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

Author

Listed:
  • Chuxuan Xiao

    (Swansea University)

  • Winifred Huang

    (University of Bath)

  • David P. Newton

    (University of Bath)

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.

Suggested Citation

  • Chuxuan Xiao & Winifred Huang & David P. Newton, 2024. "Predicting expected idiosyncratic volatility: Empirical evidence from ARFIMA, HAR, and EGARCH models," Review of Quantitative Finance and Accounting, Springer, vol. 63(3), pages 979-1006, October.
    • Handle: RePEc:kap:rqfnac:v:63:y:2024:i:3:d:10.1007_s11156-024-01279-z
    DOI: 10.1007/s11156-024-01279-z
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    Keywords

    Asset Pricing; Idiosyncratic volatility; Time-varying; ARFIMA; HAR; EGARCH;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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