This study is motivated by the problem of missing data and its consequences on accuracy of the parameters estimation and the problem of reliability of output when missing data is used as input. Precisely, the test was done on data set of non-euro currency time series of Poland, Slovakia, UK and Russia (European countries that have not adopted the euro).

Several methods of filling missing historical data were used and their imputation accuracy was compared. It was examined efficiency of imputation of: simple interpolation method, regression analysis, Principal Component Analysis (PCA) and the Expectation Maximization (EM) algorithm.

It was found that for the periods and the data series analysed, linear interpolation (for univariate series) and PCA (for multivariate series) outperformed the other methodologies.

Out performance of naïve approach such as linear interpolation for univariate series, might speak about the quality of the data and the market from which data is coming. This confirms the intuition, that in the illiquid markets, market returns exhibits autocorrelation and follow some interpolated pattern.

Furthermore, for the multivariate series, it was found that the accuracy of the imputation depends on the strength of the correlation between currencies.