A Risk-Aware Hybrid Ensemble Approach for AEX Index Forecasting: Integrating APARCH-T Volatility with LSTM-CNN-RF Architectures

The accurate prediction of financial time-series remains a formidable challenge due to inherent non-linearity, heteroskedasticity, and the presence of "fat-tailed" distributions. This study proposes a novel, risk-augmented hybrid ensemble framework designed to enhance the forecasting precision of the Amsterdam Exchange (AEX) index. Departing from conventional monolithic models, the research methodology integrates an asymmetric power autoregressive conditional heteroskedasticity (APARCH) model with a Student-t distribution to extract robust volatility features. These econometric inputs are subsequently fed into a tripartite deep learning ensemble comprising Long Short-Term Memory (LSTM) networks for temporal dependencies, Convolutional Neural Networks (CNN) for spatial feature extraction, and Random Forest (RF) for non-linear regression refinement. Empirical results demonstrate that the proposed architecture significantly outperforms baseline models, achieving a high predictive accuracy characterized by an R² of 0.9408 and a Mean Absolute Error (MAE) of 4.9542. A critical finding of this research is the significance of the leptokurtic nature of AEX returns (Kurtosis: 12.64); by anchoring the machine learning engine with APARCH-derived conditional volatility, the model effectively mitigates the impact of market noise and transient shocks. Furthermore, Value-at-Risk (VaR) backtesting validates the model's reliability for risk management, revealing that actual violations (181) remained well below the theoretical expectations (249.4) at a 95% confidence interval. The study concludes with a 30-day forward volatility projection, offering actionable insights for institutional investors and policy-makers during periods of market transition.