No abstract

No abstract

Terence Mills' best-selling graduate textbook provides detailed coverage of research techniques and findings relating to the empirical analysis of financial markets. In its previous editions it has become required reading for many graduate courses on the econometrics of financial modelling. This third edition, co-authored with Raphael Markellos, contains a wealth of material reflecting the developments of the last decade. Particular attention is paid to the wide range of nonlinear models that are used to analyse financial data observed at high frequencies and to the long memory characteristics found in financial time series. The central material on unit root processes and the modelling of trends and structural breaks has been substantially expanded into a chapter of its own. There is also an extended discussion of the treatment of volatility, accompanied by a new chapter on nonlinearity and its testing.

No abstract

No abstract

This paper studies calendar effects in the emerging Athens Stock Exchange. Rather than examining only basket indices, we analyse calendar effects for each of the constituent stocks of the Athens Stock Exchange General Index for the period from October 1986 to April 1997. In accordance with similar studies substantial evidence of 'day-of-the week', 'monthly', 'trading month' and 'holiday' effects are found. The intensity of these effects for various stocks on the basis of capitalization, beta coefficients and company type are examined. The results indicate that the calendar regularities vary significantly across the constituent shares of the General Index and that aggregation introduces a considerable bias in unravelling these regularities. Also, it is found that factors such as the beta coefficient and company type influence significantly the intensity of calendar effects.

No abstract

Abstract. This paper investigates the empirical association between stock market volatility and investor mood-proxies related to the weather (cloudiness, temperature and precipitation) and the environment (nighttime length). Overall, our results suggest that cloudiness and length of nighttime are inversely related to historical, implied and realized measures of volatility. The strength of association seems to vary with the location of an exchange on Earth with respect to the equator. Weather deviations from seasonal norms and dummies representing extreme weather conditions do not offer additional explanatory power in our datasets. JEL Classification: G14, G32Keywords: Stock market anomalies, Volatility, Sunshine effect, SAD effect, Behavioral Acknowledgements: We would like to thank Mark Kamstra, David Hirshleifer and an anonymous referee for providing us with valuable comments and suggestions which significantly improved the final version of the paper. We assume responsibility for any remaining errors.