Journal of Applied Probability
- J. Appl. Probab.
- Volume 44, Number 1 (2007), 16-40.
Distribution-invariant risk measures, entropy, and large deviations
The simulation of distributions of financial assets is an important issue for financial institutions. If risk measures are evaluated for a simulated distribution instead of the model-implied distribution, the errors in the risk measurements need to be analyzed. For distribution-invariant risk measures which are continuous on compacts, we employ the theory of large deviations to study the probability of large errors. If the approximate risk measurements are based on the empirical distribution of independent samples, then the rate function equals the minimal relative entropy under a risk measure constraint. We solve this minimization problem explicitly for shortfall risk and average value at risk.
J. Appl. Probab. Volume 44, Number 1 (2007), 16-40.
First available in Project Euclid: 30 March 2007
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Digital Object Identifier
Mathematical Reviews number (MathSciNet)
Zentralblatt MATH identifier
Primary: 91B30: Risk theory, insurance 49Q20: Variational problems in a geometric measure-theoretic setting 62B10: Information-theoretic topics [See also 94A17] 62D05: Sampling theory, sample surveys 91B28
Weber, Stefan. Distribution-invariant risk measures, entropy, and large deviations. J. Appl. Probab. 44 (2007), no. 1, 16--40. doi:10.1239/jap/1175267161. https://projecteuclid.org/euclid.jap/1175267161