Book Description
Analysis of Financial Data teaches the basic methods and techniques of data analysis to finance students, by showing them how to apply such techniques in the context of realworld empirical problems.
Adopting a largely nonmathematical approach Analysis of Financial Data relies more on verbal intuition and graphical methods for understanding.
Key features include:
Coverage of many of the major tools used by the financial economist e.g. correlation, regression, time series analysis and methods for analyzing financial volatility.
Extensive use of real data examples, which involves readers in handson computer work.
Mathematical techniques at a level suited to MBA students and undergraduates taking a first course in the topic.
Supplementary material for readers and lecturers provided on an accompanying website.
Table of Contents
 Copyright
 Preface
 1. Introduction
 2. Basic data handling
 3. Correlation
 4. An introduction to simple regression
 5. Statistical aspects of regression

6. Multiple regression
 6.1. Regression as a best fitting line
 6.2. Ordinary least squares estimation of the multiple regression model
 6.3. Statistical aspects of multiple regression
 6.4. Interpreting OLS estimates
 6.5. Pitfalls of using simple regression in a multiple regression context
 6.6. Omitted variables bias
 6.7. Multicollinearity
 6.8. Chapter summary
 6.9. Appendix 6.1: Mathematical interpretation of regression coefficients
 7. Regression with dummy variables
 8. Regression with lagged explanatory variables

9. Univariate time series analysis
 9.1. The autocorrelation function
 9.2. The autoregressive model for univariate time series
 9.3. Nonstationary versus stationary time series
 9.4. Extensions of the AR(1) model
 9.5. Testing in the AR(p) with deterministic trend model
 9.6. Chapter summary
 9.7. Appendix 9.1: Mathematical intuition for the AR(1) model

10. Regression with time series variables
 10.1. Time series regression when X and Y are stationary
 10.2. Time series regression when Y and X have unit roots: spurious regression
 10.3. Time series regression when Y and X have unit roots: cointegration
 10.4. Time series regression when Y and X are cointegrated: the error correction model
 10.5. Time series regression when Y and X have unit roots but are not cointegrated
 10.6. Chapter summary
 11. Regression with time series variables with several equations
 12. Financial volatility
 A. Writing an empirical project
 B. Data directory
Product Information
 Title: Analysis of Financial Data
 Author(s):
 Release date: January 2006
 Publisher(s): Wiley
 ISBN: 9780470013212