{"product_id":"financial-econometrics-isbn-9780471784500","title":"Financial Econometrics","description":"A comprehensive guide to financial econometrics  \u003cp\u003eFinancial econometrics is a quest for models that describe financial time series such as prices, returns, interest rates, and exchange rates. In Financial Econometrics, readers will be introduced to this growing discipline and the concepts and theories associated with it, including background material on probability theory and statistics. The experienced author team uses real-world data where possible and brings in the results of published research provided by investment banking firms and journals. Financial Econometrics clearly explains the techniques presented and provides illustrative examples for the topics discussed.\u003c\/p\u003e \u003cp\u003eSvetlozar T. Rachev, PhD (Karlsruhe, Germany) is currently Chair-Professor at the University of Karlsruhe. Stefan Mittnik, PhD (Munich, Germany) is Professor of Financial Econometrics at the University of Munich. Frank J. Fabozzi, PhD, CFA, CFP (New Hope, PA) is an adjunct professor of Finance at Yale University’s School of Management. Sergio M. Focardi (Paris, France) is a founding partner of the Paris-based consulting firm The Intertek Group. Teo Jasic, PhD, (Frankfurt, Germany) is a senior manager with a leading international management consultancy firm in Frankfurt.\u003c\/p\u003e  \u003cb\u003ePreface.\u003c\/b\u003e  \u003cp\u003e\u003cb\u003eAbbreviations and Acronyms.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAbout the Authors.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 1: Financial Econometrics: Scope and Methods.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Data Generating Process.\u003c\/p\u003e \u003cp\u003eFinancial Econometrics at Work.\u003c\/p\u003e \u003cp\u003eTime Horizon of Models.\u003c\/p\u003e \u003cp\u003eApplications.\u003c\/p\u003e \u003cp\u003eAppendix: Investment Management Process.\u003c\/p\u003e \u003cp\u003eConcepts Explained in this Chapter (in order of presentation).\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 2: Review of Probability and Statistics.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eConcepts of Probability.\u003c\/p\u003e \u003cp\u003ePrinciples of Estimation.\u003c\/p\u003e \u003cp\u003eBayesian Modeling.\u003c\/p\u003e \u003cp\u003eAppendix A: Information Structures.\u003c\/p\u003e \u003cp\u003eAppendix B: Filtration.\u003c\/p\u003e \u003cp\u003eConcepts Explained in this Chapter (in order of presentation).\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 3: Regression Analysis: Theory and Estimation.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Concept of Dependence.\u003c\/p\u003e \u003cp\u003eRegressions and Linear Models.\u003c\/p\u003e \u003cp\u003eEstimation of Linear Regressions.\u003c\/p\u003e \u003cp\u003eSampling Distributions of Regressions.\u003c\/p\u003e \u003cp\u003eDetermining the Explanatory Power of a Regression.\u003c\/p\u003e \u003cp\u003eUsing Regression Analysis in Finance.\u003c\/p\u003e \u003cp\u003eStepwise Regression.\u003c\/p\u003e \u003cp\u003eNonnormality and Autocorrelation of the Residuals.\u003c\/p\u003e \u003cp\u003ePitfalls of Regressions.\u003c\/p\u003e \u003cp\u003eConcepts Explained in this Chapter (in order of presentation) .\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 4: Selected Topics in Regression Analysis.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eCategorical and Dummy Variables in Regression Models.\u003c\/p\u003e \u003cp\u003eConstrained Least Squares.\u003c\/p\u003e \u003cp\u003eThe Method of Moments and its Generalizations.\u003c\/p\u003e \u003cp\u003eConcepts Explained in this Chapter (in order of presentation).\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 5: Regression Applications in Finance.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eApplications to the Investment Management Process.\u003c\/p\u003e \u003cp\u003eA Test of Strong-Form Pricing Efficiency.\u003c\/p\u003e \u003cp\u003eTests of the CAPM.\u003c\/p\u003e \u003cp\u003eUsing the CAPM to Evaluate Manager Performance: The Jensen Measure.\u003c\/p\u003e \u003cp\u003eEvidence for Multifactor Models.