Analysis of Financial Data teaches basic methods and techniques of data analysis to finance students. It covers many of the major tools used by the financial economist i.e. regression and time series methods including discussion of nonstationary models, multivariate concepts such as cointegration and models of conditional volatility.

Preface ix Chapter 1 Introduction 1 Organization of the book 3 Useful background 4 Appendix 1.1: Concepts in mathematics used in this book 4 Chapter 2 Basic data handling 9 Types of financial data 9 Obtaining data 15 Working with data: graphical methods 16 Working with data: descriptive statistics 21 Expected values and variances 24 Chapter summary 26 Appendix 2.1: Index numbers 27 Appendix 2.2: Advanced descriptive statistics 30 Chapter 3 Correlation 33 Understanding correlation 33 Understanding why variables are correlated 39 Understanding correlation through XY-plots 40 Correlation between several variables 44 Covariances and population correlations 45 Chapter summary 47 Appendix 3.1: Mathematical details 47 Chapter 4 An introduction to simple regression 49 Regression as a best fitting line 50 Interpreting OLS estimates 53 Fitted values and R2: measuring the fit of a regression model 55 Nonlinearity in regression 61 Chapter summary 64 Appendix 4.1: Mathematical details 65 Chapter 5 Statistical aspects of regression 69 Which factors affect the accuracy of the estimate βˆ? 70 Calculating a confidence interval for β 73 Testing whether β =0 79 Hypothesis testing involving R2: the F-statistic 84 Chapter summary 86 Appendix 5.1: Using statistical tables for testing whether β =0 87 Chapter 6 Multiple regression 91 Regression as a best fitting line 93 Ordinary least squares estimation of the multiple regression model 93 Statistical aspects of multiple regression 94 Interpreting OLS estimates 95 Pitfalls of using simple regression in a multiple regression context 98 Omitted variables bias 100 Multicollinearity 102 Chapter summary 105 Appendix 6.1: Mathematical interpretation of regression coefficients 105 Chapter 7 Regression with dummy variables 109 Simple regression with a dummy variable 112 Multiple regression with dummy variables 114 Multiple regression with both dummy and non-dummy explanatory variables 116 Interacting dummy and non-dummy variables 120 What if the dependent variable is a dummy? 121 Chapter summary 122 Chapter 8 Regression with lagged explanatory variables 123 Aside on lagged variables 125 Aside on notation 127 Selection of lag order 132 Chapter summary 135 Chapter 9 Univariate time series analysis 137 The autocorrelation function 140 The autoregressive model for univariate time series 144 Nonstationary versus stationary time series 146 Extensions of the AR(1) model 149 Testing in the AR( p) with deterministic trend model 152 Chapter summary 158 Appendix 9.1: Mathematical intuition for the AR(1) model 159 Chapter 10 Regression with time series variables 161 Time series regression when X and Y are stationary 162 Time series regression when Y and X have unit roots: spurious regression 167 Time series regression when Y and X have unit roots: cointegration 167 Time series regression when Y and X are cointegrated: the error correction model 174 Time series regression when Y and X have unit roots but are not cointegrated 177 Chapter summary 179 Chapter 11 Regression with time series variables with several equations 183 Granger causality 184 Vector autoregressions 190 Chapter summary 203 Appendix 11.1: Hypothesis tests involving more than one coefficient 204 Appendix 11.2: Variance decompositions 207 Chapter 12 Financial volatility 211 Volatility in asset prices: Introduction 212 Autoregressive conditional heteroskedasticity (ARCH) 217 Chapter summary 222 Appendix A Writing an empirical project 223 Description of a typical empirical project 223 General considerations 225 Appendix B Data directory 227 Index 231

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.

Preface. 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 R2: 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 R2: the F-statistic. 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.