Modelling and forecasting long memory in exchange rate volatility vs. stable and integrated GARCH models

Abstract

The purpose of this article is to compare stable, integrated and long-memory generalized autoregressive conditional heteroscedasticity (GARCH) models in forecasting the volatility of returns in the Turkish foreign exchange market for the period 1990-2005 and for the subperiod that covers the floating exchange rate regime 2001-2005. In the first period, we found that long-memory GARCH specifications capture the temporal pattern of volatility for returns in US and Canadian dollars against Turkish lira. For the same period, the temporal pattern of volatility for returns Australian dollar, Japanese yen, Euro and British pound against Turkish lira are best captured by stable GARCH specifications. We found that in the subperiod, only the stable GARCH models are relevant and the return series no longer exhibit the long-memory properties. It was also concluded that all return series except British pound against Turkish Lira have asymmetric effects. Our analysis has shown that when long memory, asymmetry and power terms in the conditional variance are employed, together with the skewed and leptokurtic conditional distribution (of innovations), the most accurate out-of-sample volatility is produced for the first and subperiod. Thus is useful for financial decisions which utilize such forecasts.