Forecasting Daily Forex Using Large Dimensional Vector Autoregression with Time-Varying Parameters

Abstract

Econometricians have been intensively developing tools to forecast both in economic and financial data. Despite that forecasting foreign exchange rate pairs are still problematic. In addition, predictors included in equation are not informative enough in predicting forex pairs. To remedy this obstacle, we apply Vector Autoregression (VAR) using the method socalled “Dynamic Model Averaging” to obtain estimates of the parameters. Up to 25 forex pairs are used in the DMA estimation procedures. We forecast EUR-GBP, EUR-JPY, EUR-USD, AUD-CAD, AUD-CHF and AUD-JPY with the number of horizons from $h=1$ to $h=14$ or one-day-ahead through fourteen-day-ahead prediction. We develop two model specifications in this study. The findings are: first, the Large-VAR with time-varying parameters performs well in predicting EUR-USD and AUD-JPY. Secondly, the rest of forex pairs are not well predicted using the proposed algorithm. Finally, relatively speaking, large size VARs is better in forecasting all selected forex pairs due to the more flexibility of time-varying coefficients and optimal forgetting factor. In addition, the forex market is efficient enough that using only the advanced time-series models such as Large time-varying VARs cannot confirm the profit from trading.