\u003c\/p\u003e \u003cp\u003eBenchmark Selection: Sharpe Benchmarks.\u003c\/p\u003e \u003cp\u003eReturn-Based Style Analysis for Hedge Funds.\u003c\/p\u003e \u003cp\u003eHedge Fund Survival.\u003c\/p\u003e \u003cp\u003eBond Portfolio Applications.\u003c\/p\u003e \u003cp\u003eConcepts Explained in this Chapter (in order of presentation).\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 6: Modeling Univariate Time Series.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDifference Equations.\u003c\/p\u003e \u003cp\u003eTerminology and Definitions.\u003c\/p\u003e \u003cp\u003eStationarity and Invertibility of ARMA Processes.\u003c\/p\u003e \u003cp\u003eLinear Processes.\u003c\/p\u003e \u003cp\u003eIdentification Tools.\u003c\/p\u003e \u003cp\u003eConcepts Explained in this Chapter (in order of presentation).\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 7: Approaches to ARIMA Modeling and Forecasting.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eOverview of Box-Jenkins Procedure.\u003c\/p\u003e \u003cp\u003eIdentification of Degree of Differencing.\u003c\/p\u003e \u003cp\u003eIdentification of Lag Orders.\u003c\/p\u003e \u003cp\u003eModel Estimation.\u003c\/p\u003e \u003cp\u003eDiagnostic Checking.\u003c\/p\u003e \u003cp\u003eForecasting.\u003c\/p\u003e \u003cp\u003eConcepts Explained in this Chapter (in order of presentation).\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 8: Autoregressive Conditional Heteroskedastic Models.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eARCH Process.\u003c\/p\u003e \u003cp\u003eGARCH Process.\u003c\/p\u003e \u003cp\u003eEstimation of the GARCH Models.\u003c\/p\u003e \u003cp\u003eStationary ARMA-GARCH Models.\u003c\/p\u003e \u003cp\u003eLagrange Multiplier Test.\u003c\/p\u003e \u003cp\u003eVariants of the GARCH Model.\u003c\/p\u003e \u003cp\u003eGARCH Model with Student’s \u003ci\u003et\u003c\/i\u003e-Distributed Innovations.\u003c\/p\u003e \u003cp\u003eMultivariate GARCH Formulations.\u003c\/p\u003e \u003cp\u003eAppendix: Analysis of the Properties of the GARCH(1,1) Model.\u003c\/p\u003e \u003cp\u003eConcepts Explained in this Chapter (in order of presentation).\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 9: Vector Autoregressive Models I.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eVAR Models Defined.\u003c\/p\u003e \u003cp\u003eStationary Autoregressive Distributed Lag Models.\u003c\/p\u003e \u003cp\u003eVector Autoregressive Moving Average Models.\u003c\/p\u003e \u003cp\u003eForecasting with VAR Models.\u003c\/p\u003e \u003cp\u003eAppendix: Eigenvectors and Eigenvalues.\u003c\/p\u003e \u003cp\u003eConcepts Explained in this Chapter (in order of presentation).\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 10: Vector Autoregressive Models II.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eEstimation of Stable VAR Models.\u003c\/p\u003e \u003cp\u003eEstimating the Number of Lags.\u003c\/p\u003e \u003cp\u003eAutocorrelation and Distributional Properties of Residuals.\u003c\/p\u003e \u003cp\u003eVAR Illustration.\u003c\/p\u003e \u003cp\u003eConcepts Explained in this Chapter (in order of presentation).\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 11: Cointegration and State Space Models.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eCointegration.\u003c\/p\u003e \u003cp\u003eError Correction Models.\u003c\/p\u003e \u003cp\u003eTheory and Methods of Estimation of Nonstationary VAR Models.\u003c\/p\u003e \u003cp\u003eState-Space Models.\u003c\/p\u003e \u003cp\u003eConcepts Explained in this Chapter (in order of presentation).\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 12: Robust Estimation.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eRobust Statistics.\u003c\/p\u003e \u003cp\u003eRobust Estimators of Regressions.\u003c\/p\u003e \u003cp\u003eIllustration: Robustness of the Corporate Bond Yield Spread Model.\u003c\/p\u003e \u003cp\u003eConcepts Explained in this Chapter (in order of presentation).\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 13: Principal Components Analysis and Factor Analysis.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eFactor Models.\u003c\/p\u003e \u003cp\u003ePrincipal Components Analysis.\u003c\/p\u003e \u003cp\u003eFactor Analysis.