The next step was to check if the log-returns posses serial correlation. By means of Ljung-Box test performed on squared log-returns for 20 lags it was concluded that all 3 log-return series do posses serial correlation at 1% significance level. Insted of performing a test for stationarity of given time series, it was decided to directly fit ARIMA model because it can make non-stationary time series stationary through differencing, as a result providing so called Integrated time series. It is a common appearance that the variance of stock return time series changes due to trends or seasonalities. Such variability is called Heteroscedasticity [1]. And most of the time Heteroscedasticity appears to be serially correlated and therefore, conditional on periods of increased variance. That is why such time series are referred as conditionally heteroscedastic. In order to capture these trends GARCH model was fitted. Parameters of ARIMA(p,d,q)-GARCH(m,n) are estimated by numerically optimizing the likelihood function. Number of lags p,d,q to be used in a model are determined by fitting ARIMA(p,d,q) models where p and q ranged from 1 to 4, and d ranged from 0 to 2. The best fit was determined by AIC criterion. On the residuals of appropriate ARIMA models, GARCH models were fitted using "tseries" library in R. As a result models in Table 3 were chosen.