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White-noise driven conditional McKean–Vlasov limits for systems of particles with simultaneous and random jumps

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We study the convergence of N-particle systems described by SDEs driven by Brownian motion and Poisson random measure, where the coefficients depend on the empirical measure of the system. Every particle jumps with a jump rate depending on its position and on the empirical measure of the system. Jumps are simultaneous, that is, at each jump time, all particles of the system are affected by this jump and receive a random jump height that is centred and scaled in \(N^{-1/2}\). This particular scaling implies that the limit of the empirical measures of the system is random, describing the conditional distribution of one particle in the limit system. We call such limits conditional McKean–Vlasov limits. The conditioning in the limit measure reflects the dependencies between coexisting particles in the limit system such that we are dealing with a conditional propagation of chaos property. As a consequence of the scaling in \(N^{-1/2}\) and of the fact that the limit of the empirical measures is not deterministic the limit system turns out to be solution of a non-linear SDE, where not independent martingale measures and white noises appear having an intensity that depends on the conditional law of the process.

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1.1 A priori estimates

Lemma 6.1

Grant Assumptions 12 and 3. For all \(T>0,\)

$$\begin{aligned} \underset{N\in {\mathbb {N}}^*}{\sup }{\mathbb {E}}\left[ \underset{t\le T}{\sup } \left| X^{N,1}_t\right| ^2\right] <\infty . \end{aligned}$$


Notice that

$$\begin{aligned} \underset{0\le s\le t}{\sup } |X^{N,1}_s|\le (X^{N,1}_0) + ||b||_\infty t + \underset{0\le s\le t}{\sup }\left| \int _0^s \sigma (X^{N,1}_r,\mu ^N_r)d\beta ^1_r\right| + \frac{1}{\sqrt{N}}\underset{0\le s\le t}{\sup }|M^N_s|, \end{aligned}$$

where \(M^N\) is the local martingale

$$\begin{aligned} M^N_t := \sum _{k=2}^N\int _{[0,t]\times {\mathbb {R}}_+\times E}\varPsi (X^{N,k}_{s-},X^{N,1}_{s-},\mu ^N_{s-},u^k,u^1)\mathbbm {1}_{\left\{ z\le f(X^{N,k}_{s-},\mu ^N_{s-})\right\} }d\pi ^k(s,z,u). \end{aligned}$$

Consequently, by Burkholder–Davis–Gundy’s inequality and Assumption 3,

$$\begin{aligned} {\mathbb {E}}\left[ \underset{0\le s\le t}{\sup } |X^{N,1}_s|^2\right]\le & {} C + C||b||^2_{\infty }t^2 \\&+ ||\sigma ||^2_\infty t + t||f||_\infty \frac{N-1}{N}\int _E\underset{x,y,m}{\sup }\varPsi (x,y,m,u^1,u^2)^2 d\nu (u). \end{aligned}$$

This proves the result. \(\square \)

1.2 Proof of (20)

Lemma 6.2

Grant Assumptions 12 and 3. With the notation introduced in the proof of Theorem 4.3, we have

$$\begin{aligned} {\mathbb {E}}\left[ F(\mu ^N)\right] \underset{N\rightarrow \infty }{\longrightarrow }{\mathbb {E}}\left[ F(\mu )\right] . \end{aligned}$$


Let us recall that \(\mu ^N\) denotes the empirical measure of \((X^{N,i})_{1\le i\le N}\) and that \(\mu \) is the limit in distribution of (a subsequence of) \(\mu ^N.\)

Step 1. We first show that almost surely, \( \mu \) is supported by continuous trajectories. For that sake, we start showing that \( P^N := {\mathbb {E}}\left[ \mu ^N \right] = {{\mathcal {L}}} ( X^{N, 1 } ) \) is C-tight. This follows from Prop VI. 3.26 in [17], observing that

$$\begin{aligned} \lim _{N \rightarrow \infty } {\mathbb {E}}\left[ \sup _{s \le T } | \varDelta X_s^{N, 1 } |^3 \right] = 0 , \end{aligned}$$

which follows from our conditions on \( \psi .\) Indeed, writing \( \psi ^* ( u^1, u^2 ) :=\sup _{x, y ,m } \psi ( x, y , m, u^1, u^2 ), \) we can stochastically upper bound

$$\begin{aligned} \sup _{s \le T } | \varDelta X_s^{N, 1 } |^3 \le \sup _{k \le K } | \psi ^* ( U^{k, 1 }, U^{k, 2} ) |^3/N^{3/2} , \end{aligned}$$