\u003c\/p\u003e \u003cp\u003ePCA and Factor Analysis Compared.\u003c\/p\u003e \u003cp\u003eConcepts Explained in this Chapter (in order of presentation).\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 14: Heavy-Tailed and Stable Distributions in Financial Econometrics.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eBasic Facts and Definitions of Stable Distributions.\u003c\/p\u003e \u003cp\u003eProperties of Stable Distributions.\u003c\/p\u003e \u003cp\u003eEstimation of the Parameters of the Stable Distribution.\u003c\/p\u003e \u003cp\u003eApplications to German Stock Data.\u003c\/p\u003e \u003cp\u003eAppendix: Comparing Probability Distributions.\u003c\/p\u003e \u003cp\u003eConcepts Explained in this Chapter (in order of presentation).\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 15: ARMA and ARCH Models with Infinite-Variance Innovations.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eInfinite Variance Autoregressive Processes.\u003c\/p\u003e \u003cp\u003eStable GARCH Models.\u003c\/p\u003e \u003cp\u003eEstimation for the Stable GARCH Model.\u003c\/p\u003e \u003cp\u003ePrediction of Conditional Densities.\u003c\/p\u003e \u003cp\u003eConcepts Explained in this Chapter (in order of presentation).\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAPPENDIX:\u003c\/b\u003e Monthly Returns for 20 Stocks: December 2000–November 2005.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eINDEX.\u003c\/b\u003e\u003c\/p\u003e  \u003cb\u003eSvetlozar (Zari) T. Rachev\u003c\/b\u003e completed his Ph.D. Degree in 1979 from Moscow State (Lomonosov) University, and his Doctor of Science Degree in 1986 from Steklov Mathematical Institute in Moscow. Currently he is Chair-Professor in Statistics, Econometrics and Mathematical Finance at the University of Karlsruhe in the School of Economics and Business Engineering. He is also Professor Emeritus at the University of California, Santa Barbara in the Department of Statistics and Applied Probability. He has published seven monographs, eight handbooks and special-edited volumes, and over 250 research articles. Professor Rachev is cofounder of Bravo Risk Management Group specializing in financial risk-management software. Bravo Group was recently acquired by FinAnalytica for which he currently serves as Chief-Scientist.  \u003cp\u003e\u003cb\u003eStefan Mittnik\u003c\/b\u003e studied at the Technical University Berlin, Germany, the University of Sussex, England, and at Washington University in St. Louis, where he received his doctorate degree in economics. He is now Professor of Financial Econometrics at the University of Munich, Germany, and research director at the Ifo Institute for Economic Research in Munich. Prior to joining the University of Munich he taught at SUNYStony Brook, New York, the University of Kiel, Germany, and held several visiting positions, including that of Fulbright Distinguished Chair at Washington University in St. Louis. His research focuses on financial econometrics, risk management, and portfolio optimization. In addition to purely academic interests, Professor Mittnik directs the risk management program at the Center for Financial Studies in Frankfurt, Germany, and is co-founder of the Institut für Quantitative Finanzanalyse (IQF) in Kiel, where he now chairs the scientific advisory board.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eFrank J. Fabozzi\u003c\/b\u003e is an Adjunct Professor of Finance and Becton Fellow in the School of Management at Yale University. Prior to joining the Yale faculty, he was a Visiting Professor of Finance in the Sloan School at MIT. Professor Fabozzi is a Fellow of the International Center for Finance at Yale University and on the Advisory Council for the Department of Operations Research and Financial Engineering at Princeton University. He is the editor of \u003ci\u003eThe Journal of Portfolio Management\u003c\/i\u003e and an associate editor of the \u003ci\u003eThe Journal of Fixed Income\u003c\/i\u003e. He earned a doctorate in economics from the City University of New York in 1972. In 2002 Professor Fabozzi was inducted into the Fixed Income Analysts Society’s Hall of Fame. He earned the designation of Chartered Financial Analyst and Certified Public Accountant. He has authored and edited numerous books in finance.