Risk measure

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

In financial mathematics, 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.

Mathematically[edit]

A risk measure is defined as a mapping from a set of random variables to the real numbers. This set of random variables represents portfolio returns. 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 \{+\infty\} should have certain properties:[1]

Normalized
\rho(0) = 0
Translative
\mathrm{If}\; a \in \mathbb{R} \; \mathrm{and} \; Z \in \mathcal{L} ,\;\mathrm{then}\; \rho(Z + 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_2) \leq \rho(Z_1)

Set-valued[edit]

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.[2]

Mathematically[edit]

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 + 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:[3]

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 + u1) = R(X) - u
Monotone
\forall X_2 - X_1 \in L_d^p(K) \Rightarrow R(X_2) \supseteq R(X_1)

Examples[edit]

Well known risk measures[edit]

Variance[edit]

Variance (or standard deviation) is not a risk measure. This can be seen since it has neither the translation property nor monotonicity. That is, Var(X + 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[edit]

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.[4]

Risk measure to acceptance set[edit]

  • If \rho is a (scalar) risk measure then A_{\rho} = \{X \in L^p: \rho(X) \leq 0\} is an acceptance set.
  • If R is a set-valued risk measure then A_R = \{X \in L^p_d: 0 \in R(X)\} is an acceptance set.

Acceptance set to risk measure[edit]

  • If A is an acceptance set (in 1-d) then \rho_A(X) = \inf\{u \in \mathbb{R}: X + u1 \in A\} defines a (scalar) risk measure.
  • If A is an acceptance set then R_A(X) = \{u \in M: X + u1 \in A\} is a set-valued risk measure.

Relation with deviation risk measure[edit]

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

  • D(X) = \rho(X - \mathbb{E}[X])
  • \rho(X) = D(X) - \mathbb{E}[X].

\rho is called expectation bounded if it satisfies \rho(X) > \mathbb{E}[-X] for any nonconstant X and \rho(X) = \mathbb{E}[-X] for any constant X.[5]

See also[edit]

References[edit]

  1. ^ Artzner, Philippe; Delbaen, Freddy; Eber, Jean-Marc; Heath, David (1999). "Coherent Measures of Risk" (pdf). Mathematical Finance 9 (3): 203–228. doi:10.1111/1467-9965.00068. Retrieved February 3, 2011. 
  2. ^ Jouini, Elyes; Meddeb, Moncef; Touzi, Nizar (2004). "Vector–valued coherent risk measures". Finance and Stochastics 8 (4): 531–552. doi:10.1007/s00780-004-0127-6. 
  3. ^ Hamel, A. H.; Heyde, F. (2010). "Duality for Set-Valued Measures of Risk" (pdf). SIAM Journal on Financial Mathematics 1 (1): 66–95. doi:10.1137/080743494. Retrieved August 17, 2012.  edit
  4. ^ Andreas H. Hamel; Frank Heyde; Birgit Rudloff (2011). "Set-valued risk measures for conical market models" (pdf). Mathematics and Financial Economics 5 (1): 1–28. doi:10.1007/s11579-011-0047-0. Retrieved April 20, 2012. 
  5. ^ Rockafellar, Tyrrell; Uryasev, Stanislav; Zabarankin, Michael (2002). Deviation Measures in Risk Analysis and Optimization (pdf). Retrieved October 13, 2011. 

Further reading[edit]