| Recent studies of long memory processes suggested the existence of long-range dependencies in volatilities of many financial assets. This led to the introduction of a new process called Fractionally Integrated Generalized AutoRegressive Conditional Heteroskedasticity (FIGARCH). The primary purpose of this new process is to capture the observed long-memory in financial market volatility. In this study, the conditional volatilities of daily spot and futures rate series of four different foreign currencies are estimated by using this new FIGARCH model. The empirical results suggest the existence of long memory in foreign exchange market volatility. When compared with GARCH and IGARCH models, FIGARCH model which implies a slow decay of a shock to the optimal forecast of the future conditional variance performs better in describing the foreign exchange market volatility. Then this study extends univariate FIGARCH process to a bivariate setting and investigates its performance in making dynamic hedging decisions. The ex-post estimation results show that FIGARCH process improves the hedging effectiveness for the German Mark, the Swiss Franc, and the Japanese Yen. The ex-ante estimation results, however, show that less sophisticated GARCH process yields higher risk reduction than FIGARCH model. |
