Financial Econometric Modeling
Published on 21. May 2020
Book
Paperback/Softback
640 pages
978-0-19-085706-6 (ISBN)
Description
Financial econometrics brings financial theory and econometric methods together with the power of data to advance understanding of the global financial universe upon which all modern economies depend. Financial Econometric Modeling is an introductory text that meets the learning challenge of integrating theory, measurement, data, and software to understand the modern world of finance. Empirical applications with financial data play a central position in this book's exposition. Each chapter is a how-to guide that takes readers from ideas and theories through to the practical realities of modeling, interpreting, and forecasting financial data. The book reaches out to a wide audience of students, applied researchers, and industry practitioners, guiding readers of diverse backgrounds on the models, methods, and empirical practice of modern financial econometrics.
Financial Econometric Modeling delivers a self-contained first course in financial econometrics, providing foundational ideas from financial theory and relevant econometric technique. From this foundation, the book covers a vast arena of modern financial econometrics that opens up empirical applications with data of the many different types that are now generated in financial markets. Every chapter follows the same principle ensuring that all results reported in the book may be reproduced using standard econometric software packages such as Stata or EViews, with a full set of data and programs provided to ensure easy implementation.
Financial Econometric Modeling delivers a self-contained first course in financial econometrics, providing foundational ideas from financial theory and relevant econometric technique. From this foundation, the book covers a vast arena of modern financial econometrics that opens up empirical applications with data of the many different types that are now generated in financial markets. Every chapter follows the same principle ensuring that all results reported in the book may be reproduced using standard econometric software packages such as Stata or EViews, with a full set of data and programs provided to ensure easy implementation.
Reviews / Votes
Financial econometrics is the study and application of compelling econometric methods with a cogent financial purpose. This new book delivers a masterful introduction to financial econometrics at its best. It does so with enticing prose, motivating examples, utmost clarity and, ultimately, just the right balance of breadth and depth. In a world of big data and new technologies, not only does this rich treatment provide the fundamentals needed for more advanced explorations but also, in my view, the desire to explore further. To anyone new to this field, or to anyone who does not believe the field to be approachable and exciting, I say: this book will be an eye-opener. * Federico M. Bandi, James Carey Endowed Professor in Business, Johns Hopkins University* A comprehensive and long-overdue pedagogical treatment of financial econometrics * the only book to cover concepts, methodology, and empirical examples demonstrated with popular Stata and EViews software accessible to beginning students. The book is a self-contained first course, achieving the remarkable feat of an exhaustive introductory treatment that is inspiring, rigorous, and easy to read with clever organization into fundamentals, methods, and topics. A must-have reference source, perfect for teaching financial econometrics in masters courses or to graduate students with limited backgrounds.Eric Renault, C.V. Starr Professor of Economics, University of Warwick * Financial Econometric Modeling provides a broad introduction to financial econometrics, with an emphasis on applications and encouraging students to get their hands dirty from the very beginning. The authors cover a vast amount of material. The fact that all of the topics come with sample data sets for students to use * and all of the empirical work in the book can be replicated in EViews and Statawill be very attractive to many instructors and students.Andrew Patton, Zelter Family Professor of Economics, Duke University * I strongly recommend this textbook. It offers the perfect mix between solid bases and new developments, and between theoretical descriptions of tools and algorithms and a rich set of fully worked-out examples. * Massimo Guidolin, Professor of Finance, Bocconi University *
More details
Language
English
Place of publication
New York
United States
Target group
College/higher education
Dimensions
Height: 235 mm
Width: 191 mm
Thickness: 34 mm
Weight
1165 gr
ISBN-13
978-0-19-085706-6 (9780190857066)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Persons
Stan Hurn is Professor of Econometrics at Queensland University of Technology. He held previous positions at the University of Glasgow and Brasenose College, Oxford. He is a Fellow of the Society of Financial Econometrics and Founding Member and Director of the National Centre for Econometric Research in Australia.
Vance L. Martin is Professor of Econometrics at the University of Melbourne. He has published widely in the area of financial econometrics and is coauthor, with Stan Hurn, of the highly successful introductory text Econometric Modeling with Time Series Specification, Estimation, and Testing (2013).
Peter C.B. Phillips is Sterling Professor of Economics at Yale University, Distinguished Professor at the University of Auckland, and Distinguished Term Professor at Singapore Management University. He is Founding Editor of the journal Econometric Theory and an elected fellow of many learned societies including the British Academy, the American Academy of Arts and Sciences, and the Royal Society of New Zealand. His work has advanced diverse areas of econometrics, introduced new methods of research in financial economics, and influenced applied work throughout the social and business sciences.
