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A branching particle system approximation for a class of FBSDEs
Probability, Uncertainty and Quantitative Risk volume 1, Article number: 9 (2016)
Abstract
In this paper, a new numerical scheme for a class of coupled forward-backward stochastic differential equations (FBSDEs) is proposed by using branching particle systems in a random environment. First, by the four step scheme, we introduce a partial differential Eq. (PDE) used to represent the solution of the FBSDE system. Then, infinite and finite particle systems are constructed to obtain the approximate solution of the PDE. The location and weight of each particle are governed by stochastic differential equations derived from the FBSDE system. Finally, a branching particle system is established to define the approximate solution of the FBSDE system. The branching mechanism of each particle depends on the path of the particle itself during its short lifetime ε=n −2α, where n is the number of initial particles and \({\alpha }<\frac {1}{2}\) is a fixed parameter. The convergence of the scheme and its rate of convergence are obtained.
Introduction
Since the work of Pardoux and Peng (1990), forward-backward stochastic differential equations (FBSDEs) have been extensively studied and have found important applications in many fields, including finance, risk measure, stochastic control and so on (cf. Cvitanić and Ma (1996); El Karoui et al. (1997); Ma and Yong (1999); Xiong and Zhou (2007), and Yong and Zhou (1999)). For instance, we consider a risk minimizing economic management problem. x(·) denotes an economic quantity, which can be interpreted as cash-balance, wealth and an intrinsic value in different fields. Suppose that x(·) is governed by
where v(·) is the control strategy of a policymaker and A(·),B(·),C(·),D(·) are bounded and deterministic. Let ρ(x v(1)) denote the risk of the economic quantity x v(1), where the risk measure is convex in the sense of Föllmer and Schied (1999). Recently, Rosazza Gianin (2006) established the relationship between the risk measure ρ(·) and the g-expectation \(\mathcal {E}_{g}^{v}\) (see Peng (2010)):
where the functional \(g: [0,1]\times \mathbb {R}\times \mathbb {R}\times \mathbb {R}\rightarrow \times \mathbb {R}\) satisfies g(t,y,0)=0 and is the generator of the following BSDE:
Thus, the objective of the policymaker is equivalent to minimizing
subject to the FBSDE.
In previous work, Ma et al. (1994) studied the solvability of the adapted solution to the FBSDEs, in particular, they designed a direct scheme, called the four step scheme to solve the FBSDEs explicitly. However, in most cases, it is often difficult to get the solution in closed form so it is important to study numerical methods for solving FBSDEs. Following the earlier works of Bally (1997) and Douglas et al. (1996), various efforts have been made to find efficient numerical schemes for FBSDEs. In the decoupled forward-backward case, these include the PDE method in the Markovian case (e.g., Chevance (1997)), random walk approximations (e.g., Briand et al. (2001) and Ma et al. (2002)), Malliavin calculus and Monte-Carlo method (e.g., Zhang (2004), Ma and Zhang (2005), and Bouchard and Touzi (2004)) and so on. However, in the case of coupled FBSDEs, to our knowledge, there are very few works in the literature, such as Milstein and Tretyakov (2006), Delarue and Menozzi (2006), Cvitanić and Zhang (2005), and Ma et al. (2008).
In this paper, we are interested in investigating a new numerical scheme for a class of coupled FBSDEs by a branching particle system approximation. There are various studies about particle system representations for stochastic partial differential equations with application to filtering since the pioneering work of Crisan and Lyons (1997) and Del Moral (1996). Here we list a few which are closely related to the present work: Kurtz and Xiong (1999); Kurtz and Xiong (2001), Crisan (2002), Xiong (2008), Liu and Xiong (2013), Crisan and Xiong (2014).
Particle system representations for FBSDEs are studied in Henry-Labordère et al. (2014) when the forward part is independent of the backward one, namely, the decoupled case. In this case, the approximation of the solution of a PDE and that of the forward SDE can be constructed separately. However, for the coupled case, the construction of the branching particle system must consider both the PDE and the SDE in a delicate manner. This paper can be regarded as a first attempt in this direction. One of the main advantages of this method is the circumventing of the computation of conditional expectations via regression methods.
