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 BASICS of FINANCIAL ECONOMETRICS With the growth in quantitative finance, financial econometrics has emerged as a vitally important field, providing the analytical models to address complex financial product structures, valuation, and risk assessment. The Basics of Financial Econometrics covers the commonly used techniques in the field…mehr
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 BASICS of FINANCIAL ECONOMETRICS With the growth in quantitative finance, financial econometrics has emerged as a vitally important field, providing the analytical models to address complex financial product structures, valuation, and risk assessment. The Basics of Financial Econometrics covers the commonly used techniques in the field without using unnecessary mathematical or statistical proofs and derivations, and with a clear emphasis on basic ideas and how to apply them. Financial econometrics is an indispensable component to modern finance and a crucial body of knowledge for financial professionals. The Basics of Financial Econometrics addresses the key relationship between econometrics and quantitative finance, and provides practical examples that use real-world financial data. Areas covered include: * Building financial models * Asset pricing * Derivative pricing * Portfolio allocation * Hedging strategies * Model selection * Strategy development Written for both seasoned financial professionals and advanced students of finance, The Basics of Financial Econometrics provides a complete, real-world overview that provides a strong foundation in financial econometrics.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
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.
Inhaltsangabe
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 ¿-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
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 ¿-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
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