Jun Yu is Lee Kong Chian Professor of Economics and Finance at Singapore Management University and Lead Principal Investigator at the Centre for Research on the Economics of Aging (CREA). He is a Fellow of the Journal of Econometrics and the Society of Financial Econometrics, and an Associate Editor of the Journal of Econometrics, Econometric Theory, and Journal of Financial Econometrics.
Vance L. Martin is Professor of Econometrics at the University of Melbourne. He has published widely in the area of financial econometrics and is coauthor, with Stan Hurn, of the highly successful introductory text Econometric Modeling with Time Series Specification, Estimation, and Testing (2013).
Peter C.B. Phillips is Sterling Professor of Economics at Yale University, Distinguished Professor at the University of Auckland, and Distinguished Term Professor at Singapore Management University. He is Founding Editor of the journal Econometric Theory and an elected fellow of many learned societies including the British Academy, the American Academy of Arts and Sciences, and the Royal Society of New Zealand. His work has advanced diverse areas of econometrics, introduced new methods of research in financial economics, and influenced applied work throughout the social and business sciences.
Jun Yu is Lee Kong Chian Professor of Economics and Finance at Singapore Management University and Lead Principal Investigator at the Centre for Research on the Economics of Aging (CREA). He is a Fellow of the Journal of Econometrics and the Society of Financial Econometrics, and an Associate Editor of the Journal of Econometrics, Econometric Theory, and Journal of Financial Econometrics.
Content
I: Fundamentals 1. Prices and Returns 1.1 What is Financial Econometrics? 1.2 Financial Assets 1.3 Equity Prices and Returns 1.4 Stock Market Indices 1.5 Bond Yields 1.6 Exercises
2. Financial Data 2.1irst Look at the Data 2.2 Summary Statistics 2.3 Percentiles and Value at Risk 2.4 The Efficient Market Hypothesis 2.5 Exercises
3. Linear Regression 3.1 The Capital Asset Pricing Model 3.2 Multi-factor CAPM 3.3 Properties of Ordinary Least Squares 3.4 Diagnostics 3.5 Measuring Portfolio Performance 3.6 Minimum Variance Portfolios 3.7 Event Analysis 3.8 Exercises
4. Stationary Dynamics 4.1 Stationarity 4.2 Univariate Time Series Models 4.3 Autocorrelation and Partial Autocorrelations 4.4 Mean Aversion and Reversion in Returns 4.5 Vector Autoregressive Models 4.6 Analysing VARs 4.7 Diebold-Yilmaz Spillover Index 4.8 Exercises
5. Nonstationarity 5.1 The RandomWalk with Drift 5.2 Characteristics of Financial Data 5.3 Dickey-Fuller Methods and Unit Root Testing 5.4 Beyond the Simple Unit Root Framework 5.5 Asset Price Bubbles 5.6 Exercises
6. Cointegration 6.1 The Present Value Model and Cointegration 6.2 Vector Error Correction Models 6.3 Estimation 6.4 Cointegration Testing 6.5 Parameter Testing 6.6 Cointegration and the Gordon Model 6.7 Cointegration and the Yield Curve 6.8 Exercises
7. Forecasting 7.1 Types of Forecasts 7.2 Forecasting Univariate Time Series Models 7.3 Forecasting Multivariate Time Series Models 7.4 Combining Forecasts. 7.5 Forecast Evaluation Statistics 7.6 Evaluating the Density of Forecast Errors 7.7 Regression Model Forecasts 7.8 Predicting the Equity Premium 7.9 Stochastic Simulation of Value at Risk 7.10 Exercises
II. Methods 8. Instrumental Variables 8.1 The Exogeneity Assumption 8.2 Estimating the Risk-Return Tradeoff 8.3 The General Instrumental Variables Estimator 8.4 Testing for Endogeneity 8.5 Weak Instruments 8.6 Consumption CAPM 8.7 Endogeneity and Corporate Finance 8.8 Exercises
