Entropic value at risk

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In financial mathematics and stochastic optimization, the concept of risk measure is used to quantify the risk involved in a random outcome or risk position. Many risk measures have hitherto been proposed, each having certain characteristics. The entropic value-at-risk (EVaR) is a coherent risk measure introduced by Ahmadi-Javid,[1][2] which is an upper bound for the value at risk (VaR) and the conditional value-at-risk (CVaR), obtained from the Chernoff inequality. The EVaR can also be represented by using the concept of relative entropy. Because of its connection with the VaR and the relative entropy, this risk measure is called "entropic value-at-risk". The EVaR was developed to tackle some computational inefficiencies[clarification needed] of the CVaR. Getting inspiration from the dual representation of the EVaR, Ahmadi-Javid[1][2] developed a wide class of coherent risk measures, called g-entropic risk measures. Both the CVaR and the EVaR are members of this class.


Let (\Omega,\mathcal{F},P) be a probability space with  \Omega a set of all simple events,  \mathcal{F} a  \sigma -algebra of subsets of  \Omega and  P a probability measure on  \mathcal{F} . Let  X be a random variable and  \mathbf{L}_{M^+} be the set of all Borel measurable functions  X:\Omega\rightarrow \R whose moment-generating function  M_X(z) exists for all  z\geq 0 . The entropic value-at-risk (EVaR) of  X\in \mathbf{L}_{M^+} with confidence level  1-\alpha is defined as follows:

\text{EVaR}_{1-\alpha}(X):=\inf_{z>0}\{z^{-1}\ln(M_X(z)/\alpha)\}. \,






In finance, the random variable X \in \mathbf{L}_{M^+}, in the above equation, is used to model the losses of a portfolio.

Consider the Chernoff inequality

\text{Pr}(X\geq a)\leq e^{-za}M_X(z),\quad \forall z>0.\,






Solving the equation   e^{-za}M_X(z)=\alpha for  a , results in  a_X(\alpha,z):=z^{-1}\ln(M_X(z)/\alpha) . By considering the equation (1), we see that  \text{EVaR}_{1-\alpha}(X):=\inf_{z>0}\{a_X(\alpha,z)\} , which shows the relationship between the EVaR and the Chernoff inequality. It is worth noting that  a_X(1,z) is the entropic risk measure or exponential premium, which is a concept used in finance and insurance, respectively.

Let  \mathbf{L}_{M} be the set of all Borel measurable functions  X:\Omega\rightarrow \R whose moment-generating function  M_X(z) exists for all  z. The dual representation (or robust representation) of the EVaR is as follows:

\text{EVaR}_{1-\alpha}(X)=\sup_{Q\in \Im}(E_Q(X))\,






where  X\in \mathbf{L}_{M} , and  \Im is a set of probability measures on  (\Omega,\mathcal{F}) with  \Im=\{Q\ll P:D_{KL}(Q||P)\leq-\ln\alpha\} . Note that  D_{KL}(Q||P):=\int\frac{dQ}{dP}(\ln\frac{dQ}{dP})dP is the relative entropy of  Q with respect to  P, also called the Kullback–Leibler divergence. The dual representation of the EVaR discloses the reason behind its naming.


  • The EVaR is a coherent risk measure.
  • The moment-generating function  M_X(z) can be represented by the EVaR: for all  X\in \mathbf{L}_{M^+} and z>0

M_X(z)=\sup_{0<\alpha\leq 1}\{\alpha\exp(z\text{EVaR}_{1-\alpha}(X))\}.\,






  • For  X,Y\in\mathbf{L}_M ,  \text{EVaR}_{1-\alpha}(X)=\text{EVaR}_{1-\alpha}(Y) for all  \alpha\in]0,1] if and only if  F_X(b)=F_Y(b) for all  b\in\R .
  • The entropic risk measure with parameter  \theta , can be represented by means of the EVaR: for all  X\in \mathbf{L}_{M^+} and  \theta>0

\theta^{-1}\ln M_X(\theta)=a_X(1,\theta)=\sup_{0<\alpha\leq 1}\{\text{EVaR}_{1-\alpha}(X)+\theta^{-1}\ln\alpha\}.\,






  • The EVaR with confidence level  1-\alpha is the tightest possible upper bound that can be obtained from the Chernoff inequality for the VaR and the CVaR with confidence level  1 - \alpha;

\text{VaR}(X)\leq \text{CVaR}(X)\leq\text{EVaR}(X).\,






  • The following inequality holds for the EVaR:







where  \text{E}(X) is the expected value of  X and  \text{esssup}(X) is the essential supremum of X, i.e., \inf_{t\in\R}\{t:\text{Pr}(X\leq t)=1\} . So do hold  \text{EVaR}_0(X)=\text{E}(X) and  \lim_{\alpha\rightarrow 0}\text{EVaR}_{1-\alpha}(X)=\text{esssup}(X) .


Comparing the VaR, CVaR and EVaR for the standard normal distribution
Comparing the VaR, CVaR and EVaR for the uniform distribution over the interval (0,1)

For  X\sim N(\mu,\sigma) ,







For  X\sim U(a,b) ,







Figures 1 and 2 show the comparing of the VaR, CVaR and EVaR for  N(0,1) and  U(0,1) .


