Nonparametric Risk Management and Implied Risk Aversion

Yacine Ait-Sahalia and Andrew Lo ()

No 6130, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: Typical value-at-risk (VAR) calculations involve the probabilities of extreme dollar losses, based on the statistical distributions of market prices. Such quantities do not account for the fact that the same dollar loss can have two very different economic valuations, depending on business conditions. We propose a nonparametric VAR measure that incorporates economic valuation according to the state-price density associated with the underlying price processes. The state-price density yields VAR values that are adjusted for risk aversion, time preferences, and other variations in economic valuation. In the context of a representative agent equilibrium model, we construct an estimator of the risk-aversion coefficient that is implied by the joint observations on the cross-section of option prices and time-series of underlying asset values. "

JEL-codes: G12 G13 (search for similar items in EconPapers)
Date: 2000-03
Note: AP
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (376)

Published as Journal of Econometrics, Vol. 94 (2000): 9-51.

Downloads: (external link)
http://www.nber.org/papers/w6130.pdf (application/pdf)

Related works:
Journal Article: Nonparametric risk management and implied risk aversion (2000)
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:nbr:nberwo:6130

Ordering information: This working paper can be ordered from
http://www.nber.org/papers/w6130

Access Statistics for this paper

More papers in NBER Working Papers from National Bureau of Economic Research, Inc National Bureau of Economic Research, 1050 Massachusetts Avenue Cambridge, MA 02138, U.S.A.. Contact information at EDIRC.
Bibliographic data for series maintained by ().