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Université Savoie Mont Blanc
Approximation rate of BSDEs using randomwalk
Céline Labart, Rennes, 2018-07-06
Joint work with Christel Geiss and Antti Luoto (University ofJÿvaskÿla, Finland)
Introduction Numerical scheme and convergence results Numerical Examples Generalization to a diffusion process
Outline
Introduction
Numerical scheme and convergence results
Numerical Examples
Generalization to a diffusion process
Introduction Numerical scheme and convergence results Numerical Examples Generalization to a diffusion process
Introduction
Numerical scheme and convergence results
Numerical Examples
Generalization to a diffusion process
Introduction Numerical scheme and convergence results Numerical Examples Generalization to a diffusion process
Random walk approximation of the Brownian motion
Let tk := kh, k = 0, · · · ,n be a regular grid of [0,T ], where h = Tn and
define
Bnt :=√
h[t/h]∑k=1
εk , (εk )k=1,··· ,n i.i.d. Bernoulli r.v.:
P(εk = 1) = P(εk = −1) =12
• Donsker’s Theorem:
(Bnt )t∈[0,T ] → (Bt )t∈[0,T ] in distribution.
• Wanted:
Bnt → Bt in L2 for all t ∈ [0,T ] with a convergence rate.
Introduction Numerical scheme and convergence results Numerical Examples Generalization to a diffusion process
Random walk constructed by Skorohod embedding
τ0 = 0, τk := inf{t > τk−1 : |Bt − Bτk−1 | =√
h}
0
Bτ1
Bτ2
Bτ3
T
− 2 h
2 h
3 h
− h
h ● ● ● ●
● ●
●
● ●
Introduction Numerical scheme and convergence results Numerical Examples Generalization to a diffusion process
Properties of (Bτk )k and (τk )k
• (Bτk − Bτk−1 )k≥1d= (√
hεk )k≥1
• (τk − τk−1)k are i.i.d. ∼ τ := inf{t > 0 : |Bt | =√
h}• It holds E(τk ) = tk , E|τk − tk |2 = 2
3 tk h, k = 1, · · · ,n.
E|Bτk − Btk |2 = E|τk − tk | ≤√
23 tk h
• random walk constructed from B: Bnt :=√
h∑[t/h]
k=1 (Bτk − Bτk−1 )
Since Bntk = Bτk we have for t ∈ [tk , tk+1)
E|Bnt − Bt |2 ≤ 2E|Bτk − Btk |2 + 2E|Btk − Bt |2
≤ 2√
23 tk T
n + 2 Tn ≤ 4 T√
n
Introduction Numerical scheme and convergence results Numerical Examples Generalization to a diffusion process
Forward Backward Stochastic Differential Equations
Xt = x +∫ t
0b(r ,Xr )dr +
∫ t
0σ(r ,Xr )dBr , 0 ≤ t ≤ T
Yt = g(XT ) +∫ T
tf (s,Xs,Ys,Zs)ds −
∫ T
tZsdBs
replace (Bt )t∈[0,T ] by a random walk (Bnt )t∈[0,T ] what kind of convergence
((X nt )t∈[0,T ], (Y n
t )t∈[0,T ], (Z nt )t∈[0,T ])→ ((Xt )t∈[0,T ], (Yt )t∈[0,T ], (Zt )t∈[0,T ])?
Briand, Delyon and Mémin (2001)If b, σ, f and g are Lipschitz and (Bn
t )t∈[0,T ] such that
sup0≤t≤T |Bnt − Bt | → 0, n→∞, in probability, then
sup0≤t≤T
|Y nt − Yt |2 +
∫ T
0|Z n
s − Zs|2ds → 0 when n→∞ in probability.
Introduction Numerical scheme and convergence results Numerical Examples Generalization to a diffusion process
Other results
• Toldo (2005) extends the previous result to BSDEs with randomterminal time
• Numerical schemes : Ma, Protter, San Martin and Torres (2002),Peng, Xu (2008) and Mémin, Peng, Xu (2008) (Implicit and explicitschemes for BSDEs and RBSDEs), Martinez, San Martin and Torres(2011) (RBSDEs), Janczak (2008, 2009) (generalized RBSDEs withrandom terminal time)
=⇒ convergence in probability or weak convergence: no rate
Introduction Numerical scheme and convergence results Numerical Examples Generalization to a diffusion process
Introduction
Numerical scheme and convergence results
Numerical Examples
Generalization to a diffusion process
Introduction Numerical scheme and convergence results Numerical Examples Generalization to a diffusion process
DiscretizationWe consider the BSDE
Yt = g(BT ) +∫ T
tf (s,Ys,Zs)ds −
∫ T
tZsdBs, 0 ≤ t ≤ T
and its approximation
Y ntk = g(Bn
T ) + hn−1∑m=k
f (tm+1,Y ntm ,Z
ntm )−
n−1∑m=k
Z ntm (Bn
tm+1− Bn
tm )︸ ︷︷ ︸law=√
hεm+1
.
For n large enough: ∃! (Fτk )k -adapted solution (Y ntk ,Z
ntk )n−1
k=0 .The corresponding scheme:
Y ntn := g
(n∑
k=1
√hεk
)
Z ntk−1
:=Eτk−1 [Y n
tkεk ]
√h
, k = 1, ...,n
Y ntk−1
:= Eτk−1 [Yntk ] + f (tk ,Y n
tk−1,Z n
tk−1)h
Introduction Numerical scheme and convergence results Numerical Examples Generalization to a diffusion process
Discretization : approximation of Z
From Zhang’s paper (2005), we have
Zt = Et
(g(BT )
BT − Bt
T − t
)+ Et
(∫ T
tf (s,Ys,Zs)
Bs − Bt
s − tds
),
LemmaFor all k = 0,1, . . . ,n − 1, we have
Z ntk = Eτk
(g(Bn
T )Bn
tn − Bntk
tn − tk
)+ hEτk
(n−1∑
m=k+1
f (tm+1,Y ntm ,Z
ntm )
Bntm − Bn
tktm − tk
).
