Modeling asymmetric volatility with long memory effect using a FIGARCH ANN approach: evidence from ANTM stock

Abstract

This study models the volatility of daily returns for PT Aneka Tambang Tbk (ANTM), which exhibits volatility clustering, fat tails, asymmetry, and long memory. The analysis proceeds in two stages: (i) conditional variance modeling using FIGARCH (1, d, 1) to represent long-memory dynamics; and (ii) design of a FIGARCH ANN hybrid (backpropagation) to absorb residual nonlinearity/asymmetry. Preprocessing tests confirm stationarity in log returns, followed by ARIMA baseline selection and confirmation of conditional heteroskedasticity in the residuals. Long-memory estimation via the GPH procedure confirms statistically significant long memory (past shocks have persistent effects). Compared to GARCH (1,1) and EGARCH, FIGARCH provides a better fit because it has the smallest AIC/BIC value. Residual diagnostics for FIGARCH are clean, indicating no autocorrelation and no remaining ARCH effects the model captures the main volatility structure. Ten steps ahead forecasts show the conditional variance stabilizing, implying that shocks decay slowly (persistence) toward a relatively stable level. The ANN component trained on residuals/logvariance reduces error metrics (MSE/RMSE/MAE) compared with standalone FIGARCH, evidencing the benefit of nonlinear correction for short horizon accuracy.