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Stability of the value function and the set of minimizers w.r.t


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Abstract. Stability of the value function and the set of minimizers w.r.t. the given data is a desirable feature of optimal transport problems. For the classical Kan- torovich transport problem, stability is satisfied under mild assumptions and in general frameworks such as the one of Polish spaces. However, for the martingale transport problem several works based on different strategies established stability results forRonly. We show that the restriction to dimensiond= 1is not accidental by presenting a sequence of marginal distributions onR2 for which the martingale optimal transport problem is neither stable w.r.t. the value nor the set of minimiz- ers. Our construction adapts to any dimensiond2. For d2it also provides a contradiction to the martingale Wasserstein inequality established by Jourdain and Margheriti ind= 1.

Keywords: martingale optimal transport, stability, convex order, martingale cou- plings, Monge-Kantorovich transport problems

Mathematics Subject Classification (2020): 60G42, 60B10, 60E15, 49Q22

1. Introduction

For two probability measuresµandν onRdletΠ(µ, ν)denote the set of all couplings betweenµandν, i.e. the set of all probability measures onRd×Rdwhich have marginal distributionsµand ν. Letc:Rd×Rd→Rbe measurable and integrable with respect to the elements ofΠ(µ, ν). The classical optimal transport problem is given by

(OT) Vc(µ, ν) = inf




c(x, y) dπ(x, y).

For the cost function c1(x, y) := ky−xk (where k · k is the Euclidean norm) the 1- Wasserstein distanceW1:=Vc1 is a metric onP1(Rd), the space of probability measures µ that satisfyR


Two probability measures µ, ν ∈ P1(Rd) are said to be in convex order, denoted by µ≤cν, if R


Rdϕdν for all convex functions ϕ∈L1(ν). Ifµ≤cν, Strassen’s theorem yields that there exists at least one martingale coupling between µ and ν. A martingale coupling is a coupling π ∈ Π(µ, ν) for which there exists a disintegration (πx)x∈Rd such that




ydπx(y) =x for µ-a.e. x∈Rd. If µ≤cν, the martingale optimal transport problem is given by

(MOT) VcM(µ, ν) = inf




c(x, y) dπ(x, y)

Date: January 30, 2021.

MB is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foun- dation) under Germany’s Excellence Strategy EXC 2044 –390685587, Mathematics Münster:




whereΠM(µ, ν) denotes the set of all martingale couplings betweenµ andν.

Stability in d = 1. Let us recall two reasons why stability results are crucial from an applied perspective. Firstly, they enable the strategy of approximating the problem by a discretized problem or by any other that can rapidly be solved computationally (cf. [1, 12]). Secondly, any application to noisy data would require stability for the results to be meaningful. In relation with (MOT), this discussion is motivated by its connection to (robust) mathematical finance (cf. [3, 10]).

Let µ, ν ∈ P1(R) with µ ≤c ν, and (µn)n∈N and (νn)n∈N be sequences of prob- ability measures on R with finite first moment such that limn→∞W1n, µ) = 0, limn→∞W1n, ν) = 0 and µnc νn for all n ∈ N. The following stability results are available:

(S1) Accumulation Points of Minimizers: Letcbe a continuous cost function which is sufficiently integrable (e.g.|c(x, y)| ≤A(1 +|x|+|y|)) and let πn be a min- imizer of the (MOT) problem between µn and νn for all n ∈ N. Any weak accumulation point of (πn)n∈N is a minimizer of (MOT) between µand ν.

(S2) Continuity of the Value: Letcbe a continuous cost function which is sufficiently integrable (e.g.|c(x, y)| ≤A(1 +|x|+|y|)). There holds

n→∞lim VcMn, νn) =VcM(µ, ν).

(S3) Approximation: For all π ∈ ΠM(µ, ν) there exists a sequence (πn)n∈N of mar- tingale couplings betweenµn and νn that converges weakly toπ.

This constitutes the heart of the theory of stability recently consolidated for the martingale transport problem on the real line. Before we go more into the details of the literature let us stress that with (S3) any minimizer can be approximated by a sequence of martingale transport with prescribed marginals. Therefore, under mild assumptions (S3) implies (S2). Moreover, due to the tightness of S

n∈NΠMn, νn), (S2) implies (S1).

