Abstract. In our paper we use data mining to compare the volatility structure of high (daily) and low (weekly, monthly) frequencies for seven Romanian companies traded on Bucharest Stock Exchange and three market indices, during 1997-2012. For each of the 10 time series and three frequencies we fit a GARCH-in-mean model and we find that persistency is more present in the daily returns as compared with the weekly and monthly series. On the other hand, the GARCH-in-mean failed to confirm (on our data) the theoretical hypothesis that an increase in volatility leads to a rise in future returns, mainly because the variance coefficient from the mean equation of the model was not statistically significant for most of the time series analyzed and on most of the frequencies. The diagnosis that we ran in order the verify the goodness of fit for the model showed that GARCH-in-mean was well fitted on the weekly and monthly time series but behaved less well on the daily time series.

Keywords: stock returns; volatility; persistence; GARCH model; emerging markets; data mining.