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Arthur CHARPENTIER - Rennes Risk Workshop - April, 2015 Local utility and multivariate risk aversion Arthur Charpentier, Alfred Galichon & Marc Henry Rennes Risk Workshop, April 2015 to appear in Mathematics of Operations Research http ://papers.ssrn.com/id=1982293 1

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Page 1: Slides risk-rennes

Arthur CHARPENTIER - Rennes Risk Workshop - April, 2015

Local utility and multivariate risk aversionArthur Charpentier, Alfred Galichon & Marc Henry

Rennes Risk Workshop, April 2015

to appear in Mathematics of Operations Research

http ://papers.ssrn.com/id=1982293

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Page 2: Slides risk-rennes

Arthur CHARPENTIER - Rennes Risk Workshop - April, 2015

Choice between multivariate risky prospects

– In view of violations of expected utility, a vast literature has emerged on otherfunctional evaluations of risky prospects, particularly Rank DependentPreferences (RDU).

– Simultaneously, a literature developed on the characterization of attitudes tomultiple non substitutable risks and multivariate risk premia within theframework of expected utility.

– Here we characterize attitude to multivariate generalizations of standardnotions of increasing risk with local utility– Rothschild-Stiglitz mean preserving increase in risk– Quiggin monotone mean preserving increase in risk

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Arthur CHARPENTIER - Rennes Risk Workshop - April, 2015

Expected utility– Decision maker ranks random vectors X with law invariant functional

Φ(X) = Φ(FX), with FX the cdf of X.– Utility x 7→ U(x) :

Φ(F ) =∫U(x)dF (x)

Quiggin-Yaari functional– Distortion x 7→ φ(x) :

Φ(F ) =∫φ(t)F−1(t)dt =

∫ (∫ x

−∞φ(F (s))ds

)︸ ︷︷ ︸

U(x,F )

dF (x)

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Arthur CHARPENTIER - Rennes Risk Workshop - April, 2015

Aumann & Serrano Riskiness– Given X, the index of risk R(X) is defined to be the unique positive solution

(if exists) of E[exp(−X/R(X))] = 1– Index of Riskiness x 7→ R :

R such that∫

exp(− xR

)︸ ︷︷ ︸

U(x)

dF (x) = 1

where U is CARA.

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Arthur CHARPENTIER - Rennes Risk Workshop - April, 2015

Local utility– Decision maker ranks random vectors X with law invariant functional

Φ(X) = Φ(FX), with FX the cdf of X.– Local utility x 7→ U(x;F ) is the Fréchet derivative of Φ at F :

Φ(F ′)− Φ(F )−∫U(x;F )[dF ′(x)− dF (x)]→ 0

– If Φ is expected utility, local and global utilities coincide– If Φ is the Quiggin-Yaari functional Φ(X) =

∫φ(t)F−1

X (t)dt, then localutility is U(x;F ) =

∫ x−∞ φ(F (z))dz

– If Φ is the Aumann-Serrano index of riskiness, the local utilitycst[1− exp(−αx)] is CARA

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Page 6: Slides risk-rennes

Arthur CHARPENTIER - Rennes Risk Workshop - April, 2015

Rothschild-Stiglitz mean preserving increase in riskOne of the most commonly used stochastic orderings to compare risky prospectsis the mean preserving increase in risk (MPIR or concave ordering). Let X and Ybe two prospects.Definition :Y %MPIR X if E[u(X)] ≥ E[u(Y )] for all concave utility u.

Characterization :∗ Y L= X + Z with E[Z|X] = 0 (where “L=” denotes equality in distribution).

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Arthur CHARPENTIER - Rennes Risk Workshop - April, 2015

Local utility and MPIR– Aversion to MPIR is equivalent to concavity of local utility (Machina 1982 for

the univariate result)– Still holds for multivariate risks :

– Φ is MPIR averse (Schur concave) if Φ(Y ) ≤ Φ(X) when Y is an MPIR ofX.