Early versions of (S1) and (S2) for special classes of cost-functions were obtained by Juillet [14] and later by Guo and Obloj [9]. The general version of (S1) and (S2) was first shown by Backhoff-Veraguas and Pammer [2, Theorem 1.1, Corollary 1.2]

and Wiesel [19, Theorem 2.9]. Only very recently, Beiglböck, Jourdain, Margheriti and Pammer [4] have proven (S3). We want to stress that (S1), (S2) and (S3) are given in a minimal formulation and that in the articles some aspects of the results are notably stronger. For instance, the cost functioncin (S1) and (S2) can be replaced by a uniformly converging sequence(cn)n∈N[2]. Moreover, it is an important achievement that on top of weak convergence we have convergence w.r.t. (an extension of) the adapted Wasserstein metric for the approximation in (S3) [4] and for the convergence in (S1) [5], see also [19]. Finally, these stability results also hold for weak martingale optimal transport which is an extension of (MOT) w.r.t. the structure of the cost function (cf. [5, Theorem 2.6]). For further details we invite the interested reader to directly consult the articles.

The martingale Wasserstein inequality introduced by Jourdain and Margheriti in [11, Theorem 2.12] belongs also to the context of stability and approximation and it appears for example as the important last step in the proof of (S3) in [4]. In dimension d= 1 there exists a constant C >0independent of µand ν such that

(MWI) M1(µ, ν)≤CW1(µ, ν).




m=n= 2 y

x m=n= 3

Figure 1. The construction for m =n= 2 and m= n= 3. The red circles indicate the Dirac measures of µm each with mass m1 and the blue circles indicate the Dirac measures ofνm,n each with mass 2m1 .

where M1(µ, ν) is the value of the (MOT) problem between µ and ν w.r.t. the cost function kx−yk. Moreover, they proved thatC= 2is sharp. For their proof Jourdain and Margheriti introduce a family of martingale couplings π ∈ ΠM(µ, ν) that satisfy R

R×R|x−y|dπ(x, y)≤2W1(µ, ν) (including the particularly notable inverse transform martingale coupling).

Instability in d ≥2. The stability of (OT) (and its extension to weak optimal trans- port [5, Theorem 2.5]) is independent of the dimension. However, Beiglböck et al. had to restrict their stability theorem for (weak) (MOT) in the critical step to dimension d= 1(cf. [5, Theorem 2.6 (b’)]). Similarly, in dimensiond≥2, Jourdain and Margher- iti could only extend the martingale Wasserstein inequality for product measures and for measures in relation through a homothetic transformation, see [11, Section 3]. The difficulties in expanding these stability results to higher dimensions are not of technical nature but a consequence of instability of (MOT) in higher dimensions without further assumptions.

In the following we construct a sequence of probability measures on R2 for which (S1), (S2) and (S3) do not hold. Moreover, we provide an example that shows that the inequality (MWI) does not hold in dimension d= 2 for any fixed constant C >0 without further assumptions. Since one can embed this example into Rd for anyd≥3 by the map ι: (x, y)7→(x, y,0, ...,0), these results also fail in any higher dimension.

We denote by Pθ the one step probability kernel of the simple random walk along the line lθ that makes an angle θ∈


with thex-axis. More precisely:

Pθ :R23(x1, x2)7→ 1

2 δ(x1,x2)+(cos(θ),sin(θ))(x1,x2)−(cos(θ),sin(θ))

∈ P1(R2).

Form, n∈N≥1 we define two probability measures onR2 by µm :=





(i,0) and νm,n :=µmPπ


where µmPπ

2n denotes the application of the kernel Pπ

2n to µm. Figure 1 illustrates (µ2, ν2,2) and(µ3, ν3,3).

Since for any convex functionϕ:R2→RJensen’s inequality yields Z


ϕdνm,n = Z






m(x)≥ Z


ϕdµm, we have µmcνm,n for all m, n∈N≥1.


Lemma 1.1. The martingale coupling πm,n := µm(Id, Pπ

2n) is the only martingale coupling between µm andνm,n for all m, n∈N≥1.

For everym≥2the sequence (µm, νm,n)n∈Nserves as a counterexample to analogue versions of (S1), (S2) and (S3) in dimension d = 2. The crucial observation is that whereas ΠMm, νm,n) consists of exactly one element for all n ∈ N by Lemma 1.1, there are infinitely many different martingale couplings between µm and the limit of (νm,n)n∈N.

Proposition 1.2. Let m≥2. There holds limn→∞W1m,n, µmP0) = 0.

