The information content of option-implied information for volatility forecasting with investor sentiment

Sung Won Seo and Jun Sik Kim

Journal of Banking & Finance, 2015, vol. 50, issue C, 106-120

Abstract: This study explores the effect of investor sentiment on the volatility forecasting power of option-implied information. We find that the risk-neutral skewness has the explanatory power regarding future volatility only during high sentiment periods. Furthermore, the implied volatility has varying volatility forecasting ability depending on the level of investor sentiment. Our findings suggest that the effectiveness of volatility forecasting models based on option-implied information varies over time with the level of investor sentiment. We confirm the important role of investor sentiment in volatility forecasting models exploiting option-implied information with strong evidence from in-sample and out-of-sample analyses. We also present improvements in the accuracy of volatility forecasts from volatility forecasting models derived by incorporating investor sentiment in these models.

Keywords: Investor sentiment; Risk-neutral skewness; Implied volatility; Volatility forecasting (search for similar items in EconPapers)
JEL-codes: C13 C22 C53 G14 (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (41)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378426614003100
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:jbfina:v:50:y:2015:i:c:p:106-120

DOI: 10.1016/j.jbankfin.2014.09.010

Access Statistics for this article

Journal of Banking & Finance is currently edited by Ike Mathur

More articles in Journal of Banking & Finance from Elsevier
Bibliographic data for series maintained by Catherine Liu ().