Unspanned Stochastic Volatility: Empirical Evidence and Affine Representation
Pierre Collin-Dufresne and
Robert Goldstein
No 2001-E9, GSIA Working Papers from Carnegie Mellon University, Tepper School of Business
Abstract:
Most models of the term structure are restrictive in that they assume the bond market forms a complete market. That is, they assume all sources of risk affecting fixed income derivatives can be completely hedged by a portfolio consisting solely of bonds. Below, we demonstrate that this prediction fails in practice. In particular, we find that changes in swap rates have very limited explanatory power for returns on at-the-money straddles -- portfolios mainly exposed to volatility risk. We term this empirical feature `unspanned' stochastic volatility (USV). We demonstrate that bivariate Markov (affine such as Fong and Vasicek (1991) and Longstaff and Schwartz (1992), or not) models cannot exhibit USV. Then, we determine necessary (and apparently sufficient) parameter restrictions for trivariate Markov affine systems to exhibit USV. Finally, we show that USV occurs naturally within the Heath-Jarrow-Morton framework.
New Economics Papers: this item is included in nep-ets
References: Add references at CitEc
Citations: View citations in EconPapers (1)
Downloads: (external link)
http://www.andrew.cmu.edu/user/dufresne
Our link check indicates that this URL is bad, the error code is: 404 Not Found (http://www.andrew.cmu.edu/user/dufresne [302 Moved Temporarily]--> https://www.andrew.cmu.edu/user/dufresne)
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:cmu:gsiawp:-314813579
Ordering information: This working paper can be ordered from
https://student-3k.t ... /gsiadoc/GSIA_WP.asp
Access Statistics for this paper
More papers in GSIA Working Papers from Carnegie Mellon University, Tepper School of Business Tepper School of Business, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213-3890.
Bibliographic data for series maintained by Steve Spear ().