Exploring the Nonlinear Idiosyncratic Volatility Puzzle: Evidence from China

Bo Li, Sabri Boubaker, Zhenya Liu (), Waël Louhichi and Yao Yao
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Bo Li: Beijing International Studies University
Zhenya Liu: Renmin University of China
Waël Louhichi: ESSCA School of Management
Yao Yao: University of Birmingham

Computational Economics, 2023, vol. 62, issue 2, No 3, 527-559

Abstract: 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.

Keywords: Idiosyncratic volatility puzzle; Portfolio-based approach; Functional data analysis; China’s A-share markets (search for similar items in EconPapers)
JEL-codes: C31 C32 C51 G12 (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10614-022-10265-3

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