{"product_id":"the-basics-of-financial-econometrics-isbn-9781118573204","title":"The Basics of Financial Econometrics","description":"\u003cb\u003eAn accessible guide to the growing field of financial econometrics\u003c\/b\u003e \u003cp\u003eAs 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.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eThe Basics of Financial Econometrics \u003c\/i\u003ecovers 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. \u003c\/p\u003e \u003cul\u003e \u003cli\u003eCovers the basics of financial econometrics—an important topic in quantitative finance\u003c\/li\u003e \u003cli\u003eContains several chapters on topics typically not covered even in basic books on econometrics such as model selection, model risk, and mitigating model risk\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eGeared 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.\u003c\/p\u003e  Preface xiii  \u003cp\u003eAcknowledgments xvii\u003c\/p\u003e \u003cp\u003eAbout the Authors xix\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1 Introduction 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eFinancial Econometrics at Work 2\u003c\/p\u003e \u003cp\u003eThe Data Generating Process 5\u003c\/p\u003e \u003cp\u003eApplications of Financial Econometrics to Investment Management 6\u003c\/p\u003e \u003cp\u003eKey Points 10\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2 Simple Linear Regression 13\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Role of Correlation 13\u003c\/p\u003e \u003cp\u003eRegression Model: Linear Functional Relationship between Two Variables 14\u003c\/p\u003e \u003cp\u003eDistributional Assumptions of the Regression Model 16\u003c\/p\u003e \u003cp\u003eEstimating the Regression Model 18\u003c\/p\u003e \u003cp\u003eGoodness-of-Fit of the Model 22\u003c\/p\u003e \u003cp\u003eTwo Applications in Finance 25\u003c\/p\u003e \u003cp\u003eLinear Regression of a Nonlinear Relationship 36\u003c\/p\u003e \u003cp\u003eKey Points 38\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 3 Multiple Linear Regression 41\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Multiple Linear Regression Model 42\u003c\/p\u003e \u003cp\u003eAssumptions of the Multiple Linear Regression Model 43\u003c\/p\u003e \u003cp\u003eEstimation of the Model Parameters 43\u003c\/p\u003e \u003cp\u003eDesigning the Model 45\u003c\/p\u003e \u003cp\u003eDiagnostic Check and Model Significance 46\u003c\/p\u003e \u003cp\u003eApplications to Finance 51\u003c\/p\u003e \u003cp\u003eKey Points 79\u003c\/p\u003e \u003cp\u003e\u003cb\u003echapter 4 Building and Testing a Multiple Linear Regression Model 81\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Problem of Multicollinearity 81\u003c\/p\u003e \u003cp\u003eModel Building Techniques 84\u003c\/p\u003e \u003cp\u003eTesting the Assumptions of the Multiple Linear Regression Model 88\u003c\/p\u003e \u003cp\u003eKey Points 100\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 5 Introduction to Time Series Analysis 103\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWhat Is a Time Series? 103\u003c\/p\u003e \u003cp\u003eDecomposition of Time Series 104\u003c\/p\u003e \u003cp\u003eRepresentation of Time Series with Difference Equations 108\u003c\/p\u003e \u003cp\u003eApplication: The Price Process 109\u003c\/p\u003e \u003cp\u003eKey Points 113\u003c\/p\u003e \u003cp\u003e\u003cb\u003echapter 6 Regression Models with Categorical Variables 115\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIndependent Categorical Variables 116\u003c\/p\u003e \u003cp\u003eDependent Categorical Variables 137\u003c\/p\u003e \u003cp\u003eKey Points 140\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7 Quantile Regressions 143\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eLimitations of Classical Regression Analysis 144\u003c\/p\u003e \u003cp\u003eParameter Estimation 144\u003c\/p\u003e \u003cp\u003eQuantile Regression Process 146\u003c\/p\u003e \u003cp\u003eApplications of Quantile Regressions in Finance 148\u003c\/p\u003e \u003cp\u003eKey Points 155\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 8 Robust Regressions 157\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eRobust Estimators of Regressions 158\u003c\/p\u003e \u003cp\u003eIllustration: Robustness of the\u003c\/p\u003e \u003cp\u003eCorporate Bond Yield Spread Model 161\u003c\/p\u003e \u003cp\u003eRobust Estimation of Covariance and Correlation Matrices 