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Modeling and forecasting multifractal volatility established upon the heterogeneous market hypothesis

Author

Listed:
  • Tao, Qizhi
  • Wei, Yu
  • Liu, Jiapeng
  • Zhang, Ting
Abstract
We construct a new type of multifractal volatility models based on heterogeneous market hypothesis. Similar to the model setting of heterogeneous autoregressive model for realized volatility (HAR-RV), we replace the variables utilized in the HAR-RV model with daily, weekly and monthly multifractal volatility. To evaluate the performance of our new multifractal volatility models, we compare the volatility forecasting accuracy of our models to that of other traditional benchmarks. The model confidence set (MCS) test shows that, although the autoregressive fractionally integrated moving average models for realized volatility (ARFIMA-RV) are the best forecasting ones, under several loss functions, our new multifractal volatility models outperform other traditional ones. In addition, our new models survive the MCS test in many cases.

Suggested Citation

  • Tao, Qizhi & Wei, Yu & Liu, Jiapeng & Zhang, Ting, 2018. "Modeling and forecasting multifractal volatility established upon the heterogeneous market hypothesis," International Review of Economics & Finance, Elsevier, vol. 54(C), pages 143-153.
    • Handle: RePEc:eee:reveco:v:54:y:2018:i:c:p:143-153
    DOI: 10.1016/j.iref.2017.08.003
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    4. Chao Liang & Yongan Xu & Zhonglu Chen & Xiafei Li, 2023. "Forecasting China's stock market volatility with shrinkage method: Can Adaptive Lasso select stronger predictors from numerous predictors?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(4), pages 3689-3699, October.
    5. Lin, Tiantian & Liu, Dehong & Zhang, Lili & Lung, Peter, 2019. "The information content of realized volatility of sector indices in China’s stock market," International Review of Economics & Finance, Elsevier, vol. 64(C), pages 625-640.
    6. Yuan, Ying & Zhang, Tonghui, 2020. "Forecasting stock market in high and low volatility periods: a modified multifractal volatility approach," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    7. Liu, Yuntong & Wei, Yu & Wang, Qian & Liu, Yi, 2022. "International stock market risk contagion during the COVID-19 pandemic," Finance Research Letters, Elsevier, vol. 45(C).
    8. Yu Wei & Lan Bai & Kun Yang & Guiwu Wei, 2021. "Are industry‐level indicators more helpful to forecast industrial stock volatility? Evidence from Chinese manufacturing purchasing managers index," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(1), pages 17-39, January.
    9. Cui, Yan & Feng, Yun, 2020. "Composite hedge and utility maximization for optimal futures hedging," International Review of Economics & Finance, Elsevier, vol. 68(C), pages 15-32.
    10. Xun Huang & Huiyue Tang, 2022. "Measuring multi‐volatility states of financial markets based on multifractal clustering model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 422-434, April.
    11. Ma, Feng & Zhang, Yaojie & Huang, Dengshi & Lai, Xiaodong, 2018. "Forecasting oil futures price volatility: New evidence from realized range-based volatility," Energy Economics, Elsevier, vol. 75(C), pages 400-409.

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    More about this item

    Keywords

    Realized volatility; Multifractal volatility; HAR-RV; ARFIMA-RV; MCS test;
    All these keywords.

    JEL classification:

    • G1 - Financial Economics - - General Financial Markets
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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