Impact of Macroeconomic Integration in Hybrid GARCH-GRU Volatility Modelling on Nifty Bank

Title

Impact of Macroeconomic Integration in Hybrid GARCH-GRU Volatility Modelling on Nifty Bank

Creator

Singh, Aryan; Singh, Monisha; Lakshmi, R.

Description

In countries like India, where banking systems are closely tied to macroeconomic swings, being able to forecast volatility is critical for managing financial risk. Sudden changes in interest rates, exchange movements, or growth expectations can unsettle banks much faster than in mature markets. Econometric tools such as the Generalised Autoregressive Conditional Heteroskedasticity (GARCH) model remain popular because they capture volatility clustering well, but they fall short when the data exhibit nonlinear patterns. Neural networks-particularly Gated Recurrent Units (GRUs)-handle time-series dynamics more effectively, though they tend to miss traits specific to financial volatility. In this work, we put forward a hybrid GARCH-GRU framework that blends the traditional strengths of econometric models with the pattern-learning ability of neural networks, while also folding in key macroeconomic indicators. The framework is applied to the Nifty Bank index and draws on daily records spanning March 2010 to December 2022. Altogether, the dataset includes just over three thousand observations, covering more than a decade of varied market conditions. The framework uses a two-step design: conditional volatility from a GJR-GARCH(1,1) model is first estimated and then used as input, along with macroeconomic variables such as repo rates, exchange rates (USD/INR, CNY/INR, EUR/INR), oil prices, and GDP growth, for the GRU network. Our results indicate that the hybrid model performs noticeably better, cutting the Mean Absolute Error by about a quarter. The error falls from 0.000263 in the baseline GARCH model to 0.000199 under the hybrid design. Among the different factors considered, movements in exchange rates and changes in repo rates stand out most strongly, showing how these macroeconomic signals feed directly into risk management for Indian banks. 2025 IEEE.

Source

4th International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2025;pp.406-411

Date

01-01-2025

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Banking sector volatility; Emerging markets; Financial risk management; GARCH models; GRU networks; Hybrid forecasting models; Macroeconomic indicators; Volatility prediction

Coverage

Singh A., Christ University, Dept. of Statistics and Data Science, Bengaluru, India; Singh M., Christ University, Dept. of Statistics and Data Science, Bengaluru, India; Lakshmi R., Christ University, Dept. of Statistics and Data Science, Bengaluru, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISBN: 979-833156587-9;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/ICAAIC64647.2025.11330787

https://www.scopus.com/pages/publications/105034847411?origin=resultslist