166\u003c\/p\u003e \u003cp\u003eApplications 168\u003c\/p\u003e \u003cp\u003eKey Points 170\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9 Autoregressive Moving Average Models 171\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eAutoregressive Models 172\u003c\/p\u003e \u003cp\u003eMoving Average Models 176\u003c\/p\u003e \u003cp\u003eAutoregressive Moving Average Models 178\u003c\/p\u003e \u003cp\u003eARMA Modeling to Forecast S\u0026amp;P 500 Weekly Index Returns 181\u003c\/p\u003e \u003cp\u003eVector Autoregressive Models 188\u003c\/p\u003e \u003cp\u003eKey Points 189\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 10 Cointegration 191\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eStationary and Nonstationary Variables and Cointegration 192\u003c\/p\u003e \u003cp\u003eTesting for Cointegration 196\u003c\/p\u003e \u003cp\u003eKey Points 211\u003c\/p\u003e \u003cp\u003e\u003cb\u003echapter 11 Autoregressive Heteroscedasticity Model and Its Variants 213\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eEstimating and Forecasting Volatility 214\u003c\/p\u003e \u003cp\u003eARCH Behavior 215\u003c\/p\u003e \u003cp\u003eGARCH Model 223\u003c\/p\u003e \u003cp\u003eWhat Do ARCH\/GARCH Models Represent? 226\u003c\/p\u003e \u003cp\u003eUnivariate Extensions of GARCH Modeling 226\u003c\/p\u003e \u003cp\u003eEstimates of ARCH\/GARCH Models 229\u003c\/p\u003e \u003cp\u003eApplication of GARCH Models to Option Pricing 230\u003c\/p\u003e \u003cp\u003eMultivariate Extensions of ARCH\/GARCH Modeling 231\u003c\/p\u003e \u003cp\u003eKey Points 233\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 12 Factor Analysis and Principal Components Analysis 235\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eAssumptions of Linear Regression 236\u003c\/p\u003e \u003cp\u003eBasic Concepts of Factor Models 237\u003c\/p\u003e \u003cp\u003eAssumptions and Categorization of Factor Models 240\u003c\/p\u003e \u003cp\u003eSimilarities and Differences between Factor Models and Linear Regression 241\u003c\/p\u003e \u003cp\u003eProperties of Factor Models 242\u003c\/p\u003e \u003cp\u003eEstimation of Factor Models 244\u003c\/p\u003e \u003cp\u003ePrincipal Components Analysis 251\u003c\/p\u003e \u003cp\u003eDifferences between Factor Analysis and PCA 259\u003c\/p\u003e \u003cp\u003eApproximate (Large) Factor Models 261\u003c\/p\u003e \u003cp\u003eApproximate Factor Models and PCA 263\u003c\/p\u003e \u003cp\u003eKey Points 264\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 13 Model Estimation 265\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eStatistical Estimation and Testing 265\u003c\/p\u003e \u003cp\u003eEstimation Methods 267\u003c\/p\u003e \u003cp\u003eLeast-Squares Estimation Method 268\u003c\/p\u003e \u003cp\u003eThe Maximum Likelihood Estimation Method 278\u003c\/p\u003e \u003cp\u003eInstrumental Variables 283\u003c\/p\u003e \u003cp\u003eMethod of Moments 284\u003c\/p\u003e \u003cp\u003eThe M-Estimation Method and M-Estimators 289\u003c\/p\u003e \u003cp\u003eKey Points 289\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 14 Model Selection 291\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003ePhysics and Economics: Two Ways of Making Science 291\u003c\/p\u003e \u003cp\u003eModel Complexity and Sample Size 293\u003c\/p\u003e \u003cp\u003eData Snooping 296\u003c\/p\u003e \u003cp\u003eSurvivorship Biases and Other Sample Defects 297\u003c\/p\u003e \u003cp\u003eModel Risk 300\u003c\/p\u003e \u003cp\u003eModel Selection in a Nutshell 301\u003c\/p\u003e \u003cp\u003eKey Points 303\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 15 Formulating and Implementing Investment Strategies Using Financial Econometrics 305\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Quantitative Research Process 307\u003c\/p\u003e \u003cp\u003eInvestment Strategy Process 314\u003c\/p\u003e \u003cp\u003eKey Points 318\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix A Descriptive Statistics 321\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eBasic Data Analysis 321\u003c\/p\u003e \u003cp\u003eMeasures of Location and Spread 328\u003c\/p\u003e \u003cp\u003eMultivariate Variables and Distributions 332\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix B Continuous Probability Distributions Commonly Used in Financial Econometrics 343\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eNormal Distribution 344\u003c\/p\u003e \u003cp\u003eChi-Square