Non-Parametric Risk Management and Implied Risk Aversion

49 Pages Posted: 2 Jun 1998

See all articles by Yacine Ait-Sahalia

Yacine Ait-Sahalia

Princeton University - Department of Economics; National Bureau of Economic Research (NBER)

Andrew W. Lo

Massachusetts Institute of Technology (MIT) - Sloan School of Management; National Bureau of Economic Research (NBER); Massachusetts Institute of Technology (MIT) - Computer Science and Artificial Intelligence Laboratory (CSAIL)

Date Written: January 4, 1998

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 Classification: D81, G11

Suggested Citation

Ait-Sahalia, Yacine and Lo, Andrew W., Non-Parametric Risk Management and Implied Risk Aversion (January 4, 1998). CRSP Working Paper No. 468. Available at SSRN: https://ssrn.com/abstract=94133 or http://dx.doi.org/10.2139/ssrn.94133

Yacine Ait-Sahalia (Contact Author)

Princeton University - Department of Economics ( email )

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Andrew W. Lo

Massachusetts Institute of Technology (MIT) - Sloan School of Management ( email )

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National Bureau of Economic Research (NBER) ( email )

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Cambridge, MA 02138
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Massachusetts Institute of Technology (MIT) - Computer Science and Artificial Intelligence Laboratory (CSAIL)

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United States

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