where \(K \sim Poiss ( N T \Vert f\Vert _\infty ) \) is Poisson distributed with parameter \( N T \Vert f\Vert _\infty ,\) and where \( (U^{k, 1 }, U^{k,2 } )_k \) is an i.i.d. sequence of \( \nu _1 \otimes \nu _1\)-distributed random variables, independent of K. The conclusion then follows from the fact that due to our Assumption 3, \( {\mathbb {E}}\left[ | \psi ^* ( U^{k, 1 }, U^{k, 2} ) |^3 \right] < \infty \) such that

$$\begin{aligned}&{\mathbb {E}}\left[ \sup _{k \le K } | \psi ^* ( U^{k, 1 }, U^{k, 2} ) |^3/N^{3/2} \right] \le {\mathbb {E}}\left[ \frac{1}{N^{3/2}} \sum _{k=1}^K | \psi ^* ( U^{k, 1 }, U^{k, 2} ) |^3 \right] \\&\quad \le \frac{{\mathbb {E}}\left[ | \psi ^* ( U^{k, 1 }, U^{k, 2} ) |^3 \right] }{N^{3/2} } {\mathbb {E}}\left[ K\right] =\frac{{\mathbb {E}}\left[ | \psi ^* ( U^{k, 1 }, U^{k, 2} ) |^3 \right] }{N^{3/2} } N T \Vert f\Vert _\infty \rightarrow 0 \end{aligned}$$

as \( N \rightarrow 0.\)

As a consequence of the above arguments, we know that \( {\mathbb {E}}\left[ \mu ( \cdot ) \right] \) is supported by continuous trajectories. This means that there exists a Borel set \( G \in {\mathcal {D}} ( {\mathbb {R}}_+, {\mathbb {R}}) \) such that \( G \subset C( {\mathbb {R}}_+, {\mathbb {R}}) \) and \( {\mathbb {E}}\left[ \mu ( G) \right] = 1.\) In particular, almost surely, \( \mu (G) = 1,\) which implies that almost surely, \( \mu \) is supported by continuous trajectories. Indeed, \(\mu (G)\) is a r.v. taking values in [0, 1],  and its expectation equals one. Thus \(\mu (G)\) equals one a.s.

We now turn to the heart of this proof and show that \({\mathbb {E}}\left[ F( \mu ^N) \right] \rightarrow {\mathbb {E}}\left[ F( \mu \right] .\) The latter expression contains terms like

$$\begin{aligned} \int _s^tb(Y^1_r,\mu _r)\partial _{x^1}g(Y^1_r,Y^2 _r)dr \end{aligned}$$

for some bounded smooth function g. However, by our assumptions, the continuity of \( m \mapsto b ( x, m) \) is expressed with respect to the Wasserstein 1-distance. Yet, we only have information on the convergence of \(\mu ^N_r\) to \( \mu _r\) for the topology of the weak convergence.

In what follows we make use of Skorokhod’s representation theorem and realize all random measures \( \mu ^N \) and \(\mu \) on an appropriate probability space such that we have almost sure convergence of these realizations (we do not change notation), that is, we know that almost surely,

$$\begin{aligned} \mu ^N \rightarrow \mu \end{aligned}$$

as \(N \rightarrow \infty .\) (Recall that we have already chosen a subsequence in the beginning of the proof of Theorem 4.3). Since \(\mu \) is almost surely supported by continuous trajectories, we also know that almost surely, \(\mu _t^N \rightarrow \mu _t\) weakly for all t (this is a consequence of Theorem 12.5.(i) of [3]).

Step 2. In a first time, let us prove that, a.s., for all t\(\mu ^N_t\) converges to \(\mu _t\) for the metric \(W_1\). Thus we need to show additionally that almost surely, for all \(t \ge 0, \) \( \int |x| d \mu _t^N(x) \rightarrow \int |x| d \mu _t(x).\)

To prove this last fact, it will be helpful to consider rather the convergence of the triplets \( ( \mu ^N, X^{N, 1 }, \mu ^N (|x|)).\) Since the sequence of laws of these triplets is tight as well (the tightness of \((\mu ^N)_N\) and \((X^{N,1})_N\) have been stated in Sect. 4.1, and the tightness of \((\mu ^N(|x|)_N)\) is classical from Aldous’ criterion since \(\mu ^N_t(|x|) = N^{-1}\sum _{k=1}^N |X^{N,k}_t|\)), we may assume that, after having chosen another subsequence and then a convenient realization of this subsequence, we dispose of a sequence of random triplets such that almost surely, as \(N \rightarrow \infty , \)

$$\begin{aligned} ( \mu ^N, X^{N, 1 }, \mu ^N ( |x| ) ) \rightarrow ( \mu , Y, A), \end{aligned}$$

where \( A = ( A_t)_t\) is some process having càdlàg trajectories. In addition, it can be proven that the sequence \((\mu ^N(|x|))_N\) is C-tight (for similar reasons as \((X^{N,1})_N\)), hence A has continuous trajectories.

