An Online Vector Error Correction Model for Exchange Rates Forecasting

Abstract

Financial time series are known for their non-stationary behaviour. However, sometimes they exhibit some stationary linear combinations. When this happens, it is said that those time series are cointegrated.The Vector Error Correction Model (VECM) is an econometric model which characterizes the joint dynamic behaviour of a set of cointegrated variables in terms of forces pulling towards equilibrium. In this study, we propose an Online VEC model (OVECM) which optimizes how model parameters are obtained using a sliding window of the most recent data. Our proposal also takes advantage of the long-run relationship between the time series in order to obtain improved execution times. Our proposed method is tested using four foreign exchange rates with a frequency of 1-minute, all related to the USD currency base. OVECM is compared with VECM and ARIMA models in terms of forecasting accuracy and execution times. We show that OVECM outperforms ARIMA forecasting and enables execution time to be reduced considerably while maintaining good accuracy levels compared with VECM.