Coherent risk measure

In the fields of Actuarial Science and financial economics there are a number of ways that risk can be defined; to clarify the concept theoreticians have described a number of properties that a risk measure might or might not have. A coherent risk measure is a function \varrho that satisfies properties of monotonicity, sub-additivity, homogeneity, and translational invariance.

Properties

Consider a random outcome X viewed as an element of a linear space \mathcal{L} of measurable functions, defined on an appropriate probability space. A functional \varrho : \mathcal{L} → \R \cup \{+\infty\} is said to be coherent risk measure for \mathcal{L} if it satisfies the following properties:[1]

Normalized
\varrho(0) = 0

That is, the risk of holding no assets is zero.

Monotonicity
\mathrm{If}\; Z_1,Z_2 \in \mathcal{L} \;\mathrm{and}\; Z_1 \leq Z_2 \; \mathrm{a.s.} ,\; \mathrm{then} \; \varrho(Z_1) \geq \varrho(Z_2)

That is, if portfolio Z_2 always has better values than portfolio Z_1 under almost all scenarios then the risk of Z_2 should be less than the risk of Z_1.[2] E.g. If Z_1 is an in the money call option (or otherwise) on a stock, and Z_2 is also an in the money call option with a lower strike price.

Sub-additivity
\mathrm{If}\; Z_1,Z_2 \in \mathcal{L} ,\; \mathrm{then}\; \varrho(Z_1 + Z_2) \leq \varrho(Z_1) + \varrho(Z_2)

Indeed, the risk of two portfolios together cannot get any worse than adding the two risks separately: this is the diversification principle.

Positive homogeneity
\mathrm{If}\; \alpha \ge 0 \; \mathrm{and} \; Z \in \mathcal{L} ,\; \mathrm{then} \; \varrho(\alpha Z) = \alpha \varrho(Z)

Loosely speaking, if you double your portfolio then you double your risk.

Translation invariance

If A is a deterministic portfolio with guaranteed return a and Z \in \mathcal{L} then

\varrho(Z + A) = \varrho(Z) - a

The portofolio A is just adding cash a to your portfolio Z. In particular, if a=\varrho(Z) then \varrho(Z+A)=0.

Convex risk measures

The notion of coherence has been subsequently relaxed. Indeed, the notions of Sub-additivity and Positive Homogeneity can be replaced by the notion of convexity:[3]

Convexity
\text{If }Z_1,Z_2 \in \mathcal{L}\text{ and }\lambda \in [0,1] \text{ then }\varrho(\lambda Z_1 + (1-\lambda) Z_2) \leq \lambda \varrho(Z_1) + (1-\lambda) \varrho(Z_2)

General framework of Wang transform

Wang transform of the decumulative distribution function

A Wang transform of the decumulative distribution function is an increasing function g \colon [0,1] \rightarrow [0,1] where g(0)=0 and g(1)=1. [4] This function is called distortion function or Wang transform function.

The dual distortion function is \tilde{g}(x) = 1 - g(1-x).[5][6] Given a probability space (\Omega,\mathcal{F},\mathbb{P}), then for any random variable X and any distortion function g we can define a new probability measure \mathbb{Q} such that for any A \in \mathcal{F} it follows that \mathbb{Q}(A) = g(\mathbb{P}(X \in A)). [5]

Actuarial premium principle

For any increasing concave Wang transform function, we could define a corresponding premium principle :[4] \varrho(X)=\int_0^{+\infty}g\left(\bar{F}_X(x)\right) dx

Coherent risk measure

A coherent risk measure could be defined by a Wang transform of the decumulative distribution function g if on only if g is concave.[4]

Examples of risk measure

Value at risk

It is well known that value at risk is not, in general, a coherent risk measure as it does not respect the sub-additivity property. An immediate consequence is that value at risk might discourage diversification.[1] Value at risk is, however, coherent, under the assumption of elliptically distributed losses (e.g. normally distributed) when the portfolio value is a linear function of the asset prices. However, in this case the value at risk becomes equivalent to a mean-variance approach where the risk of a portfolio is measured by the variance of the portfolio's return.

The Wang transform function (distortion function) for the Value at Risk is g(x)=\mathbf{1}_{x\geq 1-\alpha}. The non-concavity of g proves the non coherence of this risk measure.

Illustration

As a simple example to demonstrate the non-coherence of value-at-risk consider looking at the VaR of a portfolio at 95% confidence over the next year of two default-able zero coupon bonds that mature in 1 years time denominated in our numeraire currency.