Let \(\left (\Omega,,\mathcal {F},\{\mathcal {F}_{t}\}_{0\leq t\leq T},\mathbb {P}\right)\) be a filtered complete probability space, where \(\{\mathcal {F}_{t}\}_{0\leq t\leq T}\) denotes the natural filtration generated by a standard Brownian motion \(\{W_{t}\}_{0\leq t\leq T}, \mathcal {F}=\mathcal {F}_{T}\) and T>0 is a fixed time horizon. We consider the following FBSDE in the fixed duration [0,T]:
where \(b:\mathbb {R}^{d}\times \mathbb {R}^{k}\rightarrow \mathbb {R}^{d}\), \(\sigma :\mathbb {R}^{d}\rightarrow \mathbb {R}^{d\times l}\), \(g:\mathbb {R}^{d}\times \mathbb {R}^{k}\times \mathbb {R}^{k\times l}\rightarrow \mathbb {R}^{k}\) and \(f:\mathbb {R}^{d}\rightarrow \mathbb {R}^{k}\).
In what follows, we make the following assumption:
(A1) The generator g has the following form: for z=(z 1,⋯,z l ),
and b(x,y),σ(x),g(x,y,z),f(x),C(x,y) and D(x,y) are all bounded and Lipschitz continuous maps with bounded partial derivatives up to order 2. Furthermore, the matrix σ σ ∗ is uniformly positive definite, and the function f is integrable. Here σ ∗ denote the transpose of the matrix σ.
Remark 1.1
For the generators associated with g-expectation, the condition g(y,0)=0 (we omit the variable t) together with an extra differentiability condition, we have
The case of D j (y,z) depending on z is more technically demanding in the construction of the branching particle systems. We hope to return to this case in a future work.
Relying on the idea of the four step scheme, we know that the solution to the above FBSDE has the relation Y(t)=u(t,X(t)),Z(t)=∂ x u(t,X(t))σ(X(t)), where u(t,x) is a solution to the PDE
and
with a ij =(σ σ T) ij , σ=(σ 1,⋯,σ l ) and b i being the ith coordinate of b.
For 0≤t≤T, assume v(t,x)=u(T−t,x). Note that
Remark 1.2
According to Proposition 4.2 in Ma et al. (1994), the above nonlinear parabolic partial differential equation has a unique solution.
The nonlinear parabolic partial differential Eq. (1.2) can be written as:
By rules of derivative, we have
where
and
Comparing this equation with (1.1) inKurtz and Xiong (1999) formally, we now construct an infinite particle system \(\{X_{i}(t): i\in \mathbb {N}\}\) with locations in \(\mathbb {R}^{d}\) and time varying weights \(\{A_{i}(t): i\in \mathbb {N}\}\) governed by the following equations: for 0<t≤T,i=1,2,⋯
with i.i.d initial random sequence \(\{(X_{i}(0), A_{i}(0)), i\in \mathbb {N}\}\) taking values in \(\mathbb {R}^{d}\times \mathbb {R}\), where \(\{B_{i}(t), i\in \mathbb {N}\}\) are independent standard Brownian motions and
where \({C_{b}^{2}}(\mathbb {R}^{d})\) denotes the collection of all bounded functions with bounded continuous derivatives up to order 2. In Theorem 2.2, we will show that the density function of V(t) determined by the above infinite particle system is v(t,x), which is exactly the solution to PDE (1.2).
The rest of this paper is organized as follows. In “Particle system approximation” Section, we construct infinite and finite particle systems to respectively get the approximate solution of the PDE and prove the convergent results. “Branching particle system approximation” Section is devoted to the formulation of a branching particle system to represent the approximate solution of the PDE. In “Numerical solution” Section, we present the numerical solution of the FBSDE system and its error bound. Finally, “Conclusion” Section concludes the paper.