– Equivalent to concavity of U(x;F ) in x for all distributions FProof : Φ Schur concave iff Φ decreasing along all martingales Xt

Φ(Xt+dt)− Φ(Xt) = E [U(Xt+dt;FXt)− U(Xt;FXt)]= E

[Tr(D2U(Xt;FXt)σT

t σt)]

Itô≤ 0 iff U(x;F ) concave

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Page 8: Slides risk-rennes

Arthur CHARPENTIER - Rennes Risk Workshop - April, 2015

Two shortcomings of MPIR in the theory of risk sharing– Arrow-Pratt more risk averse decision makers do not necessarily pay more

(than less risk averse ones) for a mean preserving decrease in risk.– Ross (Econometrica 1981)

– Partial insurance contracts offering mean preserving reduction in risk can bePareto dominated.– Landsberger and Meilijson (Annals of OR 1994)

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Arthur CHARPENTIER - Rennes Risk Workshop - April, 2015

Bickel-Lehmann dispersion orderThese shortcomings are not shared by the Bickel-Lehmann dispersion order(Bickel and Lehmann 1979). Let X and Y have cdfs FX and FY and quantilesQX = F−1

X and QY = F−1Y .

Definition :Y %Disp X if QY (s)−QY (s′) ≥ QX(s)−QX(s′).

Characterization (Landsberger-Meilijson) :YL= X + Z with Z and X comonotonic,

Examples :– Normal, exponential and uniform families are dispersion ordered by the variance.– Arrow (1970) stretches of a distribution X 7→ x+ α(X − x), α > 1, are more

dispersed.

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Arthur CHARPENTIER - Rennes Risk Workshop - April, 2015

Local utility and attitude to mean preserving dispersionincrease (Quiggin’s monotone MPIR)– Φ is MMPIR averse if and only if

E[U ′(X;FX)

E[U ′(X;FX)]1{X > x}]≤ E [1{X > x}]

– Example : Quiggin-Yaari functional

Φ(X) =∫φ(t)F−1

X (t)dt

with local utility is

U(x;F ) =∫ x

−∞φ(FX(z))dz

is MMPIR averse iff density φ(u) is stochastically dominated by the uniform(called pessimism by Quiggin)

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Arthur CHARPENTIER - Rennes Risk Workshop - April, 2015

Risk sharing and dispersion– More risk averse decision makers will always pay at least as much (as less risk

averse agents) for a decrease in risk if and only if it is Bickel-Lehmann lessdispersed.– Landsberger and Meilijson (Management Science 1994)

– Unless the uninsured position is Bickel-Lehmann more dispersed than theinsured position, the existing contract can be improved so as to raise theexpected utility of both parties, regardless of their (concave) utility functions.– Landsberger and Meilijson (Annals of OR 1994)

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Arthur CHARPENTIER - Rennes Risk Workshop - April, 2015

Partial insurance

Consider the following insurance contract :

Insuree Insurer

Before Y 0After X1 X2

– By construction Y = X1 +X2.– (X1, X2) is Pareto efficient if and only if comonotonic (Landsberger-Meilijson).– Hence the contract is efficient iff Y = X1 +X2 %Disp X1.

We generalize this result to the case of multivariate risks.

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Arthur CHARPENTIER - Rennes Risk Workshop - April, 2015

Multivariate extension of Bickel-Lehmann and MonotoneMPIR

Quantiles and comonotonicity feature in the definition and the characterizationof the Bickel-Lehmann dispersion ordering. They seem to rely on the ordering onthe real line.

However we can revisit these notions to provide– Multivariate notion of comonotonicity– Multivariate quantile definition

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Arthur CHARPENTIER - Rennes Risk Workshop - April, 2015

Revisiting comonotonicity– X and Y are comonotonic if there exists Z such that X = TX(Z) andY = TY (Z), TX , TY increasing functions.

– Example :– If X(ωi) = xi and Y (ωi) = yi, i = 1 . . . , n, with x1 ≤ . . . ≤ xn andy1 ≤ . . . ≤ yn, then X and Y are comonotonic.

– By the simple rearrangement inequality,

∑i=1,...,n

xiyi = max

∑i=1,...,n

xiyσ(i) : σ permutation

.

– General characterization : X and Y are comonotonic iff

E[XY ] = sup{E[XY ] : Y L= Y

}.

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Arthur CHARPENTIER - Rennes Risk Workshop - April, 2015

Revisiting the quantile function

The quantile function of a prospect X is the generalized inverse of the cumulativedistribution function :

u 7→ QX(u) = inf{x : P(X ≤ x) ≥ u}

Equivalent characterizations :– The quantile function QX of a prospect X is an increasing rearrangement of X,– The quantile QX(U) of X is the version of X which is comonotonic with the

uniform random variable U on [0, 1].– QX is the only l.s.c. increasing function such that

E[QX(U)U ] = sup{E[XU ]; X L= X}.

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Arthur CHARPENTIER - Rennes Risk Workshop - April, 2015

Multivariate µ-quantiles and µ-comonotonicity, (Galichon andHenry, JET 2012)

– The univariate quantile function of a random variable X is the only l.s.c.increasing function such that

E[QX(U)U ] = sup{E[XU ]; X L= X}.

