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September 28, 2025Open Access

Modeling Stock Market Volatility of HDFC Bank Through Time Series Techniques

VYVijaya Lakshmi Yalamanchali KDK. Hema DivyaKoneru Lakshmaiah Education Foundation NBN. Chandan BabuSwami Vivekanand College of Pharmacy

Key Points

GARCH outperforms other models in forecasting stock market volatility.
Traditional econometric methods like ARIMA and GARCH are compared to advanced machine learning techniques like LSTM.
Volatility forecasts affect key aspects of financial reporting and risk management.
The findings underscore the importance of interdisciplinary approaches to volatility modeling in finance.

Abstract

This research investigates HDFC Bank’s stock market volatility using ARIMA, ARCH, GARCH, RNN, and LSTM models. The study evaluates predictive accuracy and explores implications for accounting and finance. Traditional econometric models (ARIMA, ARCH, GARCH) are compared with advanced machine learning methods (RNN, LSTM). Results show that GARCH provides superior forecasting accuracy. From an accounting perspective, volatility forecasts influence financial reporting, capital allocation, and risk disclosure. Findings highlight the interdisciplinary role of volatility modeling in finance and accounting.

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