Moreover, we have the following:

(i) Letc1(x, y) :=ky−xkfor allx, y∈R2andπm,n:=µm(Id, Pπ

2n)for alln∈N≥1. The martingale couplingsπn are minimizers of the (MOT)problem betweenµm and νm,n w.r.t. c1. Moreover, (πm,n)n∈N is weakly convergent but the limit is not an optimizer of (MOT) w.r.t. c1 between (its marginals)µm andµmP0. (ii) Let c1 be defined as in (i). There holds

n→∞lim VcM1m, νm,n) = 1> VcM1m, µmP0).

(iii) The set ΠMm, µmP0)\ {µm(Id, P0)} is non empty and no element in this set can be approximated by a weakly convergent sequence (πm,n)n∈N of martingale couplings πm,n ∈ΠMm, νm,n).

The sequence(µn, νn,n)n∈Nshows that there cannot exist a constantC >0for which the inequality (MWI) holds in dimension d= 2.

Proposition 1.3. There holds


M1n, νn,n)

W1n, νn,n) = +∞.

Remark 1.4. The theory of MOT in dimension two and further is also challenging in other aspects. For instance, the concept of irreducible components and convex paving can be directly reduced to the study of potential functions in dimension d= 1, whereas there are at least three different advanced approaches in dimensiond≥2(cf.[8, 6, 17]).

On the level of processes we would like to remind the reader that a higher dimensional version of Kellerer’s theorem is still not proved or disproved. One major obstacle is that the one-dimensional proof via Lipschitz-Markov kernels cannot be extrapolated, see [13, Section 2.2] where a method similar to ours is used.

2. Proofs

We denote byf#µthe push-forward of the measureµ under the function f.

Proof of Lemma 1.1. Let m, n ≥ 2 be integers and θn := 2nπ . We denote by Lθn the projection parallel to the line lθn ={(x1,tan(θn)x1) :x1∈R}onto the x-axis, i.e.

Lθn :R23(x1, x2)7→x1−tan(θn)−1x2∈R.

Moreover, by settingν˜:= (Lθn)#νm,n and µ˜:= (Lθn)#µm one has

(2.1) ν˜= 1





δi = ˜µ.


Letπ ∈ΠMm, νm,n). As Lθn is a linear map, π˜:= (Lθn⊗Lθn)#π is a martingale coupling of µ˜ and ν. Indeed, for all˜ ϕ∈Cb(R2) there holds



ϕ(x)(y−x) d˜π(x) =Lθn Z


ϕ(Lθn(x))(y−x) dπ(x)

= 0

and this property is equivalent to π˜ being a martingale coupling. Jensen’s inequality in conjunction with (2.1) yields



x2d˜µ(x)≤ Z





d˜µ(x) = Z


y2d˜ν(y) = Z



where(˜πx)x∈Ris a disintegration ofπ˜ that satisfies (1.1). Since the square is a strictly convex function, there holdsR

Ry2d˜πx =x2 if and only ifπ˜xx. Thus, π˜= ˜µ(Id,Id) and we obtain

x1 =Lθn(x1, x2) =Lθn(y1, y2) for π-a.e. ((x1, x2),(y1, y2))∈R2×R2

because supp(µm) ⊂R× {0}. Hence, the martingale transport plan π is only trans- porting along the lines parallel tolθn. Since there are exactly two points in the support ofνm,n that lie on the same line, and we are looking for a martingale coupling, we have

π =µm(Id, Pθn).

Lemma 2.1. For all m∈N\ {0} andθ∈ 0,π2

one has W1mP0, µmPθ)≤θ.

Proof. The inequality consists merely of a comparison of angle and chord. Alternatively, for all m∈Nand θ∈


we directly compute W1mP0, µmPθ)≤



W1xP0, δxPθ) dµm(x)


2(1−cos(θ)) = 2 sin(θ/2)≤θ.

Proof of Proposition 1.2. Letm≥2. By Lemma 2.1, we know

n→∞lim W1m,n, µmP0) = lim


2n, µmP0) = 0.

Moreover, for all n∈NLemma 1.1 yields thatπm,n :=µm(Id, Pπ

2n) is the only martin- gale coupling between µm and νm,n and therefore automatically the unique minimizer of the (MOT) problem between these two marginals w.r.t. to any cost function. The sequence(πm,n)n∈Nconverges weakly toπm:=µm(Id, P0)∈ΠMm, µmP0). Note that

π0m:= 1

2m δ((1,0),(1,0))+ 2






+ 1

8m 3δ((1,0),(0,0))((1,0),(m+1,0))((m,0),(0,0))+ 3δ((m,0),(m+1,0))

is a martingale coupling betweenµmandµmP0mm1 δ


2δ(0,0)2(m+1,0) different from πm where only the mass not shared by µm andµmP0 is moved.

