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Keywords:

  • foreign exchange;
  • high-frequency data;
  • forecasting;
  • duration model

Abstract

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.