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

Zhenlong Chen, Junjie Liu and Xiaozhen Hao

Finance Research Letters, 2024, vol. 64, issue C

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.

Keywords: ASHARV-MIDAS model; Current return information; Long memory; Volatility forecasting (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:64:y:2024:i:c:s1544612324004501

DOI: 10.1016/j.frl.2024.105420

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