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Can asymmetry, long memory, and current return information improve crude oil volatility prediction? ——Evidence from ASHARV-MIDAS model

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  • Chen, Zhenlong
  • Liu, Junjie
  • Hao, Xiaozhen

Abstract

We propose an ASHARV-MIDAS model that incorporates the asymmetric and long-memory characteristics of financial asset returns, while integrating current return information into the volatility equation to enhance prediction accuracy. Additionally, we derive the lag order expression and conditional variance of short-term volatility in the novel model to analyze its distinction from the classical GARCH-MIDAS model that does not consider current return information. Empirical and robustness tests demonstrate superior in-sample parameter estimation performance and more precise out-of-sample volatility prediction capabilities of our proposed model.

Suggested Citation

  • Chen, Zhenlong & Liu, Junjie & Hao, Xiaozhen, 2024. "Can asymmetry, long memory, and current return information improve crude oil volatility prediction? ——Evidence from ASHARV-MIDAS model," Finance Research Letters, Elsevier, vol. 64(C).
    • Handle: RePEc:eee:finlet:v:64:y:2024:i:c:s1544612324004501
    DOI: 10.1016/j.frl.2024.105420
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    References listed on IDEAS

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