Introduction to Statistical Methods for Financial Models

Thomas A Severini

ISBN 10: 0367657872 / ISBN 13: 9780367657871

Published by Chapman And Hall/CRC, 2020

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - 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.

Seller Inventory # 9780367657871

Bibliographic Details

• Title: Introduction to Statistical Methods for Financial Models
• Publisher: Chapman And Hall/CRC
• Publication Date: 2020
• Language: English
• ISBN 10: 0367657872
• ISBN 13: 9780367657871
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 234W x 156H x 21D millimeters 234 millimeters width by 156 millimeters height by 21 millimeters depth
• Weight: 588 grams

About this title

Synopsis

This book provides an introduction to the use of statistical concepts and methods to model and analyze financial data. The ten chapters of the book fall naturally into three sections. Chapters 1 to 3 cover some basic concepts of finance, focusing on the properties of returns on an asset. Chapters 4 through 6 cover aspects of portfolio theory and the methods of estimation needed to implement that theory. The remainder of the book, Chapters 7 through 10, discusses several models for financial data, along with the implications of those models for portfolio theory and for understanding the properties of return data.

The audience for the book is students majoring in Statistics and Economics as well as in quantitative fields such as Mathematics and Engineering. Readers are assumed to have some background in statistical methods along with courses in multivariate calculus and linear algebra.

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.