Modeling Stock Market Volatility of HDFC Bank Through Time Series Techniques

  • Authors

    • Vijaya Lakshmi Yalamanchali Research Scholar, KL Business School, Koneru Lakshmaiah Education Foundation (Deemed to be University), Guntur District, Andhra Pradesh
    • Dr. K. Hema Divya Associate Professor, KL Business School, Koneru Lakshmaiah Education Foundation (Deemed to be University), Guntur District, Andhra Pradesh
    • Dr. N. Chandan Babu Assistant Professor, Department of Mathematics and Statistics, Bhavan’s Vivekananda College of Science, Humanities and Commerce, Secunderabad, Telangana
    https://doi.org/10.14419/0jnkf755

    Received date: July 31, 2025

    Accepted date: September 16, 2025

    Published date: September 27, 2025

  • ARIMA, ARCH, GARCH, RNN, LSTM, HDFC, Volatility, Forecasting, Accounting

  • 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.

  • References

    1. Lin, Z. (2018). Modelling and forecasting the stock market volatility of SSE Composite Index using GARCH models. Future Generation Computer Systems, 79, 960-972.
    2. Sharma, R., & Mehta, K. (2024). Stock Market Predictions Using Deep Learning: Developments and Future Research Directions. Deep Learning Tools for Predicting Stock Market Movements, 89-121.
    3. Vitali, G. (2019). Forecasting Stock Index Volatility: A comparison between GARCH and LSTM models.
    4. Raju, V. V. R. (2022). Stock Price Volatility Modeling and Forecasting of Nifty 50 Companies in India. IJMER, 11(2), 1-10.
    5. Nikkam, D., & Panduranga, V. (2016). Analyzing and Forecasting of Select Indian Banking Stocks Volatility. Gavesana Journal of Management, 8(1/2), 51-65.
    6. Bhattacharjee, S. (2023). Volatility Analysis of Indian Banking Sector using Bollinger Bands.
    7. Recent interdisciplinary accounting-finance studies (2023–2024) on volatility forecasting and financial reporting.

  • Downloads

  • How to Cite

    Yalamanchali , V. L. ., Divya , D. K. H. ., & Babu , D. N. C. . (2025). Modeling Stock Market Volatility of HDFC Bank Through Time Series Techniques. International Journal of Accounting and Economics Studies, 12(5), 1070-1072. https://doi.org/10.14419/0jnkf755