9. Generalised Method of Moments 9.1 Single Parameter Models 9.2 Multiple Parameter Models 9.3 Over-Identified Models 9.4 Estimation 9.5 Properties of the GMM Estimator 9.6 Testing 9.7 Consumption CAPM Revisited 9.8 The CKLS Model of Interest Rates 9.9 Exercises
10. Maximum Likelihood 10.1 Distributions in Finance 10.2 Estimation by Maximum Likelihood 10.3 Applications 10.4 Numerical Methods 10.5 Properties 10.6 Quasi Maximum Likelihood Estimation 10.7 Testing 10.8 Exercises
11. Panel Data Models 11.1 Types of Panel Data 11.2 Reasons for Using Panel Data 11.3 Two Introductory Panel Models 11.4 Fixed and Random Effects Panel Models 11.5 Dynamic Panel Models 11.6 Nonstationary Panel Models 11.7 Exercises
12. Latent Factor Models 12.1 Motivation 12.2 Principal Components 12.3atent Factor CAPM 12.4 Dynamic Factor Models: the Kalman Filter 12.5arametric Approach to Factors 12.6 Stochastic Volatility 12.7 Exercises
III: Topics 13. Univariate GARCH Models 13.1 Volatility Clustering. 13.2 The GARCH Model 13.3 Asymmetric Volatility Effects 13.4 Forecasting 13.5 The Risk-Return Tradeoff. 13.6 Heatwaves and Meteor Showers 13.7 Exercises
14. Multivariate GARCH Models 14.1 Motivation 14.2 Early Covariance Estimators 14.3 The BEKK Model 14.4 The DCC Model 14.5 Optimal Hedge Ratios 14.6 Capital Ratios and Financial Crises 14.7 Exercises
15. Realised Variance and Covariance 15.1 High Frequency Data 15.2 Realised Variance 15.3 Integrated Variance 15.4 Microstructure Noise 15.5 Bipower Variation and Jumps 15.6 Forecasting 15.7 The Realised GARCH Model 15.8 Realised Covariance 15.9 Exercises
16. Microstructure Models 16.1 Characteristics of High Frequency Data 16.2 Limit Order Book 16.3 Bid Ask Bounce 16.4 Information Content of Trades 16.5 Modelling Price Movements in Trades 16.6 Modelling Durations 16.7 Modelling Volatility in Transactions Time 16.8 Exercises
17. Options 17.1 Option Pricing Basics. 17.2 The Black-Scholes Option Price Model 17.3irst Look at Options Data 17.4 Estimating the Black-Scholes Model 17.5 Testing the Black-Scholes Model 17.6 Option Pricing and GARCH Volatility 17.7 The Melick-Thomas Option Price Model 17.8 Nonlinear Option Pricing. 17.9 Using Options to Estimate GARCH Models 17.10 Exercises
18. Extreme Values and Copulas 18.1 Motivation. 18.2 Evidence of Heavy Tails 18.3 Extreme Value Theory 18.4 Modelling Dependence using Copulas 18.5 Properties of Copulas 18.6 Estimating Copula Models 18.7 MGARCH Model Using Copulas 18.8 Exercises
19. Concluding Remarks A. Mathematical Preliminaries A.1 Summation Notation A.2 Expectations Operator A.3 Differentiation A.4 Taylor Series Expansions A.5 Matrix Algebra A.6 Transposition ofatrix A.7 Symmetric Matrix B. Properties of Estimators B.1 Finite Sample Properties B.2 Asymptotic Properties C. Linear Regression Model in Matrix Notation D. Numerical Optimisation E. Simulating Copulas Author index Subject index
2. Financial Data 2.1irst Look at the Data 2.2 Summary Statistics 2.3 Percentiles and Value at Risk 2.4 The Efficient Market Hypothesis 2.5 Exercises
3. Linear Regression 3.1 The Capital Asset Pricing Model 3.2 Multi-factor CAPM 3.3 Properties of Ordinary Least Squares 3.4 Diagnostics 3.5 Measuring Portfolio Performance 3.6 Minimum Variance Portfolios 3.7 Event Analysis 3.8 Exercises
4. Stationary Dynamics 4.1 Stationarity 4.2 Univariate Time Series Models 4.3 Autocorrelation and Partial Autocorrelations 4.4 Mean Aversion and Reversion in Returns 4.5 Vector Autoregressive Models 4.6 Analysing VARs 4.7 Diebold-Yilmaz Spillover Index 4.8 Exercises
5. Nonstationarity 5.1 The RandomWalk with Drift 5.2 Characteristics of Financial Data 5.3 Dickey-Fuller Methods and Unit Root Testing 5.4 Beyond the Simple Unit Root Framework 5.5 Asset Price Bubbles 5.6 Exercises
