Wiley Global Education UK

Analysis of Financial Data

Gary Koop

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Table of contents
  • Front Matterix
  • Prefaceix
  • CHAPTER 1 Introduction1
  • Organization of the book3
  • Useful background4
  • Appendix 1.1: Concepts in mathematics used in this book4
  • The equation of a straight line5
  • Summation notation5
  • Logarithms6
  • CHAPTER 2 Basic data handling9
  • Types of financial data9
  • Time series data9
  • Cross-sectional data10
  • The distinction between qualitative and quantitative data10
  • Panel data11
  • Data transformations: levels, growth rates, returns and excess returns11
  • Index numbers13
  • Obtaining data15
  • Working with data: graphical methods16
  • Fig. 2.1 Time series graph of UK pound/US dollar exchange rate.17
  • Time series graphs17
  • Exercise 2.117
  • Histograms18
  • Fig. 2.2 Histogram.18
  • Exercise 2.219
  • XY-plots19
  • Fig. 2.3 XY–plot of profits against executive compensation.20
  • Exercise 2.321
  • Working with data: descriptive statistics21
  • Fig. 2.4 Histogram.23
  • Exercise 2.424
  • Expected values and variances24
  • Chapter summary26
  • Appendix 2.1: Index numbers27
  • Calculating a Megaco price index27
  • Table 2.1.1 Stock prices of companies in different years.27
  • Table 2.1.2 Calculating a Megaco stock price index.27
  • Calculating a stock price index28
  • Table 2.1.3 Market capitalization (millions of dollars).29
  • Table 2.1.4 Calculating the value-weighted stock price index.30
  • Appendix 2.2: Advanced descriptive statistics30
  • Endnotes31
  • CHAPTER 3 Correlation33
  • Understanding correlation33
  • Properties of correlation34
  • Understanding correlation through verbal reasoning34
  • Example: The correlation between executive compensation and profit34
  • Exercise 3.135
  • Example: The determinants of market capitalization36
  • Example: House prices in Windsor, Canada36
  • Exercise 3.238
  • Understanding why variables are correlated39
  • Example: Correlation does not necessarily imply causality39
  • Exercise 3.340
  • Understanding correlation through XY-plots40
  • Fig. 3.1 XY-plot of house price versus lot size.41
  • Fig. 3.2 XY-plot of two perfectly correlated variables (r = 1).42
  • Fig. 3.3 XY-plot of two positively correlated variables (r = 0.51).42
  • Fig. 3.4 XY-plot of two uncorrelated variables (r = 0).43
  • Fig. 3.5 XY-plot of two negatively correlated variables (r = -0.58).43
  • Exercise 3.444
  • Correlation between several variables44
  • Table 3.1 The correlation matrix for X, Y, and Z.44
  • Exercise 3.545
  • Covariances and population correlations45
  • Chapter summary47
  • Appendix 3.1: Mathematical details47
  • Endnotes48
  • CHAPTER 4 An introduction to simple regression49
  • Regression as a best fitting line50
  • Fig. 4.1 Best fitting line for three data points.52
  • Example: The regression of executive compensation on profits53
  • Interpreting OLS estimates53
  • Example: Regression of executive compensation on profits (continued from page 53)54
  • Exercise 4.154
  • Fitted values and R2: measuring the fit of a regression model55
  • Exercise 4.256
  • Exercise 4.356
  • Exercise 4.458
  • Example: The capital asset pricing model58
  • Nonlinearity in regression61
  • Fig. 4.2 A quadratic relationship between X and Y.61
  • Fig. 4.3 X and Y need to be logged.63
  • Fig. 4.4 ln (X) versus ln (Y).63
  • Exercise 4.564
  • Exercise 4.664
  • Chapter summary64
  • Appendix 4.1: Mathematical details65
  • Optional exercise65
  • Endnotes66
  • CHAPTER 5 Statistical aspects of regression69
  • Which factors affect the accuracy of the estimate ?70
  • Fig. 5.1 Very small sample size.71
  • Fig. 5.2 Large sample size, large error variance.71
  • Fig. 5.3 Large sample size, small error variance.72
  • Fig. 5.4 Limited range of X values.72
  • Calculating a confidence interval for β73
  • Example: Election polls75
  • Example: Confidence intervals for the data sets in Figures 5.1–5.477
  • Table 5.1 OLS estimates and confidence intervals.77
  • Exercise 5.178
  • Example: The regression of executive compensation on profits78
  • Example: The regression of lot size on house price78
  • Exercise 5.279
  • Exercise 5.379
  • Testing whether β = 079
  • Example: The regression of executive compensation on profits (continued from page 78)82
  • Table 5.2 The regression of executive compensation on profits.82
  • Example: The capital asset pricing model (continued from page 79)83
  • Exercise 5.483
  • Exercise 5.583
  • Exercise 5.683
  • Example: The regression of lot size on house price (continued from page 78)84
  • Exercise 5.784
  • Hypothesis testing involving R2: the F-statistic84
  • Example: The regression of executive compensation on profits (continued from page 82)86
