Variance Disparity and Market Frictions

Yang-Ho Park
Additional contact information
Yang-Ho Park: https://www.federalreserve.gov/econres/yang-ho-park.htm

No 2019-059, Finance and Economics Discussion Series from Board of Governors of the Federal Reserve System (U.S.)

Abstract: This paper introduces a new model-free approach to measuring the expectation of market variance using VIX derivatives. This approach shows that VIX derivatives carry different information about future variance than S&P 500 (SPX) options, especially during the 2008 financial crisis. I find that the segmentation is associated with frictions such as funding illiquidity, market illiquidity, and asymmetric information. When they are segmented, VIX derivatives contribute more to the variance discovery process than SPX options. These findings imply that VIX derivatives would offer a better estimate of expected variance than SPX options, and that a measure of segmentation may be useful for policymakers as it signals the severity of frictions.

Keywords: VIX derivative; Asymmetric information; Economic uncertainty; Illiquidity; Implied variance (search for similar items in EconPapers)
JEL-codes: G01 G13 G14 (search for similar items in EconPapers)
Pages: 55 pages
Date: 2019-08
New Economics Papers: this item is included in nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.federalreserve.gov/econres/feds/files/2019059pap.pdf (application/pdf)

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:fip:fedgfe:2019-59

DOI: 10.17016/FEDS.2019.059

Access Statistics for this paper

More papers in Finance and Economics Discussion Series from Board of Governors of the Federal Reserve System (U.S.) Contact information at EDIRC.
Bibliographic data for series maintained by Ryan Wolfslayer ; Keisha Fournillier ().