Analysis of Financial Data 1st Edition by Gary Koop ISBN 978-0470013212

Copyright
Preface
1. Introduction
1.1. Organization of the book
1.2. Useful background
1.3. Appendix 1.1: Concepts in mathematics used in this book
1.3.1. The equation of a straight line
1.3.2. Summation notation
1.3.3. Logarithms
2. Basic data handling
2.1. Types of financial data
2.1.1. Time series data
2.1.2. Cross-sectional data
2.1.3. The distinction between qualitative and quantitative data
2.1.4. Panel data
2.1.5. Data transformations: levels, growth rates, returns and excess returns
2.1.6. Index numbers
2.2. Obtaining data
2.3. Working with data: graphical methods
2.3.1. Time series graphs
2.3.2. Histograms
2.3.3. XY-plots
2.4. Working with data: descriptive statistics
2.5. Expected values and variances
2.6. Chapter summary
2.7. Appendix 2.1: Index numbers
2.7.1. Calculating a Megaco price index
2.7.2. Calculating a stock price index
2.8. Appendix 2.2: Advanced descriptive statistics
3. Correlation
3.1. Understanding correlation
3.1.1. Properties of correlation
3.1.2. Understanding correlation through verbal reasoning
3.2. Understanding why variables are correlated
3.3. Understanding correlation through XY-plots
3.4. Correlation between several variables
3.5. Covariances and population correlations
3.6. Chapter summary
3.7. Appendix 3.1: Mathematical details
4. An introduction to simple regression
4.1. Regression as a best fitting line
4.2. Interpreting OLS estimates
4.3. Fitted values and R2: measuring the fit of a regression model
4.4. Nonlinearity in regression
4.5. Chapter summary
4.6. 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 β
5.3. Testing whether β = 0
5.4. Hypothesis testing involving R2: the F-statistic
5.5. Chapter summary
5.6. Appendix 5.1: Using statistical tables for testing whether β = 0
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
7.1. Simple regression with a dummy variable
7.2. Multiple regression with dummy variables
7.3. Multiple regression with both dummy and non-dummy explanatory variables
7.4. Interacting dummy and non-dummy variables
7.5. What if the dependent variable is a dummy?
7.6. Chapter summary
8. Regression with lagged explanatory variables
8.1. Aside on lagged variables
8.2. Aside on notation
8.3. Selection of lag order
8.4. Chapter summary
9. Univariate time series analysis
9.1. The autocorrelation function
9.1.1. Aside
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
9.5.1. Testing involving α, γ1, …, γp−1, and δ
9.5.2. Testing involving ρ
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
10.1.1. Aside for Excel users
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
10.3.1. Estimation and testing with cointegrated variables
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
11.1. Granger causality
11.1.1. Granger causality in a simple ADL model
11.1.2. Granger causality in an ADL model with p and q lags
11.1.3. Causality in both directions
11.1.4. Granger causality with cointegrated variables
11.2. Vector autoregressions
11.2.1. Lag length selection in VARs
11.2.2. Forecasting with VARs
11.2.3. Vector autoregressions with cointegrated variables
11.3. Chapter summary
11.4. Appendix 11.1: Hypothesis tests involving more than one coefficient
11.5. Appendix 11.2: Variance decompositions
12. Financial volatility
12.1. Volatility in asset prices: Introduction
12.2. Autoregressive conditional heteroskedasticity (ARCH)
12.3. Chapter summary
A. Writing an empirical project
A.1. Description of a typical empirical project
A.2. General considerations
B. Data directory