Particle system approximation
For two integrable functions v 1,v 2, we define their distance
Now we construct infinite particle systems governed by the following stochastic differential equations: for any fixed δ>0,t∈(0,T],i=1,2,⋯
where p δ is the heat kernel given by
In this paper we regard K with or without subscript as a constant which assumes different values at different places. By the boundedness of the coefficient assumed in (A1), we can verify the following condition:
(I)
We also make the following condition on the initial data:
(II) \(\left \{\left (A_{i}(0), X_{i}(0)\right),\left (A^{\delta }_{j}(0), X^{\delta }_{j}(0)\right)\right \}\) is an i.i.d sequence and
Theorem 2.1
Assume that \(\{A^{\delta }_{i}(0), X^{\delta }_{i}(0)\}\) is i.i.d and independent of {B i }. Under (A1), for every \(\phi \in {C_{b}^{2}}(\mathbb {R}^{d})\), we have
and
where
and
while \(\tilde {b}_{i}\) is the ith coordinator of \(\tilde {b}\) and a ij =(σ σ ∗) ij .
Proof
By the law of large numbers, we have
Applying Itô’s formula to (2.1),
where ∇∗ denotes the transpose of the gradient operator ∇.
By the boundedness of \(\tilde {c}\), it is easy to show that there is a constant K such that
Hence, the martingale term on the right hand side of (2.3) can be estimated as follows:
Integrating and averaging both sides of (2.3), we see that (2.2) holds. □
Theorem 2.2
The solution to particle system (1.4) is unique and its density function is the solution to partial differential equation (1.3).
Proof
Firstly, we know for any fixed i=1,2,⋯, the SDE
has a unique solution because of the Lipschitz condition on the coefficients. Since we know the partial differential Eq. (1.3) has a unique solution, then
is solvable. The i.i.d property of {(A i (0),X i (0))} and independence of {B i },i=1,2,⋯ ensures that V(t) is well-defined.
Following similar steps as in Theorem 2.1, for any \(\phi \in {C_{b}^{2}}(\mathbb {R}^{d})\) with compact support, it is easy to get
Since (2.5) is a parabolic PDE satisfying the uniform elliptic condition, by standard PDE theory, it is well-known that V(t) is absolutely continuous with respect to the Lebesgue measure. We denote the density function by v(t,x). Then,
Therefore
In differential form, we have
i.e. v(t,x) is the solution to Eq. (1.3). □
Remark 2.1
For any \(\phi \in {C_{b}^{2}}(\mathbb {R}^{d})\), it is obvious that
where i=1,2,⋯
Next we introduce a finite particle system to get the approximation solution: for fixed δ>0,t∈(0,T],
where i=1,2,⋯n. The initial values are given as \(X_{i}^{n,\delta }(0)=X_{i}(0), A_{i}^{n,\delta }(0)=A_{i}(0)\).
Similar to (2.4), we can prove that
Let
It follows from (2.4) and (2.7), we have
For simplicity, take notation
and
Then
and
Proposition 2.1
Under conditions (I) and (II), we have
and
Proof
For simplicity of notation, we assume that τ n,δ ≥T. Let
and
Then
Note that
where the last inequality follows from Cauchy-Schwarz inequality and the fact that for τ n,δ ≥T,
On the other hand,
Let
and
Similarly,
Then,
and
Adding (2.12) and (2.13), for t≤T, we have
By Gronwall’s inequality, we have
Then, we have
and
□
Lemma 2.1
For 0≤t≤T, we have
Proof
By the boundedness of \(\tilde {c}\), we have
Then,
where the last inequality follows from (2.8), (2.15) and the fact that ρ≤2. □
Lemma 2.2
For 0≤t≤T, we have \(\mathbb {E}\rho \left (\tilde {v}^{n,\delta }(t), v^{\delta }(t)\right)\leq \frac {K_{\delta }}{\sqrt {n}}\).
Proof
where g is the joint probability density of \((A^{\delta }_{1}(t),X^{\delta }_{1}(t))\). □
Combining Lemmas 2.1 and 2.2, we get the approximation of v n,δ to v δ as δ δ being fixed and n→∞. The next lemma estimates the distance between v δ and We adapt the argument of Crisan and Xiong (Crisan and Xiong 2014) to the current setup.
