Improving Volatility Forecasts Using Market‐Elicited Ambiguity Aversion Information

TY - JOUR

T1 - Improving Volatility Forecasts Using Market‐Elicited Ambiguity Aversion Information

AU - So, Raymond H.Y.

AU - Driouchi, Tarik

PY - 2018/11

Y1 - 2018/11

N2 - 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.

AB - 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.

U2 - 10.1111/fire.12172

DO - 10.1111/fire.12172

M3 - Article

SN - 1540-6288

VL - 53

SP - 705

EP - 740

JO - Financial Review

JF - Financial Review

IS - 4

ER -