{"id":378,"date":"2026-06-09T12:51:05","date_gmt":"2026-06-09T12:51:05","guid":{"rendered":"https:\/\/www.journals.utgjiu.ro\/JES\/?post_type=articol&#038;p=378"},"modified":"2026-06-09T12:51:05","modified_gmt":"2026-06-09T12:51:05","slug":"a-risk-aware-hybrid-ensemble-approach-for-aex-index-forecasting-integrating-aparch-t-volatility-with-lstm-cnn-rf-architectures","status":"publish","type":"articol","link":"https:\/\/www.journals.utgjiu.ro\/JES\/articol\/a-risk-aware-hybrid-ensemble-approach-for-aex-index-forecasting-integrating-aparch-t-volatility-with-lstm-cnn-rf-architectures\/","title":{"rendered":"A Risk-Aware Hybrid Ensemble Approach for AEX Index Forecasting: Integrating APARCH-t Volatility with LSTM-CNN-RF Architectures"},"content":{"rendered":"<p>The accurate prediction of financial time-series remains a formidable challenge due to inherent non-linearity,<br \/>\nheteroskedasticity, and the presence of &#8220;fat-tailed&#8221; distributions. This study proposes a novel, risk-augmented hybrid<br \/>\nensemble framework designed to enhance the forecasting precision of the Amsterdam Exchange (AEX) index.<br \/>\nDeparting from conventional monolithic models, the research methodology integrates an asymmetric power<br \/>\nautoregressive conditional heteroskedasticity (APARCH) model with a Student-t distribution to extract robust volatility<br \/>\nfeatures. These econometric inputs are subsequently fed into a tripartite deep learning ensemble comprising Long<br \/>\nShort-Term Memory (LSTM) networks for temporal dependencies, Convolutional Neural Networks (CNN) for spatial<br \/>\nfeature extraction, and Random Forest (RF) for non-linear regression refinement.<br \/>\nEmpirical results demonstrate that the proposed architecture significantly outperforms baseline models, achieving a<br \/>\nhigh predictive accuracy characterized by an $R^{2}$ of 0.9408 and a Mean Absolute Error (MAE) of 4.9542. A<br \/>\ncritical finding of this research is the significance of the leptokurtic nature of AEX returns (Kurtosis: 12.64); by<br \/>\nanchoring the machine learning engine with APARCH-derived conditional volatility, the model effectively mitigates the<br \/>\nimpact of market noise and transient shocks. Furthermore, Value-at-Risk (VaR) backtesting validates the model\u2019s<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}}},"class_list":["post-378","articol","type-articol","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/www.journals.utgjiu.ro\/JES\/wp-json\/wp\/v2\/articol\/378","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.journals.utgjiu.ro\/JES\/wp-json\/wp\/v2\/articol"}],"about":[{"href":"https:\/\/www.journals.utgjiu.ro\/JES\/wp-json\/wp\/v2\/types\/articol"}],"wp:attachment":[{"href":"https:\/\/www.journals.utgjiu.ro\/JES\/wp-json\/wp\/v2\/media?parent=378"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}