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The Basics of Financial Econometrics

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

  • Author:
  • Publisher: John Wiley & Sons
  • ISBN: 9781118573204
  • Published In: March 2014
  • Format: Hardback , 448 pages
  • Jurisdiction: International ? Disclaimer:
    Countri(es) stated herein are used as reference only

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    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.

  • Preface xiii

    Acknowledgments xvii

    About the Authors xix

    Chapter 1 Introduction 1

    Financial Econometrics at Work 2

    The Data Generating Process 5

    Applications of Financial Econometrics to Investment Management 6

    Key Points 10

    Chapter 2 Simple Linear Regression 13

    The Role of Correlation 13

    Regression Model: Linear Functional Relationship between Two Variables 14

    Distributional Assumptions of the Regression Model 16

    Estimating the Regression Model 18

    Goodness-of-Fit of the Model 22

    Two Applications in Finance 25

    Linear Regression of a Nonlinear Relationship 36

    Key Points 38

    CHAPTER 3 Multiple Linear Regression 41

    The Multiple Linear Regression Model 42

    Assumptions of the Multiple Linear Regression Model 43

    Estimation of the Model Parameters 43

    Designing the Model 45

    Diagnostic Check and Model Significance 46

    Applications to Finance 51

    Key Points 79

    chapter 4 Building and Testing a Multiple Linear Regression Model 81

    The Problem of Multicollinearity 81

    Model Building Techniques 84

    Testing the Assumptions of the Multiple Linear Regression Model 88

    Key Points 100

    CHAPTER 5 Introduction to Time Series Analysis 103

    What Is a Time Series? 103

    Decomposition of Time Series 104

    Representation of Time Series with Difference Equations 108

    Application: The Price Process 109

    Key Points 113

    chapter 6 Regression Models with Categorical Variables 115

    Independent Categorical Variables 116

    Dependent Categorical Variables 137

    Key Points 140

    Chapter 7 Quantile Regressions 143

    Limitations of Classical Regression Analysis 144

    Parameter Estimation 144

    Quantile Regression Process 146

    Applications of Quantile Regressions in Finance 148

    Key Points 155

    CHAPTER 8 Robust Regressions 157

    Robust Estimators of Regressions 158

    Illustration: Robustness of the

    Corporate Bond Yield Spread Model 161

    Robust Estimation of Covariance and Correlation Matrices 166

    Applications 168

    Key Points 170

    Chapter 9 Autoregressive Moving Average Models 171

    Autoregressive Models 172

    Moving Average Models 176

    Autoregressive Moving Average Models 178

    ARMA Modeling to Forecast S&P 500 Weekly Index Returns 181

    Vector Autoregressive Models 188

    Key Points 189

    Chapter 10 Cointegration 191

    Stationary and Nonstationary Variables and Cointegration 192

    Testing for Cointegration 196

    Key Points 211

    chapter 11 Autoregressive Heteroscedasticity Model and Its Variants 213

    Estimating and Forecasting Volatility 214

    ARCH Behavior 215

    GARCH Model 223

    What Do ARCH/GARCH Models Represent? 226

    Univariate Extensions of GARCH Modeling 226

    Estimates of ARCH/GARCH Models 229

    Application of GARCH Models to Option Pricing 230

    Multivariate Extensions of ARCH/GARCH Modeling 231

    Key Points 233

    Chapter 12 Factor Analysis and Principal Components Analysis 235

    Assumptions of Linear Regression 236

    Basic Concepts of Factor Models 237

    Assumptions and Categorization of Factor Models 240

    Similarities and Differences between Factor Models and Linear Regression 241

    Properties of Factor Models 242

    Estimation of Factor Models 244

    Principal Components Analysis 251

    Differences between Factor Analysis and PCA 259

    Approximate (Large) Factor Models 261

    Approximate Factor Models and PCA 263

    Key Points 264

    Chapter 13 Model Estimation 265

    Statistical Estimation and Testing 265

    Estimation Methods 267

    Least-Squares Estimation Method 268

    The Maximum Likelihood Estimation Method 278

    Instrumental Variables 283

    Method of Moments 284

    The M-Estimation Method and M-Estimators 289

    Key Points 289

    CHAPTER 14 Model Selection 291

    Physics and Economics: Two Ways of Making Science 291

    Model Complexity and Sample Size 293

    Data Snooping 296

    Survivorship Biases and Other Sample Defects 297

    Model Risk 300

    Model Selection in a Nutshell 301

    Key Points 303

    Chapter 15 Formulating and Implementing Investment Strategies Using Financial Econometrics 305

    The Quantitative Research Process 307

    Investment Strategy Process 314

    Key Points 318

    Appendix A Descriptive Statistics 321

    Basic Data Analysis 321

    Measures of Location and Spread 328

    Multivariate Variables and Distributions 332

    Appendix B Continuous Probability Distributions Commonly Used in Financial Econometrics 343

    Normal Distribution 344

    Chi-Square Distribution 347

    Student’s t-Distribution 349

    F-Distribution 352

    α-Stable Distribution 353

    Appendix C Inferential Statistics 359

    Point Estimators 359

    Confidence Intervals 369

    Hypothesis Testing 372

    Appendix D Fundamentals of Matrix Algebra 385

    Vectors and Matrices Defined 385

    Square Matrices 387

    Determinants 388

    Systems of Linear Equations 389

    Linear Independence and Rank 391

    Vector and Matrix Operations 391

    Eigenvalues and Eigenvectors 396

    APPENDIX E Model Selection Criterion: AIC and BIC 399

    Akaike Information Criterion 400

    Bayesian Information Criterion 402

    Appendix F Robust Statistics 405

    Robust Statistics Defined 405

    Qualitative and Quantitative Robustness 406

    Resistant Estimators 406

    M-Estimators 408

    The Least Median of Squares Estimator 408

    The Least Trimmed of Squares Estimator 409

    Robust Estimators of the Center 409

    Robust Estimators of the Spread 410

    Illustration of Robust Statistics 410

    Index 413

  • FRANK J. FABOZZI is Professor of Finance at EDHEC Business School and Editor of the Journal of Portfolio Management.

    SERGIO M. FOCARDI is Visiting Professor of Finance at Stony Brook University and a founding partner of the Paris-based consulting firm The Intertek Group.

    SVETLOZAR T. RACHEV is Professor of Finance, College of Business and Center for Finance, Stony Brook University, and Chief-Scientist with FinAnalytica.

    BALA G. ARSHANAPALLI is the Gallagher-Mills Chair of Business and Economics at Indiana University Northwest.

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