Taking a bounded and continuous function \( \varPhi : D( {\mathbb {R}}_+, {\mathbb {R}}) \rightarrow {\mathbb {R}}, \) we observe that, as \( N \rightarrow \infty , \)

$$\begin{aligned} {\mathbb {E}}\left[ \int _{D( {\mathbb {R}}_+, {\mathbb {R}}) } \varPhi d \mu \right] \leftarrow {\mathbb {E}}\left[ \int _{D( {\mathbb {R}}_+, {\mathbb {R}}) } \varPhi d \mu ^N \right] = {\mathbb {E}}\left[ \varPhi ( X^{N, 1} ) \right] \rightarrow {\mathbb {E}}\left[ \varPhi ( Y) \right] , \end{aligned}$$

such that \( {\mathbb {E}}\left[ \mu \right] = {{\mathcal {L}}} ( Y). \)

Notice that from the above follows that Y is necessarily a continuous process, since \({\mathbb {E}}\left[ \mu \right] \) is supported by continuous trajectories. Notice also that for the moment we do not know if \( A = \mu ( |x|).\)

Using that \( \sup _N {\mathbb {E}}\left[ \sup _{t \le T } |X_t^{N, 1 }|^2 \right] < \infty \) (see our a priori estimates Lemma 6.1), we deduce that the sequence \( (\sup _{ t \le T} |X_t^{N, 1 }|^{3/2} )_N\) is uniformly integrable. Therefore, \( {\mathbb {E}}\left[ \sup _{t \le T} |X_t^{N, 1 } |^{3/2}\right] \rightarrow {\mathbb {E}}\left[ \sup _{ t \le T} | Y_t|^{3/2}\right] < \infty .\) In particular, we also have that

$$\begin{aligned} {\mathbb {E}}\left[ \sup _{t\le T} \mu _t ( |x|^{3/2})\right]< \infty \quad \text{ and } \text{ thus } \quad \sup _{t\le T} \mu _t ( |x|^{3/2}) < \infty \text{ almost } \text{ surely, } \end{aligned}$$

for all T,  since

$$\begin{aligned} {\mathbb {E}}\left[ \sup _{t\le T} \mu _t ( |x|^{3/2})\right]= & {} {\mathbb {E}}\left[ \sup _{ t \le T } \int _{D({\mathbb {R}}_+, {\mathbb {R}})} | \gamma _t|^{3/2} \mu (d \gamma ) \right] \\\le & {} {\mathbb {E}}\left[ \int _{D({\mathbb {R}}_+, {\mathbb {R}})} \sup _{ t \le T } | \gamma _t|^{3/2} \mu (d \gamma ) \right] \\= & {} {\mathbb {E}}\left[ \sup _{ t \le T } | Y_t|^{3/2}\right] < \infty . \end{aligned}$$

We know that, a.s., \(\mu ^N\) converges weakly to \(\mu \) and \(\mu (G)=1,\) where \( G \subset C( {\mathbb {R}}_+, {\mathbb {R}}).\) Let us fix some \(\omega \in \varOmega \) for which the two previous properties hold. In the following, we omit this \(\omega \) in the notation. Let \(\varepsilon > 0, \) \(t \le T\) and choose M such that \( \int |x|\wedge M d \mu _t \ge \int |x| d \mu _t - \varepsilon . \) Then, as \(N \rightarrow \infty , \) almost surely,

$$\begin{aligned} \int |x| d \mu _t^N \ge \int |x| \wedge M d \mu _t^N \rightarrow \int |x| \wedge M d \mu _t . \end{aligned}$$


$$\begin{aligned} \liminf _N \int |x| d \mu _t^N \ge \int |x| d \mu _t - \varepsilon , \text{ such } \text{ that } \liminf _N \int |x| d \mu _t^N \ge \int |x| d \mu _{t}.\nonumber \\ \end{aligned}$$

Fatou’s lemma implies that

$$\begin{aligned} {\mathbb {E}}\left[ \liminf _N \int |x| d \mu _t^N \right]\le & {} \liminf _N {\mathbb {E}}\left[ \int |x| d \mu _t^N \right] = \liminf _N {\mathbb {E}}\left[ |X_t ^{N, 1 } |\right] \\= & {} {\mathbb {E}}\left[ |Y_t|\right] = {\mathbb {E}}\left[ \int |x| d \mu _t\right] . \end{aligned}$$

Together with (38) this implies that, almost surely,

$$\begin{aligned} \liminf _N \int |x| d \mu _t^N = \int |x| d \mu _t . \end{aligned}$$

Finally, since \( \int |x| d\mu ^N \rightarrow A \) and since A is continuous, for all t

$$\begin{aligned} \liminf _N \int |x| d \mu _t^N = \limsup _N \int |x| d \mu _t^N = \int |x| d \mu _t . \end{aligned}$$

This implies that almost surely, for all \(t \ge 0, \) \( \int |x| d \mu _t^N(x) \rightarrow \int |x| d \mu _t(x) = A_t < \infty .\) In particular, almost surely, for all \( t \ge 0, \)

$$\begin{aligned} W_1 ( \mu _t^N, \mu _t ) \rightarrow 0 \end{aligned}$$

(see e.g. Theorem 6.9 of [23]).