Lemma 2.3
There exists a constant C 3(T), such that
Proof
By the convolution form of (2.2), we have
where p t (y,x) is the transition density of the reflecting diffusion with generator L. By Theorem 6.4.5 in Friedman (1975), there are constants K 1,K 2 such that
Plugging it to (2.16), it follows from the boundedness of \(v^{\delta }(0,\cdot),\ \tilde {b},\ \tilde {c}\), we get
Define \(a_{t}=\sup \limits _{x\in \mathbb {R}^{d}}v^{\delta }(t,x)\). Then
By an extended Gronwall’s inequality, we get
□
Lemma 2.4
There exists a constant C 4(T), such that
Proof
Note that
where
and
Recalling that p t (y,x) is the transition density of the reflecting diffusion with generator L, the convolution form of (2.17) is as follows:
By Theorem 6.4.5 in Friedman (1975), there are constants K 1,K 2 such that
Therefore,
Define \(b_{t}=\sup \limits _{x\in \mathbb {R}^{d}}||\nabla v^{\delta }(t,x)|\). Then
By an extended Gronwall’s inequality, we get
□
As a consequence of Lemmas 2.3 and 2.4, we have the following lemma.
Lemma 2.5
There exists a constant K T , such that \(\mathbb {E}\rho \left (v^{\delta }(t),v(t)\right)\leq K_{T}\sqrt {\delta }\).
Proof
We define \(\bar {v}^{\delta }=v^{\delta }-v\). Then
Therefore,
By the convolution form of the above equation, we get
Set \(c_{t}(y)=|\bar {v}^{\delta }(t,y)|\), then
Set \(c_{t}=\int _{\mathbb {R}^{d}}c_{t}(y)dy\), then
here we used, in the first inequality, the integrability condition on v(0,·)=f.
Applying Gronwall’s inequality, we get
Then, we have
□
Remark 2.2
Set \(c_{t}^{\infty }=\sup \limits _{y\in \mathbb {R}^{d}}c_{t}(y)\), similarly, we have
Theorem 2.3
The distance of v n,δ(t) to v(t) is bounded by \(\frac {K_{\delta,T}}{\sqrt {n}}+K_{T}\sqrt {\delta }\).
Proof
Combining the conclusions from Lemmas 2.1, 2.2 and 2.5, we get
□
Branching particle system approximation
Note that K T of Theorem 2.3 above is of exponential growth as T increases, and hence, the error of the approximation grows exponentially fast. To avoid this drawback of the numerical scheme, we introduce a branching particle system to modify the weights of the particles at the time-discretization steps.
Firstly, we rewrite our infinite particle systems governed by the following stochastic differential equations: for any fixed δ>0,t∈(0,T],i=1,2,⋯
where
and
For \(V_{i}\in M(\mathbb {R}^{d}), i=1,2\), the Wasserstein metric is given by
where
and
Now, we are ready to construct the branching particle system. For fixed δ>0,ε=n −2α,0<α<1, there are n particles initially, each with weight 1 at locations \(X_{i}^{n,\delta,{\epsilon }}(0), i=1,2,\cdots,n\) which are i.i.d random variables in \(\mathbb {R}^{d}\). Assume the time interval is [0,T] and \(N^{*}=\left [\frac {T}{{\epsilon }}\right ]\) which is the largest integer not greater than \(\frac {T}{{\epsilon }}\). Define ε(t)=j ε for j ε≤t<(j+1)ε. In the time interval [j ε,(j+1)ε),j≤N ∗, there are \({m_{j}^{n}}\) particles alive and their locations and weights are determined as follows: for \(i=1,2,\cdots,{m_{j}^{n}}\),
where the initial values are defined as: \(X^{n,\delta,{\epsilon }}_{i}(0)=x, A_{i}^{n,\delta,{\epsilon }}(0,0)=1, {m_{0}^{n}}=n\).
At the end of the interval, the ith particle branches into \(\xi _{j+1}^{i}\) offsprings such that the conditional expectation and the conditional variance given the information prior to the branching satisfy
and
where \(\gamma _{j+1}^{i}\) is arbitrary and
To minimize \(\gamma _{j+1}^{i}\), take
where {x}=x−[x] is the fraction of x. In this case, we have
Now we define the unnormalized approximate filter as following:
A preliminary identity
Following similar steps as in Theorem 2.1, for every \(\phi \in {C_{b}^{2}}(\mathbb {R}^{d})\), we have
where \(\tilde {L}_{V^{\delta }(s)}\) is defined as
Now we imitate section 6.5 ofXiong (2008) to define a backward PDE for s∈[0,t] such that
Note that ψ s depends on t. Simple calculations show that \(\frac {\partial \left <V^{\delta }(t),\psi _{t}\right >}{\partial t}=0\).
