial Econometrics
- Normal Distribution
- Chi-Square Distribution
- Student's t-Distribution
- F -Distribution
- α-Stable Distribution
- Appendix C Inferential Statistics
- Point Estimators
- Confidence Intervals
- Hypothesis Testing
- Appendix D Fundamentals of Matrix Algebra
- Vectors and Matrices Defined
- Square Matrices
- Determinants
- Systems of Linear Equations
- Linear Independence and Rank
- Vector and Matrix Operations
- Eigenvalues and Eigenvectors
- Appendix E Model Selection Criterion: AIC and BIC
- Akaike Information Criterion
- Bayesian Information Criterion
- Appendix F Robust Statistics
- Robust Statistics Defined
- Qualitative and Quantitative Robustness
- Resistant Estimators
- M-Estimators
- Least Median of Squares Estimator
- Least Trimmed of Squares Estimator
- Robust Estimators of the Center
- Robust Estimators of the Spread
- Illustration of Robust Statistics.