High‐Frequency Exchange Rate Forecasting
研究了高频汇率变动的可预测性，应用ACM-ACD模型分析三组货币对，发现正确预测率在54%到70%之间，并证明通过数据过滤和动态学习可提升预测效果。
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.