Item (i): Since πm is the weak limit of the sequence (πm,n)n∈N, it is the only accu- mulation point. But as we see below in (ii), πm is not the minimizer of the (MOT) problem betweenµm andµmP0 w.r.t. c1.


Item (ii): There holds

n→∞lim VcM1m, νm,n) = lim




kx−ykdπm,n = 1

> m+ 3 4m =



kx−ykdπ0 ≥VcM1m, µmP0).

In fact, according to Lim’s result [16, Theorem 2.4], under an optimal martingale transport w.r.t. c1 the shared mass between the marginal distribution is not moving.

Sinceπ0mis the unique martingale transport between µm andµmP0 with this property, it is the minimizer of this (MOT) problem andVcM1m, µmP0) = m+34m .

Item (iii): Since π is the weak limit of the solitary elements of ΠMm, νm,n), no element of ΠMm, µmP0)\ {µm(Id, P0)} can be approximated and πm0 is an element

of this set.

Proof of Proposition 1.3. Letn∈N. By Lemma 1.1,µn(Id, Pπ

2n)is the only martingale coupling between µn and νn,n. Thus,

M1n, νn,n) = 1.

Since W1 is a metric on P1(R2), the triangle inequality yields W1n, νn,n)≤ W1n, µnP0) +W1nP0, νn,n).

We can easily compute

µnP0= 1 2n










and therefore W1n, µnP0) = 1n. By Lemma 2.1, there holds W1nP0, νn,n) ≤ 2nπ . Hence, we obtain


M1n, νn,n)

W1n, νn,n) ≥ lim




n+2nπ = +∞.

Remark 2.2 (Variations). Our construction may appear somewhat degenerate since µm is discrete and supported on a lower dimensional subspace ofR2. However, it is not particularly difficult to adapt the present construction with new measures that appear more general but yield the same result:

(ii) One could replace the rows of Dirac measures by uniform measures on parallel- ograms. More precisely, we could set


µm,n:= UnifFm,n and ν˜m,n := 1 2


m,n+ UnifF


whereFm denotes the parallelogram spanned by the points

−vn, −vn+ (m,0), vn+ (m,0) and vn with vn := 13 cos 2nπ

,sin 2nπ

∈ R2 and Fm,n± is the translation of this par- allelogram by ±3vn (cf. Figure 2). By the same argument as in Lemma 1.1, any martingale coupling between µ˜m,n and ν˜m,n can only transport along lines parallel to{(x,tan 2nπ

x) :x∈R}. In contrast to the situation in Lemma 1.1, the martingale transport along one of these parallel lines is no longer unique but everyπ∈ΠM(˜µm,n,ν˜m,n) satisfiesπ |x−y|< 13

= 0 for allm, n∈Nbecause




m=n= 2 y

x m=n= 3

Figure 2. The construction in Remark 2.2 (i) for m = n = 2 and m=n= 3. The red area is the support ofµ˜m,n and the blue area the supports ofν˜m,n.

the supports are disjoint. This restriction carries over to any weak accumu- lation point of those martingale couplings and is sufficient to show analogous versions of Proposition 1.2 and Proposition 1.3.

(iii) One could replace µm and νm,n by


µm:= (1−)µm+γ and ν˜m,n:= (1−)νm,n

whereε∈(0,1)andγ is a probability measure with full support (e.g. a standard normal distribution). There holds W1(˜µm,ν˜m,n) = (1−)W1m, νm,n) since W1 derives of the Kantorovich-Rubinstein norm [15](alternatively see[18, Bib.

Notes of Ch.6 ]or [7, §*11.8]) andM1(˜µm,˜νm,n) = (1−)M1m, νm,n) by the result of Lim[16, Theorem 2.4].

Remark 2.3. Finally, we would like to point out that Propositions 1.2 and 1.3 and their proofs are not depending on the choice of the Euclidean norm while defining c1.


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[3] M. Beiglböck, P. Henry-Labordère, and F. Penkner. Model-independent bounds for option prices—

a mass transport approach.Finance Stoch., 17(3):477–501, 2013.

[4] M. Beiglböck, B. Jourdain, W. Margheriti, and G. Pammer. Approximation of martingale cou- plings on the line in the weakadapted topology.preprint, arXiv:2101.02517, 2020.

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