Recall that g ∗ denote the transpose of a vector or matrix. Let
and
Theorem 3.1
For j ε≤t<(j+1)ε,j≤N ∗, for any ψ r ,r∈[j ε,t] satisfying Eq. (3.2), we have
Proof
For simplicity of notation, we only consider the case when j=0 and denote \(A_{i}^{n,\delta,{\epsilon }}\left (0,t\right)\) and \(X_{i}^{n,\delta,{\epsilon }}\left (t\right)\) by A(t) and X(t).
By the independent increments of B(t) and note that \({\theta _{g}^{B}}\) is a martingale, we know for r∈[0,t] and \(x\in \mathbb {R}^{n}\),
and
By Itô’s formula, we have
Then
By taking r=0 and r=t in (3.4), respectively, we have
Getting expectation on both sides,
By Itô’s formula, we know
and
Then
and
By Lemma 6.20 inXiong (2008), the following equation holds:
Similarly, we know Eq. (3.3) holds. □
Convergence of V n,δ,ε(t) to V δ(t) at any point t∈[0,T]
Proposition 3.1
For j=1,2,⋯,N ∗, there exists a constant K, such that
Proof
By the definition of \({m_{j}^{n}}\), we have
By induction, we have \(\mathbb {E} {m_{j}^{n}}\leq \mathbb {E} m_{j-2}^{n}e^{2K{\epsilon }}\leq \cdots \leq \mathbb {E} {m_{0}^{n}}e^{jK{\epsilon }}\leq ne^{KT}.\) □
Proposition 3.2
For j=1,2,⋯,N ∗, there exists a constant K, such that
Proof
Let \(a_{j}=\mathbb {E}\left ({m_{j}^{n}}\right)^{2}\), by induction
□
Lemma 3.1
For any t∈[0,T], there exists a constant K, such that
Proof
By the definition of measures, we have
Therefore
□
Lemma 3.2
For any t∈[0,T], there exists a constant K T , such that
Proof
Since
therefore
□
In the following part, we first estimate the distance between V n,δ,ε(t) and V δ(t) at the subinterval endpoints, i.e. the case that t=N ε where N is a nonnegative integer less or equal to N ∗. Then we discuss the convergence of V n,δ,ε(t) to V δ(t) at any point t∈[0,T].
Let ψ s ,0≤s≤N ε be the solution to the PDE (3.2) with t replaced by N ε. Note that <V n,δ,ε(N ε),ψ N ε >−<V n,δ,ε(0),ψ 0> can be written as a telescopic sum
As ψ N ε =ϕ, we get
Since
then
and
Lemma 3.3
There exist a constant K such that \(\mathbb {E} \left (I_{1}\right)^{2}\leq Kn^{-(1-2{\alpha })}\).
Proof
Note that
and for any j ′<j, we have
therefore,
□
Remark 3.1
To guarantee the convergence of V n,δ,ε(N ε) to V δ(N ε), we will only consider \(0<{\alpha }<\frac {1}{2}\) in the following paragraph.
Theorem 3.2
For any t∈[0,T],δ>0,ε=n −2α and \(0<{\alpha }<\frac {1}{2}\), there exists a constant K δ,T , such that \(\mathbb {E}{\rho ^{2}_{1}}\left (V^{n,\delta,{\epsilon }}(t), V^{\delta }(t)\right)\leq K_{T}n^{-(1-2{\alpha })}+K_{\delta,T}n^{-2{\alpha }}\).