  • Exercise 5.886
  • Chapter summary86
  • Appendix 5.1 : Using statistical tables for testing whether β = 087
  • Endnotes88
  • CHAPTER 6 Multiple regression91
  • Example: Explaining house prices92
  • Exercise 6.192
  • Example: The capital asset pricing model (continued from page 83)92
  • Regression as a best fitting line93
  • Ordinary least squares estimation of the multiple regression model93
  • Statistical aspects of multiple regression94
  • Interpreting OLS estimates95
  • Example: Explaining house prices (continued from page 92)95
  • Table 6.1 Regression of house price on lot size, number of bedrooms, number of bathrooms and number of storeys.*96
  • Example: The capital asset pricing model (continued from page 93)98
  • Table 6.2 Regression results for the CAPM example.98
  • Pitfalls of using simple regression in a multiple regression context98
  • Table 6.3 Regression of sale price on number of bedrooms.99
  • Table 6.4 Correlation matrix of variable in house price example.99
  • Omitted variables bias100
  • Exercise 6.2101
  • Multicollinearity102
  • Example: The effect of interest rates on the exchange rate102
  • Example: Multicollinearity illustrated using artificial data103
  • Table 6.5 Regression results using artificial data.103
  • Table 6.6 Regression results using artificial data omitting X2.104
  • Exercise 6.3105
  • Chapter summary105
  • Appendix 6.1 : Mathematical interpretation of regression coefficients105
  • Endnotes106
  • CHAPTER 7 Regression with dummy variables109
  • Example: The determinants of market capitalization109
  • Example: Explaining house prices110
  • Exercise 7.1111
  • Simple regression with a dummy variable112
  • Example: Explaining house prices (continued from page 111)112
  • Table 7.1 Regression of house prices on air conditioning dummy.112
  • Example: The determinants of market capitalization (continued from page 110)113
  • Table 7.2 Regression of market capitalization on SEO.113
  • Multiple regression with dummy variables114
  • Example: Explaining house prices (continued from page 113)114
  • Table 7.3 Regression of house price on driveway and recreation room dummies.114
  • Exercise 7.2115
  • Exercise 7.3115
  • Exercise 7.4116
  • Multiple regression with both dummy and non-dummy explanatory variables116
  • Example: Explaining house prices (continued from page 115)117
  • Example: Explaining house prices (continued from page 117)118
  • Exercise 7.5119
  • Example: The determinants of market capitalization (continued from page 114)119
  • Table 7.4 Regression of house price on ASSETS and SEO.119
  • Interacting dummy and non-dummy variables120
  • Example: Explaining house prices (continued from page 118)120
  • Exercise 7.6121
  • Exercise 7.7121
  • What if the dependent variable is a dummy?121
  • Chapter summary122
  • Endnote122
  • CHAPTER 8 Regression with lagged explanatory variables123
  • Aside on lagged variables125
  • Table 8.1 Creating lagged variables.127
  • Aside on notation127
  • Example: Long–run prediction of a stock market price index128
  • Table 8.2 Regression results for the long–run prediction of stock returns example.130
  • Example: The effect of bad news on market capitalization130
  • Table 8.3 Regression results for the effect of news on market capitalization example.131
  • Exercise 8.1132
  • Selection of lag order132
  • Example: The effect of bad news on market capitalization (continued from page 132)134
  • Table 8.4 Lag length set equal to 3.134
  • Exercise 8.2134
  • Chapter summary135
  • Endnotes135
  • CHAPTER 9 Univariate time series analysis137
  • Example: Stock prices on the NYSE137
  • Fig. 9.1 Log of stock price index.138
  • Fig. 9.2 Stock price return.139
  • Exercise 9.1139
  • The autocorrelation function140
  • Exercise 9.2140
  • Aside141
  • Example: Stock prices on the NYSE (continued from page 139)141
  • Table 9.1 Autocorrelation functions.142
  • Fig. 9.3 Autocorrelation function for stock prices.142
  • Fig. 9.4 Autocorrelation function for stock returns.143
  • Exercise 9.3143
  • The autoregressive model for univariate time series144
  • Fig. 9.5 AR(1) time series with ϕ = 0.145
  • Fig. 9.6 AR(1) time series with ϕ = 0.8.145
  • Fig. 9.7 AR(1) time series with ϕ = 1.146
  • Exercise 9.4146
  • Nonstationary versus stationary time series146
  • Example: Market efficiency and the random walk hypothesis148
  • Example: Stock prices on the NYSE (continued from page 143)148
  • Exercise 9.5148
  • Extensions of the AR(1) model149
  • Fig. 9.8 Trend stationary series.150
  • Exercise 9.6151
  • Example: Stock prices on the NYSE (continued from page 148)152
  • Table 9.2 AR(4) with deterministic trend model.152
  • Testing in the AR (p) with deterministic trend model152
  • Testing involving α, γ1, …, γp -1, and δ152
