News implied volatility and disaster concerns
Asaf Manela and
Alan Moreira
Journal of Financial Economics, 2017, vol. 123, issue 1, 137-162
Abstract:
We construct a text-based measure of uncertainty starting in 1890 using front-page articles of the Wall Street Journal. News implied volatility (NVIX) peaks during stock market crashes, times of policy-related uncertainty, world wars, and financial crises. In US postwar data, periods when NVIX is high are followed by periods of above average stock returns, even after controlling for contemporaneous and forward-looking measures of stock market volatility. News coverage related to wars and government policy explains most of the time variation in risk premia our measure identifies. Over the longer 1890–2009 sample that includes the Great Depression and two world wars, high NVIX predicts high future returns in normal times and rises just before transitions into economic disasters. The evidence is consistent with recent theories emphasizing time variation in rare disaster risk as a source of aggregate asset prices fluctuations.
Keywords: Text-based analysis; Implied volatility; Rare disasters; Equity premium; Return predictability; Machine learning (search for similar items in EconPapers)
JEL-codes: C82 E44 G12 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (251)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0304405X16301751
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:jfinec:v:123:y:2017:i:1:p:137-162
DOI: 10.1016/j.jfineco.2016.01.032
Access Statistics for this article
Journal of Financial Economics is currently edited by G. William Schwert
More articles in Journal of Financial Economics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().