Introduction to Statistical Methods for Financial Models

Thomas A Severini

July 12, 2017 by Chapman and Hall/CRC
Textbook - 370 Pages - 33 B/W Illustrations
ISBN 9781138198371 - CAT# K31368
Series: Chapman & Hall/CRC Texts in Statistical Science

was $89.95

USD$71.96

SAVE ~$17.99

Add to Wish List
SAVE 25%
When you buy 2 or more products!
See final price in shopping cart.
FREE Standard Shipping!

Features

  • Covers statistical procedures for analyzing models used for financial data, including the market model, the single-index model, and factor models
  • Presents an introduction to some more advanced topics in statistical methods and financial modeling, including efficient portfolio theory, the use of portfolio constraints, shrinkage estimation, Monte Carlo methods, arbitrage pricing theory, and the estimation of factor premiums
  • Demonstrates how the methodology may be implemented in R
  • Contains detailed numerical examples using genuine financial data along with numerous exercises including both questions requiring analytic solutions and those requiring data analysis
  • Suitable for students with a background in mathematics and statistics but no prior experience in finance

Summary

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.