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Forecasting Daily Stock Volatility Using GARCH Model: A Comparison Between BSE and SSE

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  • Sasikanta Tripathy
  • Abdul Rahman

Abstract

Modeling and forecasting the volatility of stock markets has been one of the major topics in financial econometrics in recent years. Based on the daily closing value of 23 years data, an average of 5,605 observations, for both Sensex and Shanghai Stock Exchange Composite Index, this paper makes an attempt to fit appropriate GARCH model to estimate the conditional market volatility for both Bombay Stock Exchange (BSE) and Shanghai Stock Exchange (SSE), respectively. The empirical results demonstrate that there are significant ARCH effects in both the stock markets, and it is appropriate to use the GARCH model to estimate the process.

Suggested Citation

  • Sasikanta Tripathy & Abdul Rahman, 2013. "Forecasting Daily Stock Volatility Using GARCH Model: A Comparison Between BSE and SSE," The IUP Journal of Applied Finance, IUP Publications, vol. 19(4), pages 71-83, October.
    • Handle: RePEc:icf:icfjaf:v:19:y:2013:i:4:p:71-83
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    Cited by:

    1. Kolte, Ashutosh & Roy, Jewel Kumar & Vasa, László, 2023. "The impact of unpredictable resource prices and equity volatility in advanced and emerging economies: An econometric and machine learning approach," Resources Policy, Elsevier, vol. 80(C).
    2. Jordan Ngu Chuan Yong & Sayyed Mahdi Ziaei & Kenneth R. Szulczyk, 2021. "The Impact of Covid-19 Pandemic on Stock Market Return Volatility: Evidence from Malaysia and Singapore," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 11(3), pages 191-204, March.
    3. Riko Hendrawan, 2023. "Comparison of Black-Scholes and Garch Option Models on The Kompas100 Index With a Long Straddle Strategy During 2008-2021 ," GATR Journals jfbr208, Global Academy of Training and Research (GATR) Enterprise.
    4. N. Suresh & N. R. Bharathi, 2022. "Effect of Demonetisation of on Indian High Denomination Currencies on Indian Stock Market and its Relationship with Foreign Exchange Rate," Papers 2207.06963, arXiv.org.
    5. Tamal Datta Chaudhuri & Indranil Ghosh, 2016. "Artificial Neural Network and Time Series Modeling Based Approach to Forecasting the Exchange Rate in a Multivariate Framework," Papers 1607.02093, arXiv.org.
    6. Yee-Fan Tan & Lee-Yeng Ong & Meng-Chew Leow & Yee-Xian Goh, 2021. "Exploring Time-Series Forecasting Models for Dynamic Pricing in Digital Signage Advertising," Future Internet, MDPI, vol. 13(10), pages 1-24, September.

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