Economics at your fingertips  

Risk appetite, idiosyncratic volatility and expected returns

Mahmoud Qadan

International Review of Financial Analysis, 2019, vol. 65, issue C

Abstract: This paper examines the variations in idiosyncratic volatility in stock returns over time, and evaluates the role of investor sentiment in explaining these variations. This study uses Fama and French's (2015) 5-factor model to calculate the idiosyncratic volatility with data from the Center for Research in Security Prices (CRSP) for 1980–2016, and analyzes the effects of investors' risk appetite reflected by market-based, press-based, and survey-based proxies for investor sentiment on the relationship between expected returns and idiosyncratic volatility. The findings demonstrate that risk appetite plays a significant role in explaining and predicting variations in this relationship over time. Specifically, when risk appetite increases, there is a shift from safer to more speculative stocks that is translated into positive effect on the relationship between expected returns and idiosyncratic volatility. In contrast, a lack of appetite for risk has the opposite effect. The results are robust using different subsamples and econometric procedures.

Keywords: Cross-section of stock returns; Idiosyncratic volatility; Predictability; Investor sentiment (search for similar items in EconPapers)
JEL-codes: F39 G12 G14 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
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:

DOI: 10.1016/j.irfa.2019.101372

Access Statistics for this article

International Review of Financial Analysis is currently edited by B.M. Lucey

More articles in International Review of Financial Analysis from Elsevier
Bibliographic data for series maintained by Haili He ().

Page updated 2020-06-20
Handle: RePEc:eee:finana:v:65:y:2019:i:c:s1057521919301760