Predicting Financial Market Volatility Using Regression and Machine Learning Techniques

Title

Predicting Financial Market Volatility Using Regression and Machine Learning Techniques

Creator

Chaudhari, Mahek; John, Nimitha

Description

In standard Simple Linear Regression (SLR), one of the major assumptions is that the error terms have constant variance (homoscedasticity). However, this assumption is frequently violated in many real-world datasets, resulting in inefficient estimates and reduced predictive accuracy. To overcome this shortcoming, we propose a hybrid modeling platform that combines SLR with statistical and machine learning methods. The approach starts with SLR to identify the main linear relationship. Whenever residual diagnostics report the presence of heteroskedasticity, an Autoregressive Conditional Heteroskedasticity (ARCH) model is used to estimate time-varying variance. Such estimated variances are utilized in a Weighted Generalized Least Squares (WGLS) model, which stabilizes the error structure. Finally, to capture any remaining nonlinear patterns, an Artificial Neural Network (ANN) is applied on the residuals of the WGLS model. By layering these techniques, the hybrid framework improves both stability and predictive power. Simulation studies and empirical tests on Apple Inc. stock data confirmed that the hybrid framework yields reduced MAE and RMSE values and greater explanatory strength than individual approaches. 2025 IEEE.

Source

2025 IEEE 1st International Conference on Innovations in Engineering and Next-Generation Technologies for Sustainability, ICINVENTS 2025;

Date

01-01-2025

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Artificial Neural Networks (ANN); Autoregressive Conditional Heteroskedasticity (ARCH); Hybrid Modeling; Regression Models; Time Series; Weighted Generalized Least Squares (WGLS)

Coverage

Chaudhari M., CHRIST (Deemed to be University), India; John N., CHRIST (Deemed to be University), India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISBN: 979-833155662-4;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/ICINVENTS64613.2025.11401501

https://www.scopus.com/pages/publications/105036987499?origin=resultslist