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Forecasting intraday volatility and VaR using multiplicative component GARCH model

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  • Xundi Diao
  • Bin Tong

Abstract

We use the multiplicative component GARCH model (mcsGARCH) to decompose the volatility of high-frequency returns of CSI 300 index into three components, namely the daily, the diurnal and the stochastic intraday volatilities. As expected, the diurnal volatility features an important intraday seasonality. Surprisingly due to the unique 'T + 1 trading rule' in Chinese stock market, the diurnal volatility of the 5-minute returns of CSI 300 index does not show a U-shaped pattern as in European and American stock markets. Moreover, we investigate the out-of-sample performance of the mcsGARCH model in forecasting the intraday volatility of the CSI 300 index. The results show that the mcsGARCH model performs well in Chinese stock market.

Suggested Citation

  • Xundi Diao & Bin Tong, 2015. "Forecasting intraday volatility and VaR using multiplicative component GARCH model," Applied Economics Letters, Taylor & Francis Journals, vol. 22(18), pages 1457-1464, December.
    • Handle: RePEc:taf:apeclt:v:22:y:2015:i:18:p:1457-1464
    DOI: 10.1080/13504851.2015.1039696
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    Cited by:

    1. Aneessa Firdaus Jumoorty & Ruben Thoplan & Jason Narsoo, 2023. "High frequency volatility forecasting: A new approach using a hybrid ANN‐MC‐GARCH model," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(4), pages 4156-4175, October.
    2. Zhuwei Li & Xuejiao Lu & Yuan Fu, 2022. "Interaction influence of trading rules on the quality of stock markets: the price limit rule and day trading rule from the Shanghai and Shenzhen Stock exchanges," Applied Economics, Taylor & Francis Journals, vol. 54(56), pages 6467-6479, December.
    3. Ravi Summinga-Sonagadu & Jason Narsoo, 2019. "Risk Model Validation: An Intraday VaR and ES Approach Using the Multiplicative Component GARCH," Risks, MDPI, vol. 7(1), pages 1-23, January.

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