Predicting expected idiosyncratic volatility: Empirical evidence from ARFIMA, HAR, and EGARCH models

Chuxuan Xiao (), Winifred Huang () and David P. Newton ()
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Chuxuan Xiao: Swansea University
Winifred Huang: University of Bath
David P. Newton: University of Bath

Review of Quantitative Finance and Accounting, 2024, vol. 63, issue 3, No 7, 979-1006

Abstract: Abstract We investigate the performances of the ARFIMA, HAR, and EGARCH models in capturing the time-varying property of idiosyncratic volatility (IVOL). We find that the expected IVOL predictions by HAR are superior. In diverse portfolio scenarios, a greater degree of judgment is required to assess the pricing ability of expected IVOLs. For the lowest value-weighted quintiles and the expected IVOL estimated by the HAR model, the IVOL-return relationship is negative. Conversely, the IVOL-return relationship is positive for the expected IVOL estimated by the EGARCH model. Further evidence suggests a complicated and mixed relationship between the expected IVOL estimated by the ARFIMA model and stock returns.

Keywords: Asset Pricing; Idiosyncratic volatility; Time-varying; ARFIMA; HAR; EGARCH (search for similar items in EconPapers)
JEL-codes: C53 G12 G17 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11156-024-01279-z

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