Introduction Numerical scheme and convergence results Numerical Examples Generalization to a diffusion process
Convergence resultsAssume: ∀(t , y , z), (t ′, y ′, z ′) ∈ [0,T ]× R2,
|f (t , y , z)− f (t ′, y ′, z ′)| ≤ Lf (√
t − t ′ + |y − y ′| + |z − z ′|),
∀(x , x ′) ∈ R2, |g(x)− g(x ′)| ≤ Cg(1 + |x |p0 + |x ′|p0 )|x − x ′|α.
Theorem (C.G., Labart, Luoto)
sup0≤t<T
E|Yt − Y nt |2 ≤ C1h
α2 ,
E∫ T
0|Zt − Z n
t |2dt ≤ C2hβ for β ∈ (0, α2 )
where C1 depends on Lf ,T ,Cg ,p0 and α and C2 additionally on β.
The reason for β :E|Zt − Z n
t |2 ≤ C0hα2
T−tk+ C1
hα2
(T−t)1−α2
1t 6=tk for t ∈ [tk , tk+1), k = 0, ...,n− 1,
Introduction Numerical scheme and convergence results Numerical Examples Generalization to a diffusion process
Introduction
Numerical scheme and convergence results
Numerical Examples
Generalization to a diffusion process
Introduction Numerical scheme and convergence results Numerical Examples Generalization to a diffusion process
Example 1
dYt = −(Yt + Zt )dt + ZtdBt ,
YT = B2T
The explicit solution is given by
Yt = eT−t ((Bt + T − t)2 + T − t), Zt = 2eT−t (Bt + T − t)
We compute by Monte Carlo E(|Yt − Y nt |2) and E(|Zt − Z n
t |2) for differentvalues of n.
g is a locally Lipschitz function : the convergence should go faster than1√n .
Introduction Numerical scheme and convergence results Numerical Examples Generalization to a diffusion process
Figure: log(Error on Y ) for different values of log(n)
We get a slope of −0.46
Introduction Numerical scheme and convergence results Numerical Examples Generalization to a diffusion process
Example 1 : error on Z
Figure: log(Error on Z ) for different values of log(n)
We get a slope of −0.48
Introduction Numerical scheme and convergence results Numerical Examples Generalization to a diffusion process
Example 2
dYt = −(Yt + Zt )dt + ZtdBt ,
YT =√|BT |
The solution is given by
Yt = eT−t
2 E(√|BT−t + Bt |eBT−t )
We compute by Monte Carlo E(|Yt − Y nt |2) and E(|Zt − Z n
t |2) for differentvalues of n.
g is a 12 -Hölder function : the convergence should go faster 1
n14
.
Introduction Numerical scheme and convergence results Numerical Examples Generalization to a diffusion process
Example 2 : error on Y
Figure: log(Error on Y ) for different values of log(n)
We get a slope of −0.56!!!!
Introduction Numerical scheme and convergence results Numerical Examples Generalization to a diffusion process
Introduction
Numerical scheme and convergence results
Numerical Examples
Generalization to a diffusion process
Introduction Numerical scheme and convergence results Numerical Examples Generalization to a diffusion process
The general case
Xt = x +∫ t
0b(r ,Xr )dr +
∫ t
0σ(r ,Xr )dBr , 0 ≤ t ≤ T
Yt = g(XT ) +∫ T
tf (s,Xs,Ys,Zs)ds −
∫ T
tZsdBs
• b, σ ∈ C0,2b and 1
2 -Hölder in time, unif. in space• the first and second derivatives w.r.t. the space variable are
assumed to be γ-Hölder continuous (for some γ ∈]0,1], w.r.t. theparabolic metric d((x , t), (x ′, t ′)) = (|x − x ′|2 + |t − t ′|) 1
2 ) on allcompact subsets of [0,T ]× R.
• σ(t , x) ≥ δ > 0
Introduction Numerical scheme and convergence results Numerical Examples Generalization to a diffusion process
Numerical Scheme for X and Y
X n0 = x ,
X ntk = x +
k∑j=1
b(tj ,X ntj−1
)h +√
hk∑
j=1
σ(tj ,X ntj−1
)εj
Y ntk = g(X n
T ) + hn−1∑m=k
f (tm+1,X ntm ,Y
ntm ,Z
ntm )−
√h
n−1∑m=k
Z ntmεm+1.
and
Y ntk = Eτk
(g(X n
tn ) + hn−1∑m=k
f (tm+1,X ntm ,Y
ntm ,Z
ntm )
)
Z ntk = Eτk g(X n
tn )εk+1√h
+ Eτk
(√
hn−1∑
m=k+1
f (tm+1,X ntm ,Y
ntm ,Z
ntm )εk+1
)
Introduction Numerical scheme and convergence results Numerical Examples Generalization to a diffusion process
Approximation of Z
Zt = Et
(g(XT )N t
T +∫ T
tf (r ,Xr ,Yr ,Zr )N t
r dr
)σ(t ,Xt ), Zhang (2005)
where N tv := 1
v−t
∫