The aim of this book is to provide the researcher in financial markets with the techniques necessary to undertake the empirical analysis of financial time series. To accomplish this aim we introduce and develop both univariate modelling techniques and multivariate methods, including those regression techniques for time series that seem to be particularly relevant to the finance area.

Why do we concentrate exclusively on time series techniques when, for example, cross-sectional modelling plays an important role in empirical investigations of the Capital Asset Pricing Model (CAPM): see, as an early and influential example, Fama and MacBeth (1973)? Our answer is that, apart from the usual considerations of personal expertise and interest, plus manuscript length considerations, it is because time series analysis, in both its theoretical and empirical aspects, has been for many years an integral part of the study of financial markets, with empirical research beginning with the papers by Working (1934), Cowles (1933, 1944) and Cowles and Jones (1937).

Working focused attention on a previously noted characteristic of commodity and stock prices: namely, that they resemble cumulations of purely random changes. Cowles investigated the ability of market analysts and financial services to predict future price changes, finding that there was little evidence that they could. Cowles and Jones reported evidence of positive correlation between successive price changes but, as Cowles (1960) was later to remark, this was probably due to their taking monthly averages of daily or weekly prices before computing changes: a ‘spurious correlation’ phenomenon analysed by Working (1960).