book

Analysis of Financial Data

Name: Analysis of Financial Data
Author: Gary Koop
ISBN: 9780470013212

by Gary Koop

January 2006

Beginner

250 pages

6h 29m

English

Wiley

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Copyright
Preface
1. Introduction
1.1. Organization of the book1.2. Useful background1.3. Appendix 1.1: Concepts in mathematics used in this book1.3.1. The equation of a straight line1.3.2. Summation notation1.3.3. Logarithms
2. Basic data handling
2.1. Types of financial data2.1.1. Time series data2.1.2. Cross-sectional data2.1.3. The distinction between qualitative and quantitative data2.1.4. Panel data2.1.5. Data transformations: levels, growth rates, returns and excess returns2.1.6. Index numbers2.2. Obtaining data2.3. Working with data: graphical methods2.3.1. Time series graphs2.3.2. Histograms2.3.3. XY-plots2.4. Working with data: descriptive statistics2.5. Expected values and variances2.6. Chapter summary2.7. Appendix 2.1: Index numbers2.7.1. Calculating a Megaco price index2.7.2. Calculating a stock price index2.8. Appendix 2.2: Advanced descriptive statistics
3. Correlation
3.1. Understanding correlation3.1.1. Properties of correlation3.1.2. Understanding correlation through verbal reasoning3.2. Understanding why variables are correlated3.3. Understanding correlation through XY-plots3.4. Correlation between several variables3.5. Covariances and population correlations3.6. Chapter summary3.7. Appendix 3.1: Mathematical details
4. An introduction to simple regression
4.1. Regression as a best fitting line4.2. Interpreting OLS estimates4.3. Fitted values and R2: measuring the fit of a regression model4.4. Nonlinearity in regression4.5. Chapter summary4.6. Appendix 4.1: Mathematical details
5. Statistical aspects of regression
5.1. Which factors affect the accuracy of the estimate ?5.2. Calculating a confidence interval for β5.3. Testing whether β = 05.4. Hypothesis testing involving R2: the F-statistic5.5. Chapter summary5.6. Appendix 5.1: Using statistical tables for testing whether β = 0
6. Multiple regression
6.1. Regression as a best fitting line6.2. Ordinary least squares estimation of the multiple regression model6.3. Statistical aspects of multiple regression6.4. Interpreting OLS estimates6.5. Pitfalls of using simple regression in a multiple regression context6.6. Omitted variables bias6.7. Multicollinearity6.8. Chapter summary6.9. Appendix 6.1: Mathematical interpretation of regression coefficients
7. Regression with dummy variables
7.1. Simple regression with a dummy variable7.2. Multiple regression with dummy variables7.3. Multiple regression with both dummy and non-dummy explanatory variables7.4. Interacting dummy and non-dummy variables7.5. What if the dependent variable is a dummy?7.6. Chapter summary
8. Regression with lagged explanatory variables
8.1. Aside on lagged variables8.2. Aside on notation8.3. Selection of lag order8.4. Chapter summary

9. Univariate time series analysis
9.1. The autocorrelation function9.1.1. Aside9.2. The autoregressive model for univariate time series9.3. Nonstationary versus stationary time series9.4. Extensions of the AR(1) model9.5. Testing in the AR(p) with deterministic trend model9.5.1. Testing involving α, γ1, ..., γp−1, and δ9.5.2. Testing involving ρ9.6. Chapter summary9.7. 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 stationary10.1.1. Aside for Excel users10.2. Time series regression when Y and X have unit roots: spurious regression10.3. Time series regression when Y and X have unit roots: cointegration10.3.1. Estimation and testing with cointegrated variables10.4. Time series regression when Y and X are cointegrated: the error correction model10.5. Time series regression when Y and X have unit roots but are not cointegrated10.6. Chapter summary
11. Regression with time series variables with several equations
11.1. Granger causality11.1.1. Granger causality in a simple ADL model11.1.2. Granger causality in an ADL model with p and q lags11.1.3. Causality in both directions11.1.4. Granger causality with cointegrated variables11.2. Vector autoregressions11.2.1. Lag length selection in VARs11.2.2. Forecasting with VARs11.2.3. Vector autoregressions with cointegrated variables11.3. Chapter summary11.4. Appendix 11.1: Hypothesis tests involving more than one coefficient11.5. Appendix 11.2: Variance decompositions
12. Financial volatility
12.1. Volatility in asset prices: Introduction12.2. Autoregressive conditional heteroskedasticity (ARCH)12.3. Chapter summary
A. Writing an empirical project
A.1. Description of a typical empirical projectA.2. General considerations
B. Data directory

Overview

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 real-world empirical problems.

Adopting a largely non-mathematical 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 hands-on 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.

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