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Assessing Dynamic Hedge Effectiveness and Statistical Arbitrage with Conventional and Deep Learning Models in Equity Futures

by Komal Batool, Zaara Asim Mirza, Ammar Sheikh, Hanadi Sabir, Hussain Aftab
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 57
Year of Publication: 2025
Authors: Komal Batool, Zaara Asim Mirza, Ammar Sheikh, Hanadi Sabir, Hussain Aftab
10.5120/ijca2025925919

Komal Batool, Zaara Asim Mirza, Ammar Sheikh, Hanadi Sabir, Hussain Aftab . Assessing Dynamic Hedge Effectiveness and Statistical Arbitrage with Conventional and Deep Learning Models in Equity Futures. International Journal of Computer Applications. 187, 57 ( Nov 2025), 38-63. DOI=10.5120/ijca2025925919

@article{ 10.5120/ijca2025925919,
author = { Komal Batool, Zaara Asim Mirza, Ammar Sheikh, Hanadi Sabir, Hussain Aftab },
title = { Assessing Dynamic Hedge Effectiveness and Statistical Arbitrage with Conventional and Deep Learning Models in Equity Futures },
journal = { International Journal of Computer Applications },
issue_date = { Nov 2025 },
volume = { 187 },
number = { 57 },
month = { Nov },
year = { 2025 },
issn = { 0975-8887 },
pages = { 38-63 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number57/assessing-dynamic-hedge-effectiveness-and-statistical-arbitrage-with-conventional-and-deep-learning-models-in-equity-futures/ },
doi = { 10.5120/ijca2025925919 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-11-18T21:11:12.108710+05:30
%A Komal Batool
%A Zaara Asim Mirza
%A Ammar Sheikh
%A Hanadi Sabir
%A Hussain Aftab
%T Assessing Dynamic Hedge Effectiveness and Statistical Arbitrage with Conventional and Deep Learning Models in Equity Futures
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 57
%P 38-63
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This study advances the frontier of financial risk management by rigorously comparing conventional econometric models with modern deep learning approaches for dynamic hedging in equity futures. Using weekly data from October 2019 to June 2024 on the KSE-30 Index and its futures, the authors of this study examine whether established techniques such as DCC-GARCH and GARCH-Copula can match the adaptability and predictive strength of advanced architectures, including LSTM–CNN hybrids and the FT-Net Hybrid. Optimal hedge ratios are estimated on a dynamic basis, with performance assessed through variance reduction, RMSE, Sharpe ratios, hedge effectiveness, and directional accuracy along with 4 other metrics. Beyond risk mitigation, the study extends and applies the Fuzzy TOPSIS framework for optimal model selection and testing statistical arbitrage opportunities between the models. The results highlight the transformative potential of deep learning in capturing complex market dynamics that traditional models often overlook, offering actionable insights for traders, portfolio managers, and policymakers in emerging markets.

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Index Terms

Computer Science
Information Sciences

Keywords

Volatility Modelling Hybrid Deep Learning Models Fourier Transform in Finance Multivariate GARCH Models Fuzzy TOPSIS Quantitative Trading

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