Professor Brooks’ book provides extraordinarily comprehensive treatment of econometric techniques with application to Finance. The unique feature of this book is the presentation of rich real-world case study examples. This is an ideal text book for MS in Finance, MBA with concentration in Finance and Seniors majoring in Finance. It is also an ideal text book for financial professional training and self-study.'

Chris Brooks is Professor of Finance at the ICMA Centre, University of Reading, UK, where he also obtained his PhD. He has published over sixty articles in leading academic and practitioner journals including the Journal of Business, the Journal of Banking and Finance, the Journal of Empirical Finance, the Review of Economics and Statistics and the Economic Journal. He is an associate editor of a number of journals including the International Journal of Forecasting. He has also acted as consultant for various banks and professional bodies in the fields of finance, econometrics and real estate. where: Rt denotes the simple return at time t, rt denotes the continuously compounded return at time t, pt denotes the asset price at time t, and ln denotes the natural logarithm. If the asset under consideration is a stock or portfolio of stocks, the total return to holding it is the sum of the capital gain and any dividends paid during the holding period. However, researchers often ignore any dividend payments. This is unfortunate, and will lead to an underestimation of the total returns that accrue to investors. This is likely to be negligible for very short holding periods, but will have a severe impact on cumulative returns over investment horizons of several years. Ignoring dividends will also have a distortionary effect on the crosssection of stock returns. For example, ignoring dividends will imply that ‘growth’ stocks, with large capital gains will be inappropriately favoured over income stocks (e.g. utilities and mature industries) that pay high dividends. Is financial econometrics different from ‘economic econometrics’? As previously stated, the tools commonly used in financial applications are fundamentally the same as those used in economic applications, although the emphasis and the sets of problems that are likely to be encountered when analysing the two sets of data are somewhat different. Financial data often differ from macroeconomic data in terms of their frequency, accuracy, seasonality and other properties. In economics, a serious problem is often a lack of data at hand for testing the theory or hypothesis of interest -- this is often called a ‘small samples problem’. It might be, for example, that data are required on government budget deficits, or population figures, which are measured only on an annual basis. If the methods used to measure these quantities changed a quarter of a century ago, then only at most twenty-five of these annual observations are usefully available. Two other problems that are often encountered in conducting applied econometric work in the arena of economics are those of measurement error and data revisions. These difficulties are simply that the data may be estimated, or measured with error, and will often be subject to several vintages of subsequent revisions. For example, a researcher may estimate an economic model of the effect on national output of investment in computer technology using a set of published data, only to find that the

Frequency distribution of t-ratios of mutual fund alphas (gross of transactions costs) Source: Jensen (1968). Reprinted with the permission of Blackwell Publishers 2.18 Frequency distribution of t-ratios of mutual fund alphas (net of transactions costs) Source: Jensen (1968). Reprinted with the permission of Blackwell Publishers 2.19 Performance of UK unit trusts, 1979--2000 3.1 R 2 = 0 demonstrated by a flat estimated line, i.e. a zero slope coefficient 3.2 R 2 = 1 when all data points lie exactly on the estimated line 4.1 Effect of no intercept on a regression line 4.2 Graphical illustration of heteroscedasticity 4.3 Plot of uˆ t against uˆ t−1 , showing positive autocorrelation 4.4 Plot of uˆ t over time, showing positive autocorrelation 4.5 Plot of uˆ t against uˆ t−1 , showing negative autocorrelation 4.6 Plot of uˆ t over time, showing negative autocorrelation 4.7 Plot of uˆ t against uˆ t−1 , showing no autocorrelation 4.8 Plot of uˆ t over time, showing no autocorrelation 4.9 Rejection and non-rejection regions for DW test List of figures List of tables List of boxes List of screenshots Preface to the second edition Acknowledgements Forecasting covariances and correlations Covariance modelling and forecasting in finance: some examples Historical covariance and correlation Implied covariance models Exponentially weighted moving average model for covariances Multivariate GARCH models A multivariate GARCH model for the CAPM with time-varying covariances 8.28 Estimating a time-varying hedge ratio for FTSE stock index returns 8.29 Estimating multivariate GARCH models using EViews Appendix: Parameter estimation using maximum likelihood