– Similarly, the µ-quantile QX is the essentially unique gradient of a l.s.c.convex function (Brenier, CPAM 1991),

E[〈QX(U),U〉] = sup{E[〈X,U〉]; X L= X}, for some U L= µ.

– X and Y are µ-comonotonic if for some U L= µ,

X = QX(U) and Y = QY (U),

namely if X and Y can be simultaneously rearranged relative to a referencedistribution µ.

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Arthur CHARPENTIER - Rennes Risk Workshop - April, 2015

Example : Gaussian prospects

Suppose the baseline U is standard normal,– X ∼ N(0,ΣX), hence X = Σ1/2

X OXU , with OX orthogonal,– Y ∼ N(0,ΣY ), hence Y = Σ1/2

Y OY U , with OY orthogonal,E[〈X,U〉] is minimized for X = Σ1/2

X U , so when OX is the identity. Hence thegeneralized quantile of X relative to U is

QX(U) = Σ1/2X U .

X and Y are N(0, I)-comonotonic if OX = OY (they have the same orientation).The correlation is

E[XY T] = Σ1/2X OXO

TY Σ1/2

Y = Σ1/2X Σ1/2

Y .

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Arthur CHARPENTIER - Rennes Risk Workshop - April, 2015

Computation of generalized quantiles

The optimal transportation map between µ on [0, 1]d and the empiricaldistribution relative to (X1, . . . ,Xn) satisfies– QX(U) ∈ {X1, . . . ,Xn}– µ(Q−1

X ({Xk})) = 1/n, for each k = 1, . . . , n– QX is the gradient of a convex function V : Rd → R.The solution for the “potential” V is

V (u) = maxk{〈u,Xk〉 − wk},

where w = (w1, . . . , wn)′ minimizes the convex function

w 7→∫V (u)dµ(u) +

n∑k=1

wk/n.

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Arthur CHARPENTIER - Rennes Risk Workshop - April, 2015

µ-Bickel-Lehmann dispersion order

With these multivariate extensions of quantiles and comonotonicity, we have thefollowing multivariate extension of the Bickel-Lehmann dispersion order (Bickeland Lehmann 1979).Definition :(a) Y %µDisp X if QY −QX is the gradient of a convex function.Characterization :(b) Y %µDisp X iff Y L= X +Z, where Z and X are µ-comonotonic,

The proof is based on comonotonic additivity of the µ-quantile transform, i.e.,QX+Z = QX +QZ when X and Z are µ-comonotonic.

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Arthur CHARPENTIER - Rennes Risk Workshop - April, 2015

Relation with existing multivariate dispersion orders

Based on univariate characterization (c), Giovagnoli and Wynn (Stat. and Prob.Letters 1995) propose the strong dispersive order.– Y %SD X iff Y L= ψ(X), where ψ is an expansion, i.e., if

‖ψ(x)− ψ(x′)‖ ≥ ‖x− x′‖.

This is closely related to our µ-Bickel-Lehmann ordering :– Y %SD X iff Y L= X +∇V (X), where V is a convex function.

– The latter is equivalent to Y L= X +Z, where X and Z are c-comonotonic(Puccetti and Scarsini (JMA 2011)),

– It also implies Y %µDisp X for some µ, since X and ∇V (X) areµX -comonotonic (converse not true in general).

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Arthur CHARPENTIER - Rennes Risk Workshop - April, 2015

Partial insurance for multivariate risks

Consider the following insurance contract :

Insuree Insurer

Before Y 0After X1 X2

– By construction Y = X1 +X2.– (X1,X2) is Pareto efficient if and only if µ-comonotonic

(Carlier-Dana-Galichon JET 2012).– Hence the contract is efficient iff Y = X1 +X2 %Disp X1 (from the

characterization above).

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Arthur CHARPENTIER - Rennes Risk Workshop - April, 2015

Multivariate Quiggin-Yaari functional and risk attitude– Given a baseline U ∼ µ, decision maker evaluates risks with the functional

Φ(X) = E[QX(U) · φ(U)]

(Equivalent to monotonicity relative to stochastic dominance and comonotonicadditivity of Φ - Galichon and Henry JET 2012)– Aversion to MPIR is equivalent to Φ(U) = −U– Aversion to MMPIR is equivalent to Φ(X) ≤ Φ(E[X]) (obtains immediately

from the comonotonicity characterization of Bickel-Lehmann dispersion)

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