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Improving Volatility Forecasts Using Market‐Elicited Ambiguity Aversion Information

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  • Raymond H.Y. So
  • Tarik Driouchi

Abstract

Distinguishing between risk and uncertainty, this paper proposes a volatility forecasting framework that incorporates asymmetric ambiguity shocks in the (exponential) generalized autoregressive conditional heteroskedasticity‐in‐mean conditional volatility process. Spanning 25 years of daily data and considering the differential role of investors' ambiguity attitudes in the gain and loss domains, our models capture a rich set of information and provide more accurate volatility forecasts both in‐sample and out‐of‐sample when compared to ambiguity‐free or risk‐based counterparts. Ambiguity‐based volatility‐timing trading strategies confirm the economic significance of our proposed framework and indicate that an annualized excess return of 3.2% over the benchmark could be earned from 1995 to 2014.

Suggested Citation

  • Raymond H.Y. So & Tarik Driouchi, 2018. "Improving Volatility Forecasts Using Market‐Elicited Ambiguity Aversion Information," The Financial Review, Eastern Finance Association, vol. 53(4), pages 705-740, November.
    • Handle: RePEc:bla:finrev:v:53:y:2018:i:4:p:705-740
    DOI: 10.1111/fire.12172
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    Cited by:

    1. Chen, Qiang & Han, Yu, 2023. "Options market ambiguity and its information content," Journal of Financial Markets, Elsevier, vol. 64(C).

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