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Blending Econometric and Deep Learning Approaches for Enhanced Volatility Forecasting of the KSE-100 Index

by Ubaida Fatima, Rimsha Zafar, Syeda Arsala Shah, Abdus Samad
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 35
Year of Publication: 2025
Authors: Ubaida Fatima, Rimsha Zafar, Syeda Arsala Shah, Abdus Samad
10.5120/ijca2025925503

Ubaida Fatima, Rimsha Zafar, Syeda Arsala Shah, Abdus Samad . Blending Econometric and Deep Learning Approaches for Enhanced Volatility Forecasting of the KSE-100 Index. International Journal of Computer Applications. 187, 35 ( Aug 2025), 36-44. DOI=10.5120/ijca2025925503

@article{ 10.5120/ijca2025925503,
author = { Ubaida Fatima, Rimsha Zafar, Syeda Arsala Shah, Abdus Samad },
title = { Blending Econometric and Deep Learning Approaches for Enhanced Volatility Forecasting of the KSE-100 Index },
journal = { International Journal of Computer Applications },
issue_date = { Aug 2025 },
volume = { 187 },
number = { 35 },
month = { Aug },
year = { 2025 },
issn = { 0975-8887 },
pages = { 36-44 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number35/blending-econometric-and-deep-learning-approaches-for-enhanced-volatility-forecasting-of-the-kse-100-index/ },
doi = { 10.5120/ijca2025925503 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-08-31T01:36:52.334035+05:30
%A Ubaida Fatima
%A Rimsha Zafar
%A Syeda Arsala Shah
%A Abdus Samad
%T Blending Econometric and Deep Learning Approaches for Enhanced Volatility Forecasting of the KSE-100 Index
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 35
%P 36-44
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The objective of this study is to develop a hybrid forecasting model that combines statistical and machine learning techniques to predict stock market volatility in Pakistan. The dataset used spans from January 2019 to December 2023, and the model's accuracy is evaluated using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). Traditional forecasting models often struggle to account for market uncertainty due to external economic factors, such as IMF policies and currency fluctuations. By utilising non-linear techniques, including Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), Linear Regression, and Generalised Autoregressive Conditional Heteroscedasticity (GARCH), this study aims to enhance volatility predictions, thereby enabling quicker and more informed investment decisions.

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Index Terms

Computer Science
Information Sciences

Keywords

Long Short-Term Memory (LSTM) Generalised Autoregressive Conditional Heteroscedasticity (GARCH) Autoregressive Integrated Moving Average (ARIMA) Mean Absolute Error (MAE) Root Mean Squared Error (RMSE).

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