Step 3. Now we prove that \({\mathbb {E}}\left[ F(\mu ^N)\right] \) converges to \({\mathbb {E}}\left[ F(\mu )\right] ,\) where we recall that

$$\begin{aligned} F(\mu )= & {} \psi _1(\mu _{s_1}) \cdot \ldots \cdot \psi _k(\mu _{s_k})\int _{D({\mathbb {R}}_+,{\mathbb {R}})^2}\mu \otimes \mu (d\gamma )\varphi _1(\gamma _{s_1})\ldots \varphi _k(\gamma _{s_k})\\&\left[ g(\gamma _t)-g(\gamma _s)-\int _s^t\int _{\mathbb {R}}\int _{\mathbb {R}}Lg (\gamma _r,\mu _r,x,v)\nu _1(dv)\mu _r(dx)dr\right] , \end{aligned}$$

where \(\psi _i\in C_b({\mathcal {P}}({\mathbb {R}})),\varphi _i\in C_b({\mathbb {R}}^2)\) (\(1\le i\le k\)) and \(g\in C^3_b({\mathbb {R}}^2).\) By the boundedness of the functions \(\psi _i\) (\(1\le i\le k\)) and our boundedness Assumption 3, it is sufficient to prove the following two convergence results:

$$\begin{aligned}&{\mathbb {E}}\left[ |\psi _1(\mu ^N_{s_1}) \cdot \ldots \cdot \psi _k(\mu ^N_{s_k})-\psi _1(\mu _{s_1}) \cdot \ldots \cdot \psi _k(\mu _{s_k})|\right] \underset{N\rightarrow \infty }{\longrightarrow }0, \end{aligned}$$
$$\begin{aligned}&{\mathbb {E}}\left[ |G(\mu ^N)-G(\mu )|\right] \underset{N\rightarrow \infty }{\longrightarrow }0, \end{aligned}$$


$$\begin{aligned} G(\mu ):= & {} \int _{D({\mathbb {R}}_+,{\mathbb {R}})^2}\mu \otimes \mu (d\gamma )\varphi _1(\gamma _{s_1})\ldots \varphi _k(\gamma _{s_k})\\&\left[ g(\gamma _t)-g(\gamma _s)-\int _s^t\int _{\mathbb {R}}\int _{\mathbb {R}}L g (\gamma _r,\mu _r,x,v)\nu _1(dv)\mu _r(dx)dr\right] . \end{aligned}$$

Indeed, since the functions \(\psi _i\) (\(1\le i\le k\)) and G are bounded, we have

$$\begin{aligned} {\mathbb {E}}\left[ |F(\mu ^N)-F(\mu )|\right]\le & {} C {\mathbb {E}}\left[ |\psi _1(\mu ^N_{s_1}) \cdot \ldots \cdot \psi _k(\mu ^N_{s_k})-\psi _1(\mu _{s_1}) \cdot \ldots \cdot \psi _k(\mu _{s_k})|\right] \\&+ C {\mathbb {E}}\left[ |G(\mu ^N)-G(\mu )|\right] . \end{aligned}$$

The convergence (39) follows from dominated convergence and the fact that the function

$$\begin{aligned} m\in {\mathcal {P}}(D({\mathbb {R}}_+,{\mathbb {R}}))\mapsto \psi _1(m_{s_1})...\psi _k(m_{s_k})\in {\mathbb {R}}\end{aligned}$$

is bounded and continuous at \(\mu ,\) since \(\mu \) is supported by continuous trajectories. To prove the convergence (40), let us recall that we have already shown that

  1. 1.

    \(\underset{N}{\sup }\underset{0\le s\le t}{\sup }{\mathbb {E}}\left[ \mu ^N_s(|x|^{3/2}) \right] <\infty ,\)

  2. 2.

    \(\underset{0\le s\le t}{\sup }{\mathbb {E}}\left[ \mu _t(|x|^{3/2})\right] <\infty ,\)

  3. 3.