  • Example: Stock prices on the NYSE (continued from page 152)153
  • Table 9.3 AR(1) model.154
  • Testing involvingρ154
  • Example: Long–term interest rates156
  • Table 9.4 AR(1) model.156
  • Exercise 9.7156
  • Exercise 9.8157
  • Exercise 9.9157
  • Chapter summary158
  • Appendix 9.1 : Mathematical intuition for the AR(1) model159
  • Endnotes160
  • CHAPTER 10 Regression with time series variable161
  • Time series regression when X and Y are stationary162
  • Example: The effect of financial liberalization on economic growth164
  • Table 10.1 ADL(2,2) with deterministic trend model.165
  • Exercise 10.1166
  • Exercise 10.2166
  • Aside for Excel users166
  • Time series regression when Y and X have unit roots: spurious regression167
  • Time series regression when Y and X have unit roots: cointegration167
  • Example: Cointegration between the spot and forward rates.170
  • Estimation and testing with cointegrated variables170
  • Fig. 10.1 Spot and forward rates171
  • Example: Cointegration between the spot and forward rates (continued from page 170)172
  • Table 10.2 AR(1) using the errors from the cointegrating regression.173
  • Exercise 10.3173
  • Exercise 10.4174
  • Time series regression when Y and X are cointegrated: the error correction model174
  • Example: Cointegration between the spot and forward rates (continued from page 173)176
  • Table 10.3 Simple error correction model.176
  • Exercise 10.5177
  • Exercise 10.6177
  • Time series regression when Y and X have unit roots but are not cointegrated177
  • Exercise 10.7178
  • Chapter summary179
  • Endnotes180
  • CHAPTER 11 Regression with time series variables with several equations183
  • Granger causality184
  • Granger causality in a simple ADL model185
  • Granger causality in an ADL model with p and q lags185
  • Example: Do stock price movements in Country A Granger cause stock price movements in Country B?186
  • Table 11.1 ADL model using stock returns in Country A as the dependent variable.187
  • Causality in both directions187
  • Example: Do stock price movements in Country B Granger cause stock price movements in Country A?188
  • Table 11.2 ADL model using stock returns in Country B as the dependent variable.188
  • Exercise 11.1188
  • Exercise 11.2189
  • Granger causality with cointegrated variables189
  • Exercise 11.3190
  • Vector autoregressions190
  • Example: What moves the stock and bond markets?192
  • Exercise 11.4194
  • Table 11.3 Estimates from a VAR(1) with er,r,dy,s,dp and rb as dependent variables (P-values in parentheses).194
  • Lag length selection in VARs195
  • Table 11.4 Information criteria for VAR(p) for different lag lengths.196
  • Exercise 11.5196
  • Exercise 11.6196
  • Forecasting with VARs196
  • Exercise 11.7198
  • Exercise 11.8199
  • Vector autoregressions with cointegrated variables199
  • Example: Consumption, aggregate wealth and expected stock returns201
  • Table 11.5 Johansen test for cointegration using cay data.201
  • Exercise 11.9202
  • Exercise 11.10202
  • Exercise 11.11203
  • Chapter summary203
  • Appendix 11.1 : Hypothesis tests involving more than one coefficient204
  • Table 11.6 Critical values for F-test if T - k is large.205
  • Table 11.7 Critical values for F-test if T - k is 40.206
  • Example: Do stock returns in Country A Granger cause stock returns in Country B? (continued from page 187)206
  • Appendix 11.2 : Variance decompositions207
  • Endnotes209
  • CHAPTER 12 Financial volatility211
  • Volatility in asset prices: Introduction212
  • Example: Volatility in stock prices214
  • Fig. 12.1 Log of stock price214
  • Fig. 12.2 Percentage change in stock price.215
  • Table 12.1 AR(1) model using volatility as variable of interest.215
  • Fig. 12.3 Volatility of stock price.216
  • Exercise 12.1217
  • Autoregressive conditional heteroskedasticity (ARCH)217
  • Example: Volatility in stock prices (continued from page 216)218
  • Table 12.2 ARCH(1) model using stock returns data.218
  • Table 12.3 ARCH(2) model using stock returns data.219
  • Example: Volatility in stock prices (continued from page 219)220
  • Table 12.4 GARCH(1, 1) model using stock returns data.220
  • Exercise 12.2221
  • Exercise 12.3221
  • Chapter summary222
  • Endnotes222
  • Back Matter223
  • Appendix A Writing an empirical project223
  • Description of a typical empirical project223
  • General considerations225
  • Appendix B Data directory227
Book details
  • Vendor Wiley Global Education UK
  • SKU 0-470-06303-3
  • ISBN-13 9780470063033
  • Author Gary Koop
  • Edition 1st
  • Category Business & Economics
  • Subject Finance

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Description eBook delivery & access Reviews
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.