Modeling Stock Market Volatility using ARCH and GARCH models in Indian Markets.

Nimmala Sree Anuj; Kongonda Manish; Mohammed Anas; K Shalini

doi:10.36948/ijfmr.2026.v08i03.75211

Modeling Stock Market Volatility using ARCH and GARCH models in Indian Markets.

Author(s)	Mr. Nimmala Sree Anuj, Mr. Kongonda Manish, Mr. Mohammed Anas, Mr. K Shalini
Country	India
Abstract	In this paper, we introduce Andy-Volatility, a full-stack financial analytics module, and a fundamental part of the Andy Terminal, a multi-module, intelligent trading and analytics platform. Andy-Volatility uses automated BUY, SELL, HOLD, and CAUTION trading signals of equities listed on the Bombay Stock Exchange (BSE) and the National Stock Exchange (NSE) in India by using ARMA(2,3) mean-equation modelling with GJR-GARCH (Threshold GARCH) variance-equations estimation. The system retrieves ten years of past close prices of the day, using Yahoo Finance, does joint ARMA-TGARCH estimation, and predicts conditional volatility five days ahead of the trade. Signal thresholds are computed as the ratio of forecast to current conditional volatility, and enhanced with the leverage effect (parameter of gamma) to indicate asymmetric shock sensitivity in stocks. A backtesting engine tests the volatility based strategy against a Buy-and-Hold comparison over the same historical period. Under the Andy Terminal brand, it combines with other analytical modules via a common Flask back-end, real-time WebSocket price feeds, a rolling 52-week volatility heatmap, news-sentiment fusion scoring via VADER NLP, an administrator-only paper trading module, and a user management system based on SQLite and role-based access control. Experimental evidence on 30 BSE and 31 NSE large-cap stocks shows that the TGARCH model is able to accurately determine high-persistence volatility regimes and produce actionable information with quantifiable risk-adjusted added value to passive investment. The system provides sub-second per-stock analysis latency, and has been proven on the entire Sensex and Nifty-50 universes of constituents
Keywords	Keywords: GARCH, TGARCH, GJR-GARCH, ARMA, Stock Market Volatility, Trading Signals, BSE, NSE, Sentiment Analysis, Paper Trading, Flask, Financial Analytics, Leverage Effect, Volatility Forecasting.
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 8, Issue 3, May-June 2026
Published On	2026-05-05
DOI	https://doi.org/10.36948/ijfmr.2026.v08i03.75211

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Modeling Stock Market Volatility using ARCH and GARCH models in Indian Markets.

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