Assume the following:

Under these conditions the 95% VaR for holding either of the bonds is 0 since the probability of default is less than 5%. However if we held a portfolio that consisted of 50% of each bond by value then the 95% VaR is 35% (= 0.5*0.7 + 0.5*0) since the probability of at least one of the bonds defaulting is 7.84% which exceeds 5%. This violates the sub-additivity property showing that VaR is not a coherent risk measure.

Average value at risk

The average value at risk (sometimes called expected shortfall or conditional value-at-risk) is a coherent risk measure, even though it is derived from Value at Risk which is not.

Entropic value at risk

The entropic value at risk is a coherent risk measure.[7]

Tail value at risk

The tail value at risk (or tail conditional expectation) is a coherent risk measure only when the underlying distribution is continuous.

The Wang transform function (distortion function) for the tail value at risk is g(x)=\min(\frac{x}{\alpha},1). The concavity of g proves the coherence of this risk measure in the case of continuous distribution.

Proportional Hazard (PH) risk measure

The PH risk measure (or Proportional Hazard Risk measure) transforms the hasard rates \scriptstyle \left( \lambda(t) = \frac{f(t)}{\bar{F}(t)}\right) using a coefficient \xi.

The Wang transform function (distortion function) for the PH risk measure is g_{\alpha}(x) = x^{\xi} . The concavity of g if \scriptstyle \xi<\frac{1}{2} proves the coherence of this risk measure.

Sample of Wang transform function or distortion function

g-Entropic risk measures

g-entropic risk measures are a class of information-theoretic coherent risk measures that involve some important cases such as CVaR and EVaR.[7]

The Wang risk measure

The Wang risk measure is define by the following Wang transform function (distortion function) g_{\alpha}(x)=\Phi\left[ \Phi^{-1}(x)-\Phi^{-1}(\alpha)\right]. The coherence of this risk measure is a consequence of the concavity of g.

Entropic risk measure

The entropic risk measure is a convex risk measure which is not coherent. It is related to the exponential utility.

Superhedging price

The superhedging price is a coherent risk measure.

Set-valued

In a situation with \mathbb{R}^d-valued portfolios such that risk can be measured in n \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.[8]

Properties

A set-valued coherent risk measure is a function R: L_d^p \rightarrow \mathbb{F}_M, where \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:[9]

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)
Sublinear

Set-valued convex risk measure

If instead of the sublinear property,R is convex, then R is a set-valued convex risk measure.

Dual representation

A lower semi-continuous convex risk measure \varrho can be represented as

\varrho(X) = \sup_{Q \in \mathcal{M}(P)} \{E^Q[-X] - \alpha(Q)\}

such that \alpha is a penalty function and \mathcal{M}(P) is the set of probability measures absolutely continuous with respect to P (the "real world" probability measure), i.e. \mathcal{M}(P) = \{Q \ll P\}.

A lower semi-continuous risk measure is coherent if and only if it can be represented as

\varrho(X) = \sup_{Q \in \mathcal{Q}} E^Q[-X]

such that \mathcal{Q} \subseteq \mathcal{M}(P).[10]

See also

References

  1. 1 2 Artzner, P.; Delbaen, F.; Eber, J. M.; Heath, D. (1999). "Coherent Measures of Risk". Mathematical Finance 9 (3): 203. doi:10.1111/1467-9965.00068.
  2. ↑ Wilmott, P. (2006). "Quantitative Finance" 1 (2 ed.). Wiley: 342.
  3. ↑ Föllmer, H.; Schied, A. (2002). "Convex measures of risk and trading constraints". Finance and Stochastics 6 (4): 429–447. doi:10.1007/s007800200072.
  4. 1 2 3 Wang, Shuan (1996). "Premium Calculation by Transforming the Layer Premium Density". ASTIN Bulletin 26 (1): 71–92. doi:10.2143/ast.26.1.563234.
  5. 1 2 Balbás, A.; Garrido, J.; Mayoral, S. (2008). "Properties of Distortion Risk Measures". Methodology and Computing in Applied Probability 11 (3): 385. doi:10.1007/s11009-008-9089-z.
  6. ↑ Julia L. Wirch; Mary R. Hardy. "Distortion Risk Measures: Coherence and Stochastic Dominance" (pdf). Retrieved March 10, 2012.
  7. 1 2 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.
  8. ↑ 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.
  9. ↑ 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.
  10. ↑ Föllmer, Hans; Schied, Alexander (2004). Stochastic finance: an introduction in discrete time (2 ed.). Walter de Gruyter. ISBN 978-3-11-018346-7.

External links

This article is issued from Wikipedia - version of the Wednesday, January 06, 2016. The text is available under the Creative Commons Attribution/Share Alike but additional terms may apply for the media files.