Distribution 347\u003c\/p\u003e \u003cp\u003eStudent’s t-Distribution 349\u003c\/p\u003e \u003cp\u003eF-Distribution 352\u003c\/p\u003e \u003cp\u003eα-Stable Distribution 353\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix C Inferential Statistics 359\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003ePoint Estimators 359\u003c\/p\u003e \u003cp\u003eConfidence Intervals 369\u003c\/p\u003e \u003cp\u003eHypothesis Testing 372\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix D Fundamentals of Matrix Algebra 385\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eVectors and Matrices Defined 385\u003c\/p\u003e \u003cp\u003eSquare Matrices 387\u003c\/p\u003e \u003cp\u003eDeterminants 388\u003c\/p\u003e \u003cp\u003eSystems of Linear Equations 389\u003c\/p\u003e \u003cp\u003eLinear Independence and Rank 391\u003c\/p\u003e \u003cp\u003eVector and Matrix Operations 391\u003c\/p\u003e \u003cp\u003eEigenvalues and Eigenvectors 396\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAPPENDIX E Model Selection Criterion: AIC and BIC 399\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eAkaike Information Criterion 400\u003c\/p\u003e \u003cp\u003eBayesian Information Criterion 402\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix F Robust Statistics 405\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eRobust Statistics Defined 405\u003c\/p\u003e \u003cp\u003eQualitative and Quantitative Robustness 406\u003c\/p\u003e \u003cp\u003eResistant Estimators 406\u003c\/p\u003e \u003cp\u003eM-Estimators 408\u003c\/p\u003e \u003cp\u003eThe Least Median of Squares Estimator 408\u003c\/p\u003e \u003cp\u003eThe Least Trimmed of Squares Estimator 409\u003c\/p\u003e \u003cp\u003eRobust Estimators of the Center 409\u003c\/p\u003e \u003cp\u003eRobust Estimators of the Spread 410\u003c\/p\u003e \u003cp\u003eIllustration of Robust Statistics 410\u003c\/p\u003e \u003cp\u003eIndex 413\u003c\/p\u003e   \u003cp\u003e\u003cb\u003eFRANK J. FABOZZI\u003c\/b\u003e is Professor of Finance at EDHEC Business School and Editor of the \u003ci\u003eJournal of Portfolio Management\u003c\/i\u003e.  \u003c\/p\u003e\u003cp\u003e\u003cb\u003eSERGIO M. FOCARDI\u003c\/b\u003e is Visiting Professor of Finance at Stony Brook University and a founding partner of the Paris-based consulting firm The Intertek Group.  \u003c\/p\u003e\u003cp\u003e\u003cb\u003eSVETLOZAR T. RACHEV\u003c\/b\u003e is Professor of Finance, College of Business and Center for Finance, Stony Brook University, and Chief-Scientist with FinAnalytica.  \u003c\/p\u003e\u003cp\u003e\u003cb\u003eBALA G. ARSHANAPALLI\u003c\/b\u003e is the Gallagher-Mills Chair of Business and Economics at Indiana University Northwest.     \u003c\/p\u003e\u003cp\u003e\u003cb\u003e\u003ci\u003eThe\u003c\/i\u003e BASICS \u003ci\u003eof\u003c\/i\u003e FINANCIAL ECONOMETRICS\u003c\/b\u003e  \u003c\/p\u003e\u003cp\u003eWith 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. \u003ci\u003eThe Basics of Financial Econometrics\u003c\/i\u003e 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.  \u003c\/p\u003e\u003cp\u003eFinancial econometrics is an indispensable component to modern finance and a crucial body of knowledge for financial professionals. \u003ci\u003eThe Basics of Financial Econometrics\u003c\/i\u003e addresses the key relationship between econometrics and quantitative finance, and provides practical examples that use real-world financial data. Areas covered include:  \u003c\/p\u003e\u003cul\u003e \u003cli\u003eBuilding financial models\u003c\/li\u003e \u003cli\u003eAsset pricing\u003c\/li\u003e \u003cli\u003eDerivative pricing\u003c\/li\u003e \u003cli\u003ePortfolio allocation\u003c\/li\u003e \u003cli\u003eHedging strategies\u003c\/li\u003e \u003cli\u003eModel selection\u003c\/li\u003e \u003cli\u003eStrategy development\u003c\/li\u003e \u003c\/ul\u003e  \u003cp\u003eWritten for both seasoned financial professionals and advanced students of finance, \u003ci\u003eThe Basics of Financial Econometrics\u003c\/i\u003e provides a complete, real-world overview that provides a strong foundation in financial econometrics.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47990165438693,"sku":"NP9781118573204","price":135.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781118573204.jpg?v=1761786754","url":"https:\/\/k12savings.com\/es\/products\/the-basics-of-financial-econometrics-isbn-9781118573204","provider":"K12savings","version":"1.0","type":"link"}