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eSergio Focardi\u003c\/b\u003e is a partner of The Intertek Group and a member of the Editorial Board of the \u003ci\u003eJournal of Portfolio Management\u003c\/i\u003e. He is the (co-) author of numerous articles and books on financial modeling and risk management, including the CFA Institute’s recent monograph \u003ci\u003eTrends in Quantitative Finance\u003c\/i\u003e (co-authors Fabozzi and Kolm) and the award-winning books \u003ci\u003eFinancial Modeling of the Equity Market\u003c\/i\u003e (co-authors Fabozzi and Kolm, Wiley) and \u003ci\u003eThe Mathematics of Financial Modeling and Investment Management\u003c\/i\u003e (co-author Fabozzi, Wiley). Mr. Focardi has implemented long-short portfolio construction applications based on dynamic factor analysis and conducts research in the econometrics of large equity portfolios and the modeling of regime changes. He holds a degree in Electronic Engineering from the University of Genoa and a postgraduate degree in Communications from the Galileo Ferraris Electrotechnical Institute (Turin).\u003c\/p\u003e \u003cp\u003e\u003cb\u003eTeo Jasic\u003c\/b\u003e earned his doctorate (Dr.rer.pol.) in economics from the University of Karlsruhe in 2006. He also holds an MSc degree from the National University of Singapore and a Dipl.-Ing. degree from the University of Zagreb. Currently, he is a Postdoctoral Research Fellow at the Chair ofStatistics, Econometrics and Mathematical Finance at the University of Karlsruhe in the School of Economics and Business Engineering. He is also a senior manager in Financial \u0026amp; Risk Management Group of a leading international management consultancy firm in Frankfurt, Germany. His current professional and research interests are in the areas of asset management, risk management, and financial forecasting. Dr. Jasic has published more than a dozen research papers in internationally refereed journals.\u003c\/p\u003e  Financial econometrics combines mathematical and statistical theory and techniques to understand and solve problems in financial economics. Modeling and forecasting financial time series, such as prices, returns, interest rates, financial ratios, and defaults, are important parts of this field.  \u003cp\u003eIn Financial Econometrics, you'll be introduced to this growing discipline and the concepts associated with it—from background material on probability theory and statistics to information regarding the properties of specific models and their estimation procedures.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eWith this book as your guide, you'll become familiar with:\u003c\/b\u003e\u003c\/p\u003e \u003cul\u003e \u003cli\u003eAutoregressive conditional heteroskedasticity (ARCH) and GARCH modeling\u003c\/li\u003e \u003cli\u003ePrincipal components analysis (PCA) and factor analysis\u003c\/li\u003e \u003cli\u003eStable processes and ARMA and GARCH models with fat-tailed errors\u003c\/li\u003e \u003cli\u003eRobust estimation methods\u003c\/li\u003e \u003cli\u003eVector autoregressive and cointegrated processes, including advanced estimation methods for cointegrated systems\u003c\/li\u003e \u003cli\u003eAnd much more\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eThe experienced author team of Svetlozar Rachev, Stefan Mittnik, Frank Fabozzi, Sergio Focardi, and Teo Jasic not only presents you with an abundant amount of information on financial econometrics, but they also walk you through a wide array of examples to solidify your understanding of the issues discussed.\u003c\/p\u003e \u003cp\u003eFilled with in-depth insights and expert advice, Financial Econometrics provides comprehensive coverage of this discipline and clear explanations of how the models associated with it fit into today's investment management process.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989209727205,"sku":"NP9780471784500","price":125.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780471784500.jpg?v=1761783218","url":"https:\/\/k12savings.com\/products\/financial-econometrics-isbn-9780471784500","provider":"K12savings","version":"1.0","type":"link"}