Proof
From the Eq. (3.3), it is obvious that I 2 can be separated into the sum of three parts:
where
and
Naturally,
Now consider
Then, we have
and
Consequently
Similarly,
Therefore,
With the result from Lemma 3.3, it is easy to get
By our definition of V n,δ,ε(0), we have
By triangle inequality, we have
With the results from Lemmas 3.1 and 3.2,
By Gronwall’s inequality, we have
□
We define \(v^{n,\delta,{\epsilon }}(t,x)=\frac {1}{n}\sum _{l=1}^{{m_{j}^{n}}}p_{\delta }\left (x-X_{l}^{n,\delta,{\epsilon }}(t)\right)\), i.e. the smooth density of V n,δ,ε(t), as the numerical approximation of v(t,x) and u n,δ,ε(t,x)=v n,δ,ε(T−t,x) as the numerical approximation of u(t,x). Then, we have the following corollary:
Corollary 3.1
For any \(t\in [0,T], 0<\alpha <\frac {1}{2}\), there exists a constant K δ , such that
Proof
We set u δ(t,x)=v δ(T−t,x). Then
□
Numerical solution
In this section, we will give a numerical solution of the FBSDE (1.1) based on the branching particle-system representations and derive some estimates about the convergence rate.
We also denote by \(\tilde {u}^{n,\delta,{\epsilon }}(t,x)\) another numerical approximation of u(t,x) obtained by the same numerical scheme with u n,δ,ε(t,x). Firstly, we apply the Euler Scheme to approximate X(t) of the FBSDE (1.1). Define the numerical solution \(\tilde {X}^{n,\delta,{\epsilon }}(t)\) satisfying:
Theorem 4.1
The convergence of \(\tilde {X}^{n,\delta,{\epsilon }}(t)\) to X(t) is bounded by K δ,T (n −(1−2α)∨n −2α)+K T δ.
Proof
By Eqs. (1.1) and (4.1), we have
Applying the Burkholder-Davis-Gundy and Hölder’s inequalities, we obtain
It follows from Eq. (4.1) that
and hence,
The other term in Eq. (4.2) can be estimated similarly. Therefore,
By Gronwall’s inequality, we have
□
Then, by the result of the four step scheme, we define \(Y^{n,\delta,{\epsilon }}(t)=u^{n,\delta,{\epsilon }}(t,\tilde {X}^{n,\delta,{\epsilon }}_{t})\) as the numerical solution of Y(t) in FBSDE (1.1). We have the following theorem:
Theorem 4.2
The convergence of Y n,δ,ε(t) to Y(t) is bounded by \(K_{\delta,T}\left (n^{-\frac {1-2{\alpha }}{2}}\vee n^{-{\alpha }}\right)+K_{T}\sqrt {\delta }\).
Proof
where \(J_{T-t}^{n,\delta,{\epsilon }}(x)\) is the probability density of \(\tilde {X}^{n,\delta,{\epsilon }}_{t}\). □
Conclusion
In this paper we investigated a new numerical scheme for a class of coupled forward-backward stochastic differential equations. Combining the four step scheme and the Euler Scheme, we defined a new numerical solution of the FBSDE system by branching particle systems in a random environment and proved related convergent results. Prior to our work, there was no literature about particle system representations for the numerical approximations of FBSDE systems.
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Acknowledgments
We would like to thank an anonymous referee for his/her constructive suggestions which lead to the improvement of this article.
Liu acknowledges research support by National Science Foundation of China NSFC 11501164. Xiong acknowledges research support by Macao Science and Technology Fund FDCT 076/2012/A3 and Multi-Year Research Grants of the University of Macau No. MYRG2014-00015-FST and MYRG2014-00034-FST.
Authors’ contributions
HL conducted earlier derivation of the results. DC made substantial changes over the original derivations and obtained many new proofs. JX posed the problems and supervised the whole process. The manuscript is handled by JX. All authors read and approved the final manuscript.
Competing interests
The authors declare that they have no competing interests.
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Chang, D., Liu, H. & Xiong, J. A branching particle system approximation for a class of FBSDEs. Probab Uncertain Quant Risk 1, 9 (2016). https://doi.org/10.1186/s41546-016-0007-y
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DOI: https://doi.org/10.1186/s41546-016-0007-y
Keywords
- Forward-backward stochastic differential equation
- Partial differential equations
- Branching particle system
- Numerical solution