Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Singh, Aryan; Singh, Monisha; Lakshmi, R. Title A name given to the resource Impact of Macroeconomic Integration in Hybrid GARCH-GRU Volatility Modelling on Nifty Bank Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 4th International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2025;pp.406-411 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICAAIC64647.2025.11330787" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICAAIC64647.2025.11330787</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105034847411?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105034847411?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant 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 Description An account of the resource 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. Subject The topic of the resource Banking sector volatility; Emerging markets; Financial risk management; GARCH models; GRU networks; Hybrid forecasting models; Macroeconomic indicators; Volatility prediction Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833156587-9; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online