TY - JOUR
T1 - Implied volatility sentiment
T2 - a tale of two tails
AU - Félix, Luiz
AU - Kräussl, Roman
AU - Stork, Philip
PY - 2020/5/3
Y1 - 2020/5/3
N2 - We propose a sentiment measure jointly derived from out-of-the-money index puts and single stock calls: implied volatility (IV-) sentiment. In contrast to implied correlations, our measure uses information from the tails of the risk-neutral densities from these two markets rather than across their entire moneyness structures. We find that IV-sentiment measure adds value over and above traditional factors in predicting the equity risk premium out-of-sample. Forecasting results are superior when constrained ensemble models are used vis-à-vis unregularized machine learning techniques. In a mean-reversion strategy, our IV-sentiment measure delivers economically significant results, with limited exposure to a set of cross-sectional equity factors, including Fama and French's five factors, the momentum factor and the low-volatility factor, and seems valuable in preventing momentum crashes. Our novel measure reflects overweight of tail events, which we interpret as a behavioral bias. However, we cannot rule out a risk-compensation rationale.
AB - We propose a sentiment measure jointly derived from out-of-the-money index puts and single stock calls: implied volatility (IV-) sentiment. In contrast to implied correlations, our measure uses information from the tails of the risk-neutral densities from these two markets rather than across their entire moneyness structures. We find that IV-sentiment measure adds value over and above traditional factors in predicting the equity risk premium out-of-sample. Forecasting results are superior when constrained ensemble models are used vis-à-vis unregularized machine learning techniques. In a mean-reversion strategy, our IV-sentiment measure delivers economically significant results, with limited exposure to a set of cross-sectional equity factors, including Fama and French's five factors, the momentum factor and the low-volatility factor, and seems valuable in preventing momentum crashes. Our novel measure reflects overweight of tail events, which we interpret as a behavioral bias. However, we cannot rule out a risk-compensation rationale.
KW - Equity-risk premium
KW - Implied volatility
KW - Machine learning
KW - Predictability
KW - Reversals
KW - Sentiment
UR - http://www.scopus.com/inward/record.url?scp=85078861813&partnerID=8YFLogxK
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U2 - 10.1080/14697688.2019.1696018
DO - 10.1080/14697688.2019.1696018
M3 - Article
AN - SCOPUS:85078861813
SN - 1469-7688
VL - 20
SP - 823
EP - 849
JO - Quantitative Finance
JF - Quantitative Finance
IS - 5
ER -