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This study examines global shocks and the volatility of the Russian rubble/United States dollar exchange rate using the symmetric Generalized Autoregressive Conditional Heteroscedasticity (GARCH), and Asymmetric Power Autoregressive Conditional Heteroscedasticity (APARCH) models. The GARCH and APARCH are employed under normal (Normal Gaussian) and non-normal (Student’s t and Generalized Error) distributions. Using monthly exchange rate data covering the period January 1994December 2013, the study finds that the symmetric (GARCH) model has the best fit under the non-normal distribution, which improves the overall estimation for measuring conditional variance. Conversely, the APARCH model does not show asymmetric response in exchange rate volatility and global shocks, resulting in no presence of leverage effect. The GARCH model under the Student’s t distribution produces better fit for estimating exchange rate volatility and global shocks in Russia compared to the APARCH model.

Exchange rate volatility and global shocks in Russia: an application of GARCH and APARCH models