    \(\mu (G)=1~a.s.\) for some Borel subset \( G \subset C({\mathbb {R}}_+,{\mathbb {R}}), \) \(G \in {\mathcal {D}} ({\mathbb {R}}_+, {\mathbb {R}}),\)

  4. 4.

    a.s. \(\forall r,\) \(\mu ^N_r\) converges to \(\mu _r\) for the metric \(W_1,\)

  5. 5.

    for all \(x,x'\in {\mathbb {R}},y,y'\in {\mathbb {R}}^2,m,m'\in {\mathcal {P}}_1({\mathbb {R}}),v\in {\mathbb {R}},\)

    $$\begin{aligned}&|Lg(y,m,x,v)-Lg(y',m',x',v)|\\&\quad \le C(v)(||y-y'||_1+|x-x'|+W_1(m,m')), \end{aligned}$$

    such that \(\int _{\mathbb {R}}C(v)\nu _1(dv)<\infty ,\)

  6. 6.
    $$\begin{aligned} \int _{\mathbb {R}}\underset{x,y,m}{\sup } Lg(y,m,x,v)\nu _1(dv)<\infty . \end{aligned}$$

In order to simplify the presentation, let us assume that the function G is of the form

$$\begin{aligned} G(\mu )=\int _{D^2}\mu \otimes \mu (d\gamma )\int _s^t\int _{\mathbb {R}}\int _{\mathbb {R}}Lg(\gamma _r,\mu _r,x,v)\nu _1(dv)\mu _r(dx)dr. \end{aligned}$$

Now, let us show that \({\mathbb {E}}\left[ |G(\mu ^N)-G(\mu )|\right] \) vanishes as N goes to infinity. Clearly,

$$\begin{aligned}&|G(\mu )-G(\mu ^N)|\\&\quad \le \left| G(\mu ) - \int _{D^2}\mu ^N\otimes \mu ^N(d\gamma )\left( \int _s^t\int _{\mathbb {R}}\int _{\mathbb {R}}Lg(\gamma _r,\mu _r,x,v)\nu _1(dv)\mu _r(dx)dr\right) \right| \\&\qquad +\left| \int _{D^2}\mu ^N\otimes \mu ^N(d\gamma )\left( \int _s^t\int _{\mathbb {R}}\int _{\mathbb {R}}Lg(\gamma _r,\mu _r,x,v)\nu _1(dv)\mu _r(dx)dr\right) \right. \\&\qquad \left. - \int _{D^2}\mu ^N\otimes \mu ^N(d\gamma )\left( \int _s^t\int _{\mathbb {R}}\int _{\mathbb {R}}Lg(\gamma _r,\mu _r,x,v)\nu _1(dv)\mu ^N_r(dx)dr\right) \right| \\&\qquad +\left| G(\mu ^N) - \int _{D^2}\mu ^N\otimes \mu ^N(d\gamma )\left( \int _s^t\int _{\mathbb {R}}\int _{\mathbb {R}}Lg(\gamma _r,\mu _r,x,v)\nu _1(dv)\mu ^N_r(dx)dr\right) \right| \\&\quad =:A_1+A_2+A_3. \end{aligned}$$

We first show that \(A_1\) vanishes a.s. (this implies that \({\mathbb {E}}\left[ A_1\right] \) vanishes by dominated convergence). \(A_1\) is of the form

$$\begin{aligned} A_1 = \left| \int _{D^2}\mu \otimes \mu (d\gamma ) H(\gamma ) - \int _{D^2}\mu ^N\otimes \mu ^N(d\gamma )H(\gamma )\right| , \end{aligned}$$


$$\begin{aligned} H : \gamma \in D^2 \mapsto \int _s^t\int _{\mathbb {R}}\int _{\mathbb {R}}Lg (\gamma _r,\mu _r,x,v)\nu _1(dv)\mu _r(dx)dr\in {\mathbb {R}}. \end{aligned}$$

We just have to prove that H is continuous and bounded. The boundedness is obvious, so let us verify the continuity. Let \((\gamma ^n)_n\) converge to \(\gamma \) in \(D({\mathbb {R}}_+,{\mathbb {R}})^2\). We have

$$\begin{aligned} |H(\gamma ) - H(\gamma ^n)|&\le \int _s^t\int _{\mathbb {R}}\int _{\mathbb {R}}|H(\gamma _r,\mu _r,x,v) - H(\gamma ^n_r,\mu _r,x,v)|\nu _1(dv)\mu _r(dx)dr\\&\le \int _s^t\int _{\mathbb {R}}\int _{\mathbb {R}}C(v) ||\gamma _r-\gamma ^n_r||_1 \nu _1(dv)\mu _r(dx)dr\nonumber \\ {}&\le C\int _s^t ||\gamma _r-\gamma ^n_r||_1dr, \end{aligned}$$

which vanishes by dominated convergence: the integrand vanishes at every continuity point r of \(\gamma \) (hence for a.e. r), and, for n big enough, \(\sup _{r\le t}||\gamma ^n_r||_1 \le 2\sup _{r\le t}||\gamma _r||_1.\)

