Data-driven adaptive hybrid models for exchange rate return forecasting

Exchange-rate return forecasting is challenging because financial time series may exhibit linearity, nonlinearity, regime-switching behavior, and volatility. To address these complexities, two adaptive hybrid forecasting frameworks were developed: ATW-HyF A, which dynamically combines ARIMA, SETAR, and ANN forecasts using inverse-variance weighting, and ATW-HyF B, which extends the framework by incorporating a GARCH(1,1) volatility layer to model the conditional variance of forecast errors.

The robustness of the proposed frameworks was assessed using simulated data and three foreign exchange return series: USD/NGN, EUR/USD, and GBP/USD. Forecast performance was evaluated within a rolling-origin forecasting framework and compared with competing forecasting models.

The simulation results showed that the ATW-HyF models achieved the lowest forecast errors among the competing models. However, their performance on the empirical exchange-rate series was mixed, with other models achieving lower forecast errors for some currency pairs. In particular, the ARIMA-ANN hybrid performed best for USD/NGN and EUR/USD, whereas ATW-HyF B performed best for GBP/USD.

The findings indicate that forecasting performance depends on the characteristics of the underlying exchange-rate series rather than on model complexity alone. Combining models with complementary predictive strengths can improve forecasting performance, particularly when the data exhibit linear, nonlinear, and volatility-related patterns. The incorporation of adaptive weighting and volatility modeling therefore provides a flexible approach to exchange-rate return forecasting under evolving market conditions.