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.

**Chapter 1: Introduction.**

Organization of the book.

Useful background.

Appendix 1.1: Concepts in mathematics used in this book.

**Chapter 2: Basic data handling.**

Types of financial data.

Obtaining data.

Working with data: graphical methods.

Working with data: descriptive statistics.

Expected values and variances.

Chapter summary.

Appendix 2.1: Index numbers.

Appendix 2.2: Advanced descriptive statistics.

**Chapter 3: Correlation.**

Understanding correlation.

Understanding why variables are correlated.

Understanding correlation through *XY*-plots.

Correlation between several variables.

Covariances and population correlations.

Chapter summary.

Appendix 3.1: Mathematical details.

**Chapter 4: An introduction to simple regression.**

Regression as a best fitting line.

Interpreting OLS estimates.

Fitted values and *R ^{}*2: measuring the fit of a regression model.

Nonlinearity in regression.

Chapter summary.

Appendix 4.1: Mathematical details.

**Chapter 5: Statistical aspects of regression.**

Which factors affect the accuracy of the estimate β?

Calculating a confidence interval for β.

Testing whether β = 0.

Hypothesis testing involving *R ^{}*2: the

Chapter summary.

Appendix 5.1: Using statistical tables for testing whether β = 0.

**Chapter 6: Multiple regression.**

Regression as a best fitting line.

Ordinary least squares estimation of the multiple regression model.

Statistical aspects of multiple regression.

Interpreting OLS estimates.

Pitfalls of using simple regression in a multiple regression context.

Omitted variables bias.

Multicollinearity.

Chapter summary.

Appendix 6.1: Mathematical interpretation of regression coefficients.

**Chapter 7: Regression with dummy variables.**

Simple regression with a dummy variable.

Multiple regression with dummy variables.

Multiple regression with both dummy and non-dummy explanatory variables.

Interacting dummy and non-dummy variables.

What if the dependent variable is a dummy?

Chapter summary.

**Chapter 8: Regression with lagged explanatory variables.**

Aside on lagged variables.

Aside on notation.

Selection of lag order.

Chapter summary.

**Chapter 9: Univariate time series analysis.**

The autocorrelation function.

The autoregressive model for univariate time series.

Nonstationary versus stationary time series.

Extensions of the AR(1) model.

Testing in the AR( *p*) with deterministic trend model.

Chapter summary.

Appendix 9.1: Mathematical intuition for the AR(1) model.

**Chapter 10: Regression with time series variables.**

Time series regression when *X* and *Y* are stationary.

Time series regression when *Y* and *X* have unit roots: spurious regression.

Time series regression when *Y* and *X* have unit roots: cointegration.

Time series regression when *Y* and *X* are cointegrated: the error correction model.

Time series regression when *Y* and *X* have unit roots but are not cointegrated.

Chapter summary.

**Chapter 11: Regression with time series variables with several equations.**

Granger causality.

Vector autoregressions.

Chapter summary.

Appendix 11.1: Hypothesis tests involving more than one coefficient.

Appendix 11.2: Variance decompositions.

**Chapter 12: Financial volatility.**

Volatility in asset prices: Introduction.

Autoregressive conditional heteroskedasticity (ARCH).

Chapter summary.

**Appendix A: Writing an empirical project.**

Description of a typical empirical project.

General considerations.

**Appendix B: Data directory.**

**Index.**