Risk measure

Not to be confused with deviation risk measures, e.g. standard deviation .

A Risk measure is used to determine the amount of an asset or set of assets (traditionally currency) to be kept in reserve. The purpose of this reserve is to make the risks taken by financial institutions, such as banks and insurance companies, acceptable to the regulator. In recent years attention has turned towards convex and coherent risk measurement.

Contents

Mathematically

A risk measure is defined as a mapping from a set of random variables to the real numbers. This set of random variables represents the risk at hand. The common notation for a risk measure associated with a random variable X is \rho[X]. A risk measure \rho: \mathcal{L} \to \mathbb{R} \cup \{%2B\infty\} should have certain properties:

Normalized
\rho(0) = 0
Translative
\mathrm{If}\; a \in \mathbb{R} \; \mathrm{and} \; Z \in \mathcal{L} ,\;\mathrm{then}\; \rho(Z %2B a) = \rho(Z) - a
Monotone
\mathrm{If}\; Z_1,Z_2 \in \mathcal{L} \;\mathrm{and}\; Z_1 \leq Z_2 ,\; \mathrm{then} \; \rho(Z_1) \geq \rho(Z_2)

Set-valued

In a situation with \mathbb{R}^d-valued portfolios such that risk can be measured in m \leq d of the assets, then a set of portfolios is the proper way to depict risk. Set-valued risk measures are useful for markets with transaction costs.[1]

Mathematically

A set-valued risk measure is a function R: L_d^p \rightarrow \mathbb{F}_M, where L_d^p is a d-dimensional Lp space, \mathbb{F}_M = \{D \subseteq M: D = cl (D %2B K_M)\}, and K_M = K \cap M where K is a constant solvency cone and M is the set of portfolios of the m reference assets. R must have the following properties:[2]

Normalized
K_M \subseteq R(0) \; \mathrm{and} \; R(0) \cap -\mathrm{int}K_M = \emptyset
Translative in M
\forall X \in L_d^p, \forall u \in M: R(X %2B u1) = R(X) - u
Monotone
\forall X_2 - X_1 \in L_d^p(K) \Rightarrow R(X_2) \supseteq R(X_1)

Examples

Well known risk measures

Variance

Variance (or standard deviation) is not a risk measure. This can be seen since it has neither the translation property or monotonicity. That is Var(X %2B a) = Var(X) \neq Var(X) - a for all a \in \mathbb{R}, and a simple counterexample for monotonicity can be found. The standard deviation is a deviation risk measure.

Relation to Acceptance Set

There is a one-to-one correspondence between an acceptance set and a corresponding risk measure. As defined below it can be shown that R_{A_R}(X) = R(X) and A_{R_A} = A.

Risk Measure to Acceptance Set

Acceptance Set to Risk Measure

Relation with deviation risk measure

There is a one-to-one relationship between a deviation risk measure D and an expectation-bounded risk measure \rho where for any X \in \mathcal{L}^2

\rho is expectation bounded if \rho(X) > \mathbb{E}[-X] for any nonconstant X and \rho(X) = \mathbb{E}[-X] for any constant X.[3]

See also

Further reading

References

  1. ^ Jouini, Elyes; Meddeb, Moncef; Touzi, Nizar (2004). "Vector–valued coherent risk measures". Finance and Stochastics 8 (4): 531–552. 
  2. ^ Hamel, Andreas; Heyde, Frank (December 11, 2008) (pdf). Duality for Set-Valued Risk Measures. http://www.princeton.edu/~ahamel/SetRiskHamHey.pdf. Retrieved July 22, 2010. 
  3. ^ Rockafellar, Tyrrell; Uryasev, Stanislav; Zabarankin, Michael (2002) (pdf). Deviation Measures in Risk Analysis and Optimization. http://www.ise.ufl.edu/uryasev/Deviation_measures_wp.pdf. Retrieved October 13, 2011. }}