Now we show that \({\mathbb {E}}\left[ A_2\right] \) vanishes. We have

$$\begin{aligned} A_2\le & {} \int _{D^2}\mu ^N\otimes \mu ^N(d\gamma )\\&\left( \int _s^t\left| \int _{\mathbb {R}}\int _{\mathbb {R}}Lg(\gamma _r,\mu _r,x,v)\nu _1(dv)\mu _r(dx) \right. \right. \\&\left. \left. - \int _{\mathbb {R}}\int _{\mathbb {R}}Lg (\gamma _r,\mu _r,x,v)\nu _1(dv)\mu ^N_r(dx)\right| dr\right) . \end{aligned}$$

Since the function \(x\in {\mathbb {R}}\mapsto \int _{\mathbb {R}}L g(\gamma _r,\mu _r,x,v)\nu _1(dv)\) is Lipschitz continuous (with Lipschitz constant independent of \(\gamma _r\) and \(\mu _r\)), we have, by Kantorovich-Rubinstein duality (see e.g. Remark 6.5 of [23]),

$$\begin{aligned} A_2\le C\int _{D^2}\mu ^N\otimes \mu ^N(d\gamma )\int _s^t W_1(\mu ^N_r,\mu _r)dr=C\int _s^tW_1(\mu ^N_r,\mu _r)dr. \end{aligned}$$


$$\begin{aligned} {\mathbb {E}}\left[ A_2\right] \le C\int _s^t{\mathbb {E}}\left[ W_1(\mu ^N_r,\mu _r)\right] dr, \end{aligned}$$

which vanishes by dominated convergence: the integrand vanishes thanks to Step 2, and the uniform integrability follows from the fact that

$$\begin{aligned} \underset{N}{\sup }\int _s^t{\mathbb {E}}\left[ W_1(\mu ^N_r,\mu _r)^{3/2}\right] dr\le & {} C(t-s)\underset{N}{\sup }\underset{0\le s\le t}{\sup }{\mathbb {E}}\left[ \mu ^N_s(|x|)^{3/2}\right] \\&+C(t-s)\underset{0\le s\le t}{\sup }{\mathbb {E}}\left[ \mu _s(|x|)^{3/2}\right] . \end{aligned}$$

We finally show that \({\mathbb {E}}\left[ A_3\right] \) vanishes.

$$\begin{aligned} A_3\le&\int _{D^2}\mu ^N\otimes \mu ^N(d\gamma )\left( \int _s^t\int _{\mathbb {R}}\int _{\mathbb {R}}\left| Lg(\gamma _r,\mu ^N_r,x,v)\right. \right. \\&\left. \left. -Lg(\gamma _r,\mu _r,x,v)\right| \nu _1(dv)\mu ^N_r(dx)dr\right) \\ \le&\int _{D^2}\mu ^N\otimes \mu ^N(d\gamma )\left( \int _s^t\int _{\mathbb {R}}\int _{\mathbb {R}}C(v)W_1(\mu ^N_r,\mu _r)\nu _1(dv)\mu ^N_r(dx)dr\right) , \end{aligned}$$


$$\begin{aligned} {\mathbb {E}}\left[ A_3\right] \le C\int _s^t{\mathbb {E}}\left[ W_1(\mu ^n_r,\mu _r)\right] dr, \end{aligned}$$

which vanishes for the same reasons as in the previous step where we have shown that \({\mathbb {E}}\left[ A_2\right] \) vanishes. \(\square \)

1.3 Martingale measures

For the reader’s convenience we resume in this section the definition and the essential properties of martingale measures. This section is widely inspired by [8, 24] and [20]. Let \((\varOmega ,{\mathcal {F}},{\mathbb {P}})\) be a probability space and \((E,{\mathcal {E}})\) a Lusin space. Let \({\mathcal {A}}\subset {\mathcal {E}}\) be a ring, i.e. a family of sets closed under unions and set differences. We suppose moreover that \( \sigma ( {\mathcal {A}}) = {\mathcal {E}}.\) Consider a set function \(U:{\mathcal {A}}\times \varOmega \rightarrow {\mathbb {R}}\).

Definition 6.3

\(U:{\mathcal {A}}\times \varOmega \rightarrow {\mathbb {R}}\) is an \({\mathbb {L}}^2\)-valued measure on \({\mathcal {A}}\) if

  1. (i)

    \(\forall A\in {\mathcal {A}},\) \(U(A)\in {\mathbb {L}}^2(\varOmega , {\mathcal {F}},{\mathbb {P}});\)

  2. (ii)

    U is a.s. finitely additive: \(\forall A\in {\mathcal {A}},\; \forall B\in {\mathcal {A}}\) s.t. \(A\cap B=\emptyset ,\) \(U(A\cup B)=U(A)+U(B)\) a.s.;

  3. (iii)

