Implied volatility sentiment: a tale of two tails

Luiz Félix*, Roman Kräussl, Philip Stork

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

6 Downloads (Pure)

Abstract

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.

Original languageEnglish
Pages (from-to)823-849
Number of pages27
JournalQuantitative Finance
Volume20
Issue number5
Early online date29 Jan 2020
DOIs
Publication statusPublished - 3 May 2020

Keywords

  • Equity-risk premium
  • Implied volatility
  • Machine learning
  • Predictability
  • Reversals
  • Sentiment

Fingerprint Dive into the research topics of 'Implied volatility sentiment: a tale of two tails'. Together they form a unique fingerprint.

Cite this