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Stock Market Anomalies

Student: Alexander Valkovskiy

Supervisor: Victoria V. Dobrynskaya

Faculty: Faculty of Economic Sciences

Educational Programme: Economics (Bachelor)

Year of Graduation: 2016

The presented work aims to analyze Russian stock market for presence of calendar anomalies for average daily data of Moscow Stock Exchange Index. We thus provide an empirical examination of "day of the week'' and ''month of the year'' effects using a range of autoregressive conditional heteroscedasticity models (GARCH, TGARCH, EGARCH). The results of our research are mixed and stand for different degree of anomalies existence in the market for differing periods of time. For instance, we found that ''Monday effect'' was peculiar to the default and post-default time intervals.

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