6. Cointegration 6.1 The Present Value Model and Cointegration 6.2 Vector Error Correction Models 6.3 Estimation 6.4 Cointegration Testing 6.5 Parameter Testing 6.6 Cointegration and the Gordon Model 6.7 Cointegration and the Yield Curve 6.8 Exercises
7. Forecasting 7.1 Types of Forecasts 7.2 Forecasting Univariate Time Series Models 7.3 Forecasting Multivariate Time Series Models 7.4 Combining Forecasts. 7.5 Forecast Evaluation Statistics 7.6 Evaluating the Density of Forecast Errors 7.7 Regression Model Forecasts 7.8 Predicting the Equity Premium 7.9 Stochastic Simulation of Value at Risk 7.10 Exercises
II. Methods 8. Instrumental Variables 8.1 The Exogeneity Assumption 8.2 Estimating the Risk-Return Tradeoff 8.3 The General Instrumental Variables Estimator 8.4 Testing for Endogeneity 8.5 Weak Instruments 8.6 Consumption CAPM 8.7 Endogeneity and Corporate Finance 8.8 Exercises
9. Generalised Method of Moments 9.1 Single Parameter Models 9.2 Multiple Parameter Models 9.3 Over-Identified Models 9.4 Estimation 9.5 Properties of the GMM Estimator 9.6 Testing 9.7 Consumption CAPM Revisited 9.8 The CKLS Model of Interest Rates 9.9 Exercises
10. Maximum Likelihood 10.1 Distributions in Finance 10.2 Estimation by Maximum Likelihood 10.3 Applications 10.4 Numerical Methods 10.5 Properties 10.6 Quasi Maximum Likelihood Estimation 10.7 Testing 10.8 Exercises
11. Panel Data Models 11.1 Types of Panel Data 11.2 Reasons for Using Panel Data 11.3 Two Introductory Panel Models 11.4 Fixed and Random Effects Panel Models 11.5 Dynamic Panel Models 11.6 Nonstationary Panel Models 11.7 Exercises
12. Latent Factor Models 12.1 Motivation 12.2 Principal Components 12.3atent Factor CAPM 12.4 Dynamic Factor Models: the Kalman Filter 12.5arametric Approach to Factors 12.6 Stochastic Volatility 12.7 Exercises
III: Topics 13. Univariate GARCH Models 13.1 Volatility Clustering. 13.2 The GARCH Model 13.3 Asymmetric Volatility Effects 13.4 Forecasting 13.5 The Risk-Return Tradeoff. 13.6 Heatwaves and Meteor Showers 13.7 Exercises
14. Multivariate GARCH Models 14.1 Motivation 14.2 Early Covariance Estimators 14.3 The BEKK Model 14.4 The DCC Model 14.5 Optimal Hedge Ratios 14.6 Capital Ratios and Financial Crises 14.7 Exercises
15. Realised Variance and Covariance 15.1 High Frequency Data 15.2 Realised Variance 15.3 Integrated Variance 15.4 Microstructure Noise 15.5 Bipower Variation and Jumps 15.6 Forecasting 15.7 The Realised GARCH Model 15.8 Realised Covariance 15.9 Exercises
16. Microstructure Models 16.1 Characteristics of High Frequency Data 16.2 Limit Order Book 16.3 Bid Ask Bounce 16.4 Information Content of Trades 16.5 Modelling Price Movements in Trades 16.6 Modelling Durations 16.7 Modelling Volatility in Transactions Time 16.8 Exercises
17. Options 17.1 Option Pricing Basics. 17.2 The Black-Scholes Option Price Model 17.3irst Look at Options Data 17.4 Estimating the Black-Scholes Model 17.5 Testing the Black-Scholes Model 17.6 Option Pricing and GARCH Volatility 17.7 The Melick-Thomas Option Price Model 17.8 Nonlinear Option Pricing. 17.9 Using Options to Estimate GARCH Models 17.10 Exercises
18. Extreme Values and Copulas 18.1 Motivation. 18.2 Evidence of Heavy Tails 18.3 Extreme Value Theory 18.4 Modelling Dependence using Copulas 18.5 Properties of Copulas 18.6 Estimating Copula Models 18.7 MGARCH Model Using Copulas 18.8 Exercises
19. Concluding Remarks A. Mathematical Preliminaries A.1 Summation Notation A.2 Expectations Operator A.3 Differentiation A.4 Taylor Series Expansions A.5 Matrix Algebra A.6 Transposition ofatrix A.7 Symmetric Matrix B. Properties of Estimators B.1 Finite Sample Properties B.2 Asymptotic Properties C. Linear Regression Model in Matrix Notation D. Numerical Optimisation E. Simulating Copulas Author index Subject index