; financial volatility ; forecasting ; explanatory modelling ; exchange rates …

forecast nonlinear ARMA model based simulated data and real data of financial returns. The forecasting ability of the recurrent …, recurrent artificial neural network based ARMA model and feed-forward SVR based ARMA model) by using two forecasting accuracy … evaluation metrics (NSME and sign) and robust Diebold–Mariano test. The results reveal that for one-step-ahead forecasting, the …

’ profitability forecasting, techniques, and most appropriate models to improve the correctness of predicting and acquiring more … forecasting. It conducts investigations for the relevant studies, using regression analysis, necessary tests, ascertains …

competing models. We show that the forecasting gains translate into economically and statistically significant (risk …

variancefunctions. In a genuine out-of-sample forecasting experiment theperformance of the best fitted asMA-asQGARCH model is compared … topure asMA and no-change forecasts. This is done both in terms ofconditional mean forecasting as well as in terms of risk … forecasting. …

forecasting volatility model with the most appropriate error distribution. The results suggest the presence of leverage effect … forecasting model that could guarantee a sound policy decisions. …

Recurrent Support Vector Regression for a Nonlinear ARMA Model with Applications to Forecasting Financial Returns … to forecast nonlinear ARMA model based simulated data and real data of financial returns. The forecasting ability of the … focus on fit in-sample, but SVR considers both fit and forecast out-of-sample which endows SVR with an excellent forecasting …