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This paper builds on the ARCH approach for modeling distributions with time-varying conditional variance by using the generalized Student t distribution. The distribution offers flexibility in modeling both leptokurtosis and asymmetry (characteristics seen in high-frequency financial time series data), nests the standard normal and Student t distributions, and is related to the Gram Charlier and mixture distributions. An empirical ARCH model based on this distribution is formulated and estimated using hourly exchange rate returns for four currencies. The generalized Student t is found to better model the empirical conditional and unconditional distributions than other distributional specifications.

Foreign Exchange --- Money and Monetary Policy --- Econometric and Statistical Methods: General --- Econometric Modeling: General --- Monetary Policy --- Monetary Systems --- Standards --- Regimes --- Government and the Monetary System --- Payment Systems --- Monetary economics --- Currency --- Foreign exchange --- Exchange rates --- Standing facilities --- Currencies --- Exchange rate modelling --- Monetary policy --- Money --- United States