Forecasting exchange rates using cointegration models and intra-day data


We present a cointegration analysis on the triangle (USD–DEM, USD–JPY, DEM–JPY) of foreign exchange rates using intra-day data. A vector autoregressive model is estimated and evaluated in terms of out-of-sample forecast accuracy measures. Its economic value is measured on the basis of trading strategies that account for transaction costs. We show that the typical seasonal volatility in high-frequency data can be accounted for by transforming the underlying time scale. Results are presented for the original and the modified time scales. We find that utilizing the cointegration relation among the exchange rates and the time scale transformation improves forecasting results.

Journal of Forecasting, 21(3)
Adrian Trapletti
Adrian Trapletti

Quant, software engineer, and consultant mostly investment industry. Long-term contributor and package author R Project for Statistical Computing.