    U is \({\mathbb {L}}^2\)-sigma-additive: \(\forall (A_n)_{n\in {\mathbb {N}}}\) s.t. \( A_n\in {\mathcal {A}}\) and \(A_n\cap A_m=\emptyset \) for \(n\ne m;\)

    $$\begin{aligned} \lim _{n\rightarrow \infty }\Vert U(\bigcup _{ k=1}^n A_k)-\sum _{k=1}^n U(A_k)\Vert _2=0. \end{aligned}$$

If \(\sup \{\Vert U(A)\Vert _2;\; A\in {\mathcal {A}}\}<\infty ,\) we say that U is finite. We say that U is sigma-finite if

  1. (i)

    there exists a sequence \((E_n)_{n\in {\mathbb {N}}} \subset {\mathcal {A}}\) such that \(E_n\subset E_{n+1}\) and \(\bigcup _n E_n=E;\)

  2. (ii)

    for all \(n\in {\mathbb {N}},\) \({\mathcal {E}}|_{E_n}\subset {\mathcal {A}}\) and U is finite on \((E_n,{\mathcal {E}}|_{E_n})\).

Let \(({\mathcal {F}}_t)_{t\ge 0}\) be a right-continuous and complete filtration on \((\varOmega , {\mathcal {F}},{\mathbb {P}}).\)

Definition 6.4

\(M=\{M_t (A),\; t\ge 0,\;A\in {\mathcal {A}}\}\) is a \(({\mathcal {F}}_t)_{t\ge 0}\)- martingale measure on \({\mathbb {R}}_+\times {\mathcal {A}}\) if

  1. (i)

    \(\forall t\ge 0,\) \(M_t\) is an \({\mathbb {L}}^2\)-valued sigma-finite measure on \({\mathcal {A}};\)

  2. (ii)

    \(\forall A\in {\mathcal {A}},\) \(M(A)=\{M_t(A),\; t\ge 0\} \) is a \(({\mathcal {F}}_t)_{t\ge 0}\) -martingale with \(M_0(A)=0 \quad a.s.;\)

  3. (iii)

    \(\forall A\in {\mathcal {A}},\) \(\forall B\in {\mathcal {A}},\) such that \(A\cap B=\emptyset ,\) M(A) and M(B) are orthogonal martingales.

To each martingale measure we can associate its intensity measure as stated in the following theorem which is due to [24].

Theorem 6.5

(Theorem 2.7 of [24]) If M is a \(({\mathcal {F}}_t)_{t\ge 0}\)-martingale measure on \( {\mathbb {R}}_+ \times {\mathcal {A}},\) there exists a random sigma-finite positive measure \(\nu (ds,dx)\) on \(({\mathbb {R}}_+\times E,{\mathcal {B}}({\mathbb {R}}_+)\otimes {\mathcal {E}}),\) \(({\mathcal {F}}_t)_{t\ge 0}\)- predictable, such that for each \(A\in {\mathcal {E}},\) the process \((\nu ((0,t]\times A))_{t\ge 0}\) is predictable, right continuous and satisfies

$$\begin{aligned} \forall A\in {\mathcal {A}},\; \forall t>0,\ \nu ((0,t]\times A)=\langle M(A)\rangle _t\; a.s. \end{aligned}$$

The measure \(\nu \) is called the intensity of M.

Since for all \(t\ge 0,\) \(A\mapsto M_t(A) \) is additive, using (iii) of Definition 6.4 and the fact that for orthogonal martingales the angle bracket process is zero,

$$\begin{aligned} \langle M(A),M(B)\rangle _t= & {} \langle M(A\setminus B)+M(A\cap B),\\&\times M(B\setminus A)+M(A\cap B)\rangle _t=\langle M(A\cap B)\rangle _t, \end{aligned}$$

which, due to the previous theorem, equals

$$\begin{aligned} \langle M(A),M(B)\rangle _t=\nu ((0,t]\times A\cap B)). \end{aligned}$$

Let M be a \(({\mathcal {F}}_t)_{t\ge 0}\)-martingale measure on \({\mathbb {R}}_+\times {\mathcal {A}}\) with intensity \(\nu \) and let \({\mathcal {P}}\) be the predictable sigma-field on \(\varOmega \times {\mathbb {R}}.\) We introduce the space

$$\begin{aligned} {\mathbb {L}}_{\nu }^2:= & {} \left\{ f:\varOmega \times {\mathbb {R}}_+\times E\rightarrow {\mathbb {R}}, \; {\mathcal {P}}\otimes {\mathcal {E}}\; \text {measurable},\right. \\&\times \left. {\mathbb {E}}\left[ \int _{{\mathbb {R}}_+\times E}f^2(\omega , s, x)\nu (\omega ,ds,dx)\right] <\infty \right\} \end{aligned}$$

and its dense subset of simple predictable functions defined by

$$\begin{aligned} {\mathcal {S}}=\left\{ h(\omega ,s,x)=\sum _{i=1}^nh_i(\omega )\mathbbm {1}_{]u_i,v_i]}(s)\mathbbm {1}_{B_i}(x),\; B_i\in {\mathcal {A}},\; h_i\in {\mathcal {F}}_{u_i}\; \text {bounded}\right\} . \end{aligned}$$

