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This book introduces the use of statistical concepts and methods to model and analyze financial data, including the market model, the single-index model, and factor models. It contains detailed numerical examples using genuine financial data along with numerous exercises including both questions requiring analytic solutions and those requiring data analysis.
List of contents
Returns.
Random Walk Hypothesis.
Portfolios.
Efficient Portfolio Theory.
Estimation.
Capital Asset Pricing Model.
The Market Model.
The Single-Index Model.
Factor Models.
About the author
Thomas A. Severini is a professor of statistics at Northwestern University. He is a fellow of the American Statistical Association and the author of Likelihood Methods in Statistics and Elements of Distribution Theory. He received his PhD in statistics from the University of Chicago. His research areas include likelihood inference, nonparametric and semiparametric methods, and applications to econometrics.
Summary
This book introduces the use of statistical concepts and methods to model and analyze financial data, including the market model, the single-index model, and factor models. It contains detailed numerical examples using genuine financial data along with numerous exercises including both questions requiring analytic solutions and those requiring data analysis.
Additional text
"This will be an excellent textbook designed for more mathematically oriented undergraduates and masters students. A course out of this book could be offered by mathematics, statistics, economics, and engineering departments. Students will find that this book provides valuable preparation for a career in finance. Professor Severini is a distinguished researcher. His clearly-written book combines a theorem-proof style typical of mathematics texts with concrete examples that will aid students' understanding of key concepts. Many examples are done in R which has become a popular teaching tool for courses at all levels. Besides its use as a textbook for formal courses, this book will be valuable resource for self-study."—David Ruppert, Cornell University
"Data-driven approaches to financial analysis have gained in prominence with the availability of high-performance computing and access to large databases. Balancing such a perspective with an accessible introduction to statistical theory is what sets this treatment apart. Throughout, a hands-on approach using R code and market data is used to explain both statistical concepts and to implement financial models. Thus, students may be able to grasp the theoretical underpinnings for analysis without feeling overwhelmed by them. This is a welcome addition to the Texts in Statistical Science series, and I will encourage my advanced undergraduate students to keep a copy close at hand."—Afzal S. Siddiqui, Department of Statistical Science University College London and Department of Computer and Systems Sciences Stockholm University
"This book is both an exceptional introductory textbook and the essential desk reference for all students and practitioners of financial econometrics and quantitative finance with an interest in quantitative portfolio design and analysis. This well-written book begins with a deceptively simple introduction discussing financial data and its basic models together with how to estimate these models. It develops all the techniques required to estimate and apply the standard theoretical portfolio models, and includes R codes that help the reader understand the material and implement it on real datasets. It confronts the well-known instability of weight estimates in the Markowitz portfolio model head on and provides modern techniques for improving the required statistical estimates. These first seven chapters would, taken alone, represent the ideal book for a deep, rigorous one semester course on portfolio theory for senior undergraduate or graduate students in economics, actuarial science, or quantitative finance. Three more beautiful chapters remain, covering the Market Model, the Single-Index Model, and Factor Models. Each chapter ends with an insightful guided tour of other important books and papers in the area that allow deeper engagement with the material. It includes more than 150 questions which encourage students to develop theoretical familiarity with the concepts introduced, facility with the R computations required to apply these concepts, and the beginnings of the deep financial intuition about markets which the author clearly possesses."— Matt Davison, Director, School of Mathematical & Statistical Sciences, Professor of Applied Mathematics and of Statistical & Actuarial Sciences, Western University Canada, London, Ontario
