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{{Context|date=July 2010}}
{{Technical|date=September 2010}}
 
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.<ref>{{Cite journal|last=Rudloff|first=Birgit|last2=Sass|first2=Jorn|last3=Wunderlich|first3=Ralf|date=July 21, 2008|title=Entropic Risk Constraints for Utility Maximization|url=http://www.princeton.edu/~brudloff/RudloffSassWunderlich08.pdf|accessdate=July 22, 2010|format=pdf}}</ref>  Given the connection to [[utility function]]s already, it is an obvious choice for the constraints in [[utility maximization problem]]s.
 
==Mathematical definition==
The entropic risk measure with parameter <math>\theta</math> (the risk aversion parameter) is defined as
: <math>\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\} \,</math><ref>{{cite book|first1=Hans|last1=Föllmer|first2=Alexander|last2=Schied|title=Stochastic finance: an introduction in discrete time|publisher=Walter de Gruyter|year=2004|edition=2|isbn=978-3-11-018346-7|page=174}}</ref>
where <math>H(Q|P) = E\left[\frac{dQ}{dP}\log\frac{dQ}{dP}\right]</math> is the [[relative entropy]] of ''Q'' [[Absolutely continuous#Absolute continuity of measures|<<]] ''P''.<ref name="FS10">{{Cite journal|last=Follmer|first=Hans|last2=Schied|first2=Alexander|date=October 8, 2008|title=Convex and Coherent Risk Measures|url=http://wws.mathematik.hu-berlin.de/~foellmer/papers/CCRM.pdf|accessdate=July 22, 2010|format=pdf}}</ref>
 
==Acceptance set==
The [[acceptance set]] for the entropic risk measure is the set of payoffs with positive expected utility.  That is
: <math>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\}</math>
where <math>u(X)</math> is the exponential utility function.<ref name="FS10" />
 
==Dynamic entropic risk measure==
The [[conditional risk measure]] associated with dynamic entropic risk with risk aversion parameter <math>\theta</math> is given by
:<math>\rho^{\mathrm{ent}}_t(X) = \frac{1}{\theta}\log\left(\mathbb{E}[e^{-\theta X} | \mathcal{F}_t]\right).</math>
This is a [[time consistent]] risk measure if <math>\theta</math> is constant through time.<ref name="penner_thesis">{{cite journal|last=Penner|first=Irina|year=2007|title=Dynamic convex risk measures: time consistency, prudence, and sustainability|url=http://wws.mathematik.hu-berlin.de/~penner/penner.pdf|format=pdf|accessdate=February 3, 2011}}</ref>
== See also ==
* [[Entropic value at risk]]
==References==
{{Reflist}}
 
{{DEFAULTSORT:Entropic Risk Measure}}
[[Category:Financial risk]]
[[Category:Mathematical finance]]
[[Category:Utility]]

Latest revision as of 06:27, 11 June 2013

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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.

Mathematical definition

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

ρent(X)=1θlog(𝔼[eθX])=supQ1{EQ[X]1θH(Q|P)}[2]

where H(Q|P)=E[dQdPlogdQdP] 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={XLp():E[u(X)]0}={XLp():E[eθX]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 θ is given by

ρtent(X)=1θlog(𝔼[eθX|t]).

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

See also

References

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