Work

Modeling Stock Market Volatility of HDFC Bank Through Time Series Techniques

Year: 2025

Type: article

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.

Source: International Journal of Accounting and Economics Studies

DOI: 10.14419/0jnkf755

Authors: Vijaya Lakshmi Yalamanchali, Dr. K. Hema Divya, Dr. N. Chandan Babu

Institution: Koneru Lakshmaiah Education Foundation

Language: English

Topic: Financial Risk and Volatility Modeling

Subfield: Finance

Field: Economics, Econometrics and Finance

Domain: Social Sciences

Open Access status: bronze

OpenAlex ID: W4414564674

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