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

Book Description

An accessible guide to the growing field of financial econometrics

As finance and financial products have become more complex, financial econometrics has emerged as a fast-growing field and necessary foundation for anyone involved in quantitative finance. The techniques of financial econometrics facilitate the development and management of new financial instruments by providing models for pricing and risk assessment. In short, financial econometrics is an indispensable component to modern finance.

The Basics of Financial Econometrics covers the commonly used techniques in the field without using unnecessary mathematical/statistical analysis. It focuses on foundational ideas and how they are applied. Topics covered include: regression models, factor analysis, volatility estimations, and time series techniques. In addition, an associated website contains a number of real-world case studies related to important issues in this area.

  • Covers the basics of financial econometrics—an important topic in quantitative finance

  • Contains several chapters on topics typically not covered even in basic books on econometrics such as model selection, model risk, and mitigating model risk

  • A companion website includes mini-cases that explain important topics in portfolio management, credit risk modeling, option pricing, and risk management

  • Geared towards both practitioners and finance students who need to understand this dynamic discipline, but may not have advanced mathematical training, this book is a valuable resource on a topic of growing importance.

    Note: The ebook version does not provide access to the companion files.

    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