Modeling Stock Market Volatility of HDFC Bank Through Time Series Techniques

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.