High†Frequency Exchange Rate Forecasting

Predictability of exchange rate movement is of great interest to both practitioners and regulators. We examine the predictability of exchange rate movement in the high†frequency domain. To this end, we apply a model designed for modelling high†frequency and irregularly spaced data, the autoregressive conditional multinomial–autoregressive conditional duration (ACM–ACD) model. Studying three pairs of currencies, we find strong predictability in the high†frequency quote change data, with the rate of correct predictions varying from 54 to 70%. We demonstrate that filtering the data, by increasing the threshold of mid†quote price change, in combination with dynamic learning, can improve forecasting performance.