Using Itô’s method, a stochastic integral \((g\cdot M)_t\) with respect to the time variable can be constructed for any \(g\in {\mathbb {L}}_{\nu }^2 ,\) following [24]. Namely, if \(h\in {\mathcal {S}}, \) the linear mapping \(h\rightarrow \{h\cdot M_t(A), \; t\ge 0, A\in {\mathcal {A}}\},\) defined by

$$\begin{aligned} h\cdot M_t(A)=\sum _{i=1}^nh_i(M_{v_i\wedge t }(A\cap B_i)-M_{u_i\wedge t }(A\cap B_i)) , \end{aligned}$$

can be extended to \({\mathbb {L}}^2_{\nu }.\) We denote \((g\cdot M) _t:=(g\cdot M (E))_t.\) It is straightforward to show that the following important property holds.

For all \(f,g \in {\mathbb {L}}^2_{\nu },\) \(\forall t>0,\)

$$\begin{aligned} \langle f\cdot M (A),g\cdot M(B)\rangle _t=\int _{(0,t]}\int _{A\cap B} f(\omega ,s,x)g(\omega ,s,x)\nu (\omega ,ds,dx)\quad a.s.. \end{aligned}$$

Example 4

Let \((X,{\mathcal {B}},\mu )\) be a sigma-finite measure space. A white noise is a centred Gaussian process \(W=\{W(A);\; A\in {\mathcal {B}},\; \mu (A)<\infty \}\) with covariance

$$\begin{aligned} {\mathbb {E}}\left[ W(A)W(B)\right] =\mu (A\cap B). \end{aligned}$$

If \(A\cap B=\emptyset ,\) then W(A) and W(B) are independent. Moreover, W is a.s. finitely additive, since

$$\begin{aligned} {\mathbb {E}}\left[ |W(A\cup B)-W(A)-W(B)|^2\right] =0. \end{aligned}$$

Note that W is also \({\mathbb {L}}^2\)-sigma-additive on \({\mathcal {A}}=\{A\in {\mathcal {B}},\ \mu (A)<\infty \}\). Indeed, if \((A_k)_{ k \ge 0 } \) are pairwise disjoint, \(A=\bigcup _{k\ge 0} A_k,\) and \(\mu (A)<\infty ,\) then

$$\begin{aligned} {\mathbb {E}}\left[ (W(A)-\sum _{k=1}^nW(A_k))^2\right] ={\mathbb {E}}\left[ (W(\bigcup _{k\ge n+1} A_k))^2\right] =\mu (\bigcup _{k\ge n+1}A_k)\rightarrow 0 \end{aligned}$$

Also \(\sum _kVar(W(A_k))=\sum _k\mu (A_k)=\mu (A)<\infty ,\) implying that \(\sum _kW(A_k)=W(\cup _kA_k)\) a.s.

We now discuss the particular case \(X={\mathbb {R}}_+\times E,\) \({\mathcal {B}}={\mathcal {B}}({\mathbb {R}}_+)\otimes {\mathcal {E}}. \) Let \(\mu \) be \(\sigma \)-finite measure on \( (E, {\mathcal {E}}) \) and define \({\mathcal {A}}= \{A\in {\mathcal {E}},\; s.t.\; \mu ( A)<\infty \} .\) For any \( t \ge 0 \) and \( A \in {\mathcal {A}},\) we put

$$\begin{aligned} W_t(A):=W((0,t]\times A). \end{aligned}$$

For all \(A\in {\mathcal {A}},\) \(\{W_t(A),\; t\ge 0\}\) is a centred Gaussian process with independent increments, hence an \({\mathbb {L}}^2\)-martingale. Finally \(W=\{W_t(A), t\ge 0, A\in {\mathcal {A}}\}\) is an \({\mathbb {L}}^2\)-martingale measure on \({\mathbb {R}}_+\times {\mathcal {A}},\) with intensity \( \nu (ds, dx) = ds \mu ( dx),\) with respect to its natural filtration \(({\mathcal {F}}_t)_{t\ge 0},\) where \({\mathcal {F}}_t:=\sigma \{W_u(A),\; u\le t; A\in {\mathcal {A}}\}.\) Finally, for any \(f\in {\mathbb {L}}^2_{\nu },\) we denote the stochastic integral \((f\cdot W)_t=: \int _0^t\int _{E}f(s,x)d W(s,x).\)

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Erny, X., Löcherbach, E. & Loukianova, D. White-noise driven conditional McKean–Vlasov limits for systems of particles with simultaneous and random jumps. Probab. Theory Relat. Fields 183, 1027–1073 (2022).

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