Let  \rho be a risk measure. Consider the optimization problem

\min_{\boldsymbol{w}\in \boldsymbol{W}}\rho(G(\boldsymbol{w},\boldsymbol{\psi}))\,






where  \boldsymbol{w}\in\boldsymbol{W}\subseteq\R^n is an   n-dimensional real decision vector,  \boldsymbol{\psi} is an   m-dimensional real random vector with a known probability distribution and the function  G(\boldsymbol{w},.):\R^m\rightarrow\R is a Borel measurable function for all values  \boldsymbol{w}\in\boldsymbol{W} . If  \rho is the  \text{EVaR} , then the problem (10) becomes as follows:

\min_{\boldsymbol{w}\in\boldsymbol{W}, t>0}\{t\ln M_{G(\boldsymbol{w},\boldsymbol{\psi})}(t^{-1})-t\ln\alpha\}.\,






Let  \boldsymbol{S}_{\boldsymbol{\psi}} be the support of the random vector \boldsymbol{\psi} . If  G(.,\boldsymbol{s}) is convex for all  \boldsymbol{s}\in\boldsymbol{S}_{\boldsymbol{\psi}} , then the objective function of the problem (11) is also convex. If  G(\boldsymbol{w},\boldsymbol{\psi}) has the form

G(\boldsymbol{w},\boldsymbol{\psi})=g_0(\boldsymbol{w})+\sum_{i=1}^mg_i(\boldsymbol{w})\psi_i,\quad g_i:\R^n\rightarrow\R, i=0,1,\dots,m,\,






and  \psi_1,\dots,\psi_m are independent random variables in  \mathbf{L}_M , then (11) becomes

\min_{\boldsymbol{w}\in\boldsymbol{W}, t>0}\left\lbrace
g_0(\boldsymbol(w))+t\sum_{i=1}^m\ln M_{g_i(\boldsymbol(w))\psi_i}(t^{-1})-t\ln\alpha






which is computationally tractable. But for this case, if one uses the CVaR in problem (10), then the resulting problem becomes as follows:

\min_{\boldsymbol{w}\in\boldsymbol{W}, t\in\R}\left\lbrace






It can be shown that by increasing the dimension of  \psi , problem (14) is computationally intractable even for simple cases. For example, assume that  \psi_1,\dots,\psi_m are independent discrete random variables that take   k distinct values. For fixed values of  \boldsymbol{w} and   t, the complexity of computing the objective function given in problem (13) is of order mk while the computing time for the objective function of problem (14) is of order  k^m . For illustration, assume that k= 2, m= 100 and the summation of two numbers takes 10^{-12} seconds. For computing the objective function of problem (14) one needs about 4\times 10^{10} years, whereas the evaluation of objective function of problem (13) takes about 10^{-10} seconds. This shows that formulation with the EVaR outperforms the formulation with the CVaR (see [2] for more details).

Generalization (g-entropic risk measures)[edit]

Drawing inspiration from the dual representation of the EVaR given in (3), one can define a wide class of information-theoretic coherent risk measures, which are introduced in.[1][2] Let  g be a convex proper function with  g(1)=0 and  \beta be a non-negative number. The  g -entropic risk measure with divergence level  \beta is defined as







where  \Im=\{Q\ll P:H_g(P,Q)\leq\beta\} in which  H_g(P,Q) is the generalized relative entropy of  Q with respect to  P . A primal representation of the class of g-entropic risk measures can be obtained as follows:







where  g^* is the conjugate of  g . By considering

x\ln x & x>0 \\ 
0 & x=0 \\ 
+\infty & x<0, 






with g^*(x)=e^{x-1} and  \beta=- \ln\alpha, the EVaR formula can be deduced. The CVaR is also a g-entropic risk measure, which can be obtained from (16) by setting

0 & 0\leq x\leq \frac{1}{\alpha} \\ 
+\infty & \text{otherwise}, 






with  g^*(x)=\frac{1}{\alpha}\max\{0,x\} and  \beta=0 (see [1][3] for more details).

For more results on g-entropic risk measures see.[4]

See also[edit]


  1. ^ a b c d Ahmadi-Javid, Amir (2011). An information-theoretic approach to constructing coherent risk measures. St. Petersburg, Russia: Proceedings of IEEE International Symposium on Information Theory. pp. 2125–2127. doi:10.1109/ISIT.2011.6033932. 
  2. ^ a b c d Ahmadi-Javid, Amir (2012). "Entropic value-at-risk: A new coherent risk measure". Journal of Optimization Theory and Applications 155 (3): 1105–1123. doi:10.1007/s10957-011-9968-2. 
  3. ^ Ahmadi-Javid, Amir (2012). "Addendum to: Entropic Value-at-Risk: A New Coherent Risk Measure". Journal of Optimization Theory and Applications 155 (3): 1124–1128. doi:10.1007/s10957-012-0014-9. 
  4. ^ Breuer, Thomas; Csiszar, Imre (2013). "Measuring Distribution Model Risk". arXiv:1301.4832v1.