Entropic risk measure

In financial mathematics, the entropic risk measure is a risk measure which depends on the risk aversion of the user through the exponential utility function. This makes it a theoretically interesting measure because it would provide different risk values for different individuals. However, in practice it would be difficult to use since quantifying the risk aversion for an individual is difficult to do. The entropic risk measure is the prime example of a convex risk measure which is not coherent.[1] Given the connection to utility functions already, it is an obvious choice for the constraints in utility maximization problems.

Contents

Mathematical definition

The entropic risk measure with parameter \theta (the risk aversion parameter) is defined as

\rho^{\mathrm{ent}}(X) = \frac{1}{\theta}\log\left(\mathbb{E}[e^{-\theta X}]\right) = \sup_{Q \in \mathcal{M}_1} \left\{E^Q[-X] -\frac{1}{\theta}H(Q|P)\right\} \,[2]

where H(Q|P) = E\left[\frac{dQ}{dP}\log\frac{dQ}{dP}\right] is the relative entropy of Q << P.[3]

Acceptance set

The acceptance set for the entropic risk measure is the set of payoffs with positive expected utility. That is

A = \{X \in L^p(\mathcal{F}): E[u(X)] \geq 0\} = \{X \in L^p(\mathcal{F}): E\left[e^{-\theta X}\right] \leq 1\}

where u(X) is the exponential utility function.[3]

Dynamic entropic risk measure

The conditional risk measure associated with dynamic entropic risk with risk aversion parameter \theta is given by

\rho^{\mathrm{ent}}_t(X) = \frac{1}{\theta}\log\left(\mathbb{E}[e^{-\theta X} | \mathcal{F}_t]\right).

This is a time consistent risk measure if \theta is constant through time.[4]

References

  1. ^ Rudloff, Birgit; Sass, Jorn; Wunderlich, Ralf (July 21, 2008) (pdf). Entropic Risk Constraints for Utility Maximization. http://www.princeton.edu/~brudloff/RudloffSassWunderlich08.pdf. Retrieved July 22, 2010. 
  2. ^ Föllmer, Hans; Schied, Alexander (2004). Stochastic finance: an introduction in discrete time (2 ed.). Walter de Gruyter. p. 174. ISBN 9783110183467. 
  3. ^ a b Follmer, Hans; Schied, Alexander (October 8, 2008) (pdf). Convex and Coherent Risk Measures. http://wws.mathematik.hu-berlin.de/~foellmer/papers/CCRM.pdf. Retrieved July 22, 2010. 
  4. ^ Penner, Irina (2007) (pdf). Dynamic convex risk measures: time consistency, prudence, and sustainability. http://wws.mathematik.hu-berlin.de/~penner/penner.pdf. Retrieved February 3, 2011.