The Basics of Financial Econometrics: Tools, Concepts, and Asset Management Applications

Table of contents

1. Cover Page
2. Title Page
3. Copyright
4. Dedication
5. Contents
6. Preface
7. Acknowledgments
8. About the Authors
9. CHAPTER 1: Introduction
   1. Financial Econometrics at Work
   2. The Data Generating Process
   3. Applications of Financial Econometrics to Investment Management
   4. Key Points
10. CHAPTER 2: Simple Linear Regression
    1. The Role of Correlation
    2. Regression Model: Linear Functional Relationship between Two Variables
    3. Distributional Assumptions of the Regression Model
    4. Estimating the Regression Model
    5. Goodness-of-Fit of the Model
    6. Two Applications in Finance
    7. Linear Regression of a Nonlinear Relationship
    8. Key Points
11. CHAPTER 3: Multiple Linear Regression
    1. The Multiple Linear Regression Model
    2. Assumptions of the Multiple Linear Regression Model
    3. Estimation of the Model Parameters
    4. Designing the Model
    5. Diagnostic Check and Model Significance
    6. Applications to Finance
    7. Key Points
12. CHAPTER 4: Building and Testing a Multiple Linear Regression Model
    1. The Problem of Multicollinearity
    2. Model Building Techniques
    3. Testing the Assumptions of the Multiple Linear Regression Model
    4. Key Points
13. CHAPTER 5: Introduction to Time Series Analysis
    1. What Is a Time Series?
    2. Decomposition of Time Series
    3. Representation of Time Series with Difference Equations
    4. Application: The Price Process
    5. Key Points
14. CHAPTER 6: Regression Models with Categorical Variables
    1. Independent Categorical Variables
    2. Dependent Categorical Variables
    3. Key Points
15. CHAPTER 7: Quantile Regressions
    1. Limitations of Classical Regression Analysis
    2. Parameter Estimation
    3. Quantile Regression Process
    4. Applications of Quantile Regressions in Finance
    5. Key Points
16. CHAPTER 8: Robust Regressions
    1. Robust Estimators of Regressions
    2. Illustration: Robustness of the Corporate Bond Yield Spread Model
    3. Robust Estimation of Covariance and Correlation Matrices
    4. Applications
    5. Key Points
17. CHAPTER 9: Autoregressive Moving Average Models
    1. Autoregressive Models
    2. Moving Average Models
    3. Autoregressive Moving Average Models
    4. ARMA Modeling to Forecast S&P 500 Weekly Index Returns
    5. Vector Autoregressive Models
    6. Key Points
18. CHAPTER 10: Cointegration
    1. Stationary and Nonstationary Variables and Cointegration
    2. Testing for Cointegration
    3. Key Points
19. CHAPTER 11: Autoregressive Heteroscedasticity Model and Its Variants
    1. Estimating and Forecasting Volatility
    2. ARCH Behavior
    3. GARCH Model
    4. What Do ARCH/GARCH Models Represent?
    5. Univariate Extensions of GARCH Modeling
    6. Estimates of ARCH/GARCH Models
    7. Application of GARCH Models to Option Pricing
    8. Multivariate Extensions of ARCH/GARCH Modeling
    9. Key Points
20. CHAPTER 12: Factor Analysis and Principal Components Analysis
    1. Assumptions of Linear Regression
    2. Basic Concepts of Factor Models
    3. Assumptions and Categorization of Factor Models
    4. Similarities and Differences between Factor Models and Linear Regression
    5. Properties of Factor Models
    6. Estimation of Factor Models
    7. Principal Components Analysis
    8. Differences between Factor Analysis and PCA
    9. Approximate (Large) Factor Models
    10. Approximate Factor Models and PCA
    11. Key Points
21. CHAPTER 13: Model Estimation
    1. Statistical Estimation and Testing
    2. Estimation Methods
    3. Least-Squares Estimation Method
    4. The Maximum Likelihood Estimation Method
    5. Instrumental Variables
    6. Method of Moments
    7. The M-Estimation Method and M-Estimators
    8. Key Points
22. CHAPTER 14: Model Selection
    1. Physics and Economics: Two Ways of Making Science
    2. Model Complexity and Sample Size
    3. Data Snooping
    4. Survivorship Biases and Other Sample Defects
    5. Model Risk
    6. Model Selection in a Nutshell
    7. Key Points
23. CHAPTER 15: Formulating and Implementing Investment Strategies Using Financial Econometrics
    1. The Quantitative Research Process
    2. Investment Strategy Process
    3. Key Points
24. APPENDIX A: Descriptive Statistics
    1. Basic Data Analysis
    2. Measures of Location and Spread
    3. Multivariate Variables and Distributions
25. APPENDIX B: Continuous Probability Distributions Commonly Used in Financial Econometrics
    1. Normal Distribution
    2. Chi-Square Distribution
    3. Student's t-Distribution
    4. F-Distribution
    5. α-Stable Distribution
26. APPENDIX C: Inferential Statistics
    1. Point Estimators
    2. Confidence Intervals
    3. Hypothesis Testing
27. APPENDIX D: Fundamentals of Matrix Algebra
    1. Vectors and Matrices Defined
    2. Square Matrices
    3. Determinants
    4. Systems of Linear Equations
    5. Linear Independence and Rank
    6. Vector and Matrix Operations
    7. Eigenvalues and Eigenvectors
28. APPENDIX E: Model Selection Criterion: AIC and BIC
    1. Akaike Information Criterion
    2. Bayesian Information Criterion
29. APPENDIX F: Robust Statistics
    1. Robust Statistics Defined
    2. Qualitative and Quantitative Robustness
    3. Resistant Estimators
    4. M-Estimators
    5. The Least Median of Squares Estimator
    6. The Least Trimmed of Squares Estimator
    7. Robust Estimators of the Center
    8. Robust Estimators of the Spread
    9. Illustration of Robust Statistics