A Large Dimensional VAR Model with Time-Varying Parameters for Daily Forex Forecasting

Abstract

Economic and financial data is extremely volatile relative to the others especially in the time series data. Foreign Exchange market or forex data is one among the others. Despite the fact that it is still impossible to guarantee the profits from trading using advanced mathematical model for this time series, but the predictive performance via the mean squares forecasting error and mean absolute forecasting error obtained from new techniques recently is very much improved. In this paper, we apply vector autoregression (VAR) to forecast most traded forex pairs according to dailyfx.com. The parameters obtained in our work are extraordinary where we use the algorithm so-called dynamic model averaging (DMA) and dynamic model selection (DMS) to deal with the model uncertainty. This algorithm is based closely on Kalman Filtering which has a huge advantage when compared with Markov chain Monte Carlo method. We are able to perform up to 27 variables in VAR and set the lag of 14 periods. We forecast EUR-USD, GBP-USD and EUR-JPY with the number of horizon from $$h=1$$ to $$h=14$$ or one-day-ahead through fourteen-day-ahead prediction where each predicted value is obtained via iterated forecasting method. The findings in this work are: first, DMA algorithm, the Large-VAR with time-varying parameters perform well in predicting GBP-USD. EUR-USD and EUR-JPY. In addition, DMS, an algorithm that selects the highest model probability in Bayesian model averaging obviously outperforms DMA method. Secondly, by having algorithm which is able to track the degree of changes in each dimension for VAR via forgetting factors, the larger matrix in data usage results in more time variation degree in VAR parameters. Furthermore, we also illustrate the dynamic Minnesota prior for each point in time. We finally found that the proposed method delivered excellent predictive performance for large time-varying parameter VAR up to 27 variables.