Conducting empirical research or doing a project or dissertation in finance 13.1 What is an empirical research project and what is it for? 13.2 Selecting the topic 13.3 Sponsored or independent research? 13.4 The research proposal 13.5 Working papers and literature on the internet 13.6 Getting the dataIn the limit, as the frequency of the sampling of the data is increased so that they are measured over a smaller and smaller time interval, the simple and continuously compounded returns will be identical. Motivations for the first edition This book had its genesis in two sets of lectures given annually by the author at the ICMA Centre (formerly ISMA Centre), University of Reading and arose partly from several years of frustration at the lack of an appropriate textbook. In the past, finance was but a small sub-discipline drawn from economics and accounting, and therefore it was generally safe to

The value of econometrics page 2 Time series data 4 Log returns 8 Points to consider when reading a published paper 11 1.5 Features of EViews 21 2.1 Names for y and xs in regression models 28 2.2 Reasons for the inclusion of the disturbance term 30 2.3 Assumptions concerning disturbance terms and their interpretation 44 2.4 Standard error estimators 48 2.5 Conducting a test of significance 56 2.6 Carrying out a hypothesis test using confidence intervals 60 2.7 The test of significance and confidence interval approaches compared 61 2.8 Type I and type II errors 64 2.9 Reasons for stock market overreactions 71 2.10 Ranking stocks and forming portfolios 72 2.11 Portfolio monitoring 72 3.1 The relationship between the regression F-statistic and R 2 111 3.2 Selecting between models 117 4.1 Conducting White’s test 134 4.2 ‘Solutions’ for heteroscedasticity 138 4.3 Conditions for DW to be a valid test 148 4.4 Conducting a Breusch--Godfrey test 149 4.5 The Cochrane--Orcutt procedure 151 The data required may be available electronically through a financial information provider, such as Reuters or from published government figures. Alternatively, the required data may be available only via a survey after distributing a set of questionnaires i.e. primary data. Step 3: choice of estimation method relevant to the model proposed in step 1 For example, is a single equation or multiple equation technique to be used? Step 4: statistical evaluation of the model What assumptions were required to estimate the parameters of the model optimally? Were these assumptions satisfied by the data or the model? Also, does the model adequately describe the data? If the answer is ‘yes’, proceed to step 5; if not, go back to steps 1--3 and either reformulate the model, collect more data, or select a different estimation technique that has less stringent requirements. Step 5: evaluation of the model from a theoretical perspective Are the parameter estimates of the sizes and signs that the theory or intuition from step 1 suggested? If the answer is ‘yes’, proceed to step 6; if not, again return to stages 1--3. Step 6: use of model When a researcher is finally satisfied with the model, it can then be used for testing the theory specified in step 1, or for formulating forecasts or suggested courses of action. This suggested course of action might be for an individual (e.g. ‘if inflation and GDP rise, buy stocks in sector X’), or as an input to government policy (e.g. ‘when equity markets fall, program trading causes excessive volatility and so should be banned’). Introduction This chapter sets the scene for the book by discussing in broad terms the questions of what is econometrics, and what are the ‘stylised facts’ describing financial data that researchers in this area typically try to capture in their models. It also collects together a number of preliminary issues relating to the construction of econometric models in finance.there is an ever greater need for a textbook like this that applies relevant econometric topics to the field of finance. The book explains difficult concepts in a clear and easily understandable way, with plenty of real-world practical illustrations. A particularly welcome feature, and extremely helpful to students, is the use of examples with computer printouts on how to estimate models using the Eviews software. I highly recommend it.' econometrics, but which also covered more recently developed approaches usually found only in more advanced texts To use examples and terminology from finance rather than economics since there are many introductory texts in econometrics aimed at students of economics but none for students of finance To litter the book with case studies of the use of econometrics in practice taken from the academic finance literature To include sample instructions, screen dumps and computer output from two popular econometrics packages. This enabled readers to see how the techniques can be implemented in practice To develop a companion web site containing answers to end-of-chapter questions, PowerPoint slides and other supporting materials. Cross-sectional data Cross-sectional data are data on one or more variables collected at a single point in time. For example, the data might be on: ● A poll of usage of Internet stockbroking services ● A cross-section of stock returns on the New York Stock Exchange