Linear Models and Time-Series Analysis: Regression, ANOVA, ARMA and GARCH (Wiley Series in Probability and Statistics) - Hardcover

Paolella, Marc S.

Hardcover

ISBN 10: 1119431905 ISBN 13: 9781119431909

Publisher: Wiley, 2018

Synopsis

A comprehensive and timely edition on an emerging new trend in time series

Linear Models and Time-Series Analysis: Regression, ANOVA, ARMA and GARCH sets a strong foundation, in terms of distribution theory, for the linear model (regression and ANOVA), univariate time series analysis (ARMAX and GARCH), and some multivariate models associated primarily with modeling financial asset returns (copula-based structures and the discrete mixed normal and Laplace). It builds on the author's previous book, Fundamental Statistical Inference: A Computational Approach, which introduced the major concepts of statistical inference. Attention is explicitly paid to application and numeric computation, with examples of Matlab code throughout. The code offers a framework for discussion and illustration of numerics, and shows the mapping from theory to computation. 

The topic of time series analysis is on firm footing, with numerous textbooks and research journals dedicated to it. With respect to the subject/technology, many chapters in Linear Models and Time-Series Analysis cover firmly entrenched topics (regression and ARMA). Several others are dedicated to very modern methods, as used in empirical finance, asset pricing, risk management, and portfolio optimization, in order to address the severe change in performance of many pension funds, and changes in how fund managers work. 

• Covers traditional time series analysis with new guidelines
• Provides access to cutting edge topics that are at the forefront of financial econometrics and industry
• Includes latest developments and topics such as financial returns data, notably also in a multivariate context
• Written by a leading expert in time series analysis 
• Extensively classroom tested
• Includes a tutorial on SAS
• Supplemented with a companion website containing numerous Matlab programs
• Solutions to most exercises are provided in the book

Linear Models and Time-Series Analysis: Regression, ANOVA, ARMA and GARCH is suitable for advanced masters students in statistics and quantitative finance, as well as doctoral students in economics and finance. It is also useful for quantitative financial practitioners in large financial institutions and smaller finance outlets.    

About this title

About the Author

Marc S. Paolella is Professor of Empirical Finance at the University of Zurich, Switzerland. He is also the Editor of Econometrics and an Associate Editor of the Royal Statistical Society Journal Series. With almost 20 years of teaching experience, he is a frequent collaborator to journals and a member of many editorial boards and societies.

From the Back Cover

Linear Models and Time-Series Analysis

Regression, ANOVA, ARMA and GARCH

A comprehensive and timely edition on an emerging new trend in time-series

Linear Models and Time-Series Analysis: Regression, ANOVA, ARMA and GARCH sets a strong foundation, in terms of distribution theory, for the linear model (regression and ANOVA), univariate time-series analysis (ARMAX and GARCH), and some multivariate models associated primarily with modeling financial asset returns (copula-based structures and the discrete mixed normal and Laplace). It builds on the author's previous book, Fundamental Statistical Inference: A Computational Approach, which introduced the major concepts of statistical inference. Attention is explicitly paid to application and numeric computation, with examples of MATLAB code throughout.

The chapters on regression, ANOVA, and ARMA cover much standard ground, with the emphasis on clear, detailed derivations of all major results, as well as development of MATLAB codes and use of simulations to show the theory. Important topics such as (small-sample distribution theory associated with) unit root tests and time-varying parameter regression models, and order selection in ARMAX models, are also discussed in detail.

Several chapters are dedicated to very modern methods in empirical finance, risk management, and portfolio optimization. In particular, various methods for non-Gaussian, non-elliptic, large-scale portfolio optimization are developed, along with MATLAB codes, and the incorporation of transaction costs. The use of such models is shown, with real data, to highly outperform common models such as Markowitz, Gaussian CCC, and DCC.

Further highlights include:

• An extensive appendix that discusses and derives all major results associated with ellipticity
• Two appendix chapters that detail the theory and computation of the distribution and moments for Gaussian quadratic forms, and their ratios, including use of exact methods and saddlepoint approximations
• A tutorial on the use of SAS for data manipulation is included
• Solutions to most exercises are provided within the book

This book is suitable for advanced masters students in statistics and quantitative finance, as well as doctoral students in economics and finance. It is also useful for quantitative financial practitioners in large financial institutions and smaller finance outlets.

From the Inside Flap

Linear Models and Time-Series Analysis

Regression, ANOVA, ARMA and GARCH

A comprehensive and timely edition on an emerging new trend in time-series

Linear Models and Time-Series Analysis: Regression, ANOVA, ARMA and GARCH sets a strong foundation, in terms of distribution theory, for the linear model (regression and ANOVA), univariate time-series analysis (ARMAX and GARCH), and some multivariate models associated primarily with modeling financial asset returns (copula-based structures and the discrete mixed normal and Laplace). It builds on the author's previous book, Fundamental Statistical Inference: A Computational Approach, which introduced the major concepts of statistical inference. Attention is explicitly paid to application and numeric computation, with examples of MATLAB code throughout.

The chapters on regression, ANOVA, and ARMA cover much standard ground, with the emphasis on clear, detailed derivations of all major results, as well as development of MATLAB codes and use of simulations to show the theory. Important topics such as (small-sample distribution theory associated with) unit root tests and time-varying parameter regression models, and order selection in ARMAX models, are also discussed in detail.

Several chapters are dedicated to very modern methods in empirical finance, risk management, and portfolio optimization. In particular, various methods for non-Gaussian, non-elliptic, large-scale portfolio optimization are developed, along with MATLAB codes, and the incorporation of transaction costs. The use of such models is shown, with real data, to highly outperform common models such as Markowitz, Gaussian CCC, and DCC.

Further highlights include:

• An extensive appendix that discusses and derives all major results associated with ellipticity
• Two appendix chapters that detail the theory and computation of the distribution and moments for Gaussian quadratic forms, and their ratios, including use of exact methods and saddlepoint approximations
• A tutorial on the use of SAS for data manipulation is included
• Solutions to most exercises are provided within the book

This book is suitable for advanced masters students in statistics and quantitative finance, as well as doctoral students in economics and finance. It is also useful for quantitative financial practitioners in large financial institutions and smaller finance outlets.

Edition details

• Publisher: Wiley
• Publication date: 2018
• Language: English
• ISBN 10: 1119431905
• ISBN 13: 9781119431909
• Binding: Hardcover
• Edition number: 1
• Number of pages: 896