Exploring the Nonlinear Idiosyncratic Volatility Puzzle: Evidence from China

B. Li, Sabri Boubaker, Z. Liu, W. Louhichi and Y. Yao

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Abstract: This paper studies the spectrum of the idiosyncratic volatility (IVOL) puzzle in the Chinese A-share market using functional data analysis (FDA). It highlights a nonlinear IVOL puzzle with a steady reduction in the bottom 20% of average returns and a large drop of 1% in the top 10%, consistent with the herding, certainty, and reflection effects in China's A-share markets. Furthermore, empirical evidence suggests that the FDA technique has a 30% greater goodness of fit than linear regressions, suggesting that nonlinearity plays a non-negligible role in the IVOL puzzle. These results can be useful for investors and hedgers, as they show that stock returns decline accelerated as the IVOL increases. \textcopyright 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Keywords: China's A-share markets; Functional data analysis; Idiosyncratic volatility puzzle; Portfolio-based approach (search for similar items in EconPapers)
Date: 2023
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Published in Computational Economics, 2023, 62 (2), pp.527-559. ⟨10.1007/s10614-022-10265-3⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04435519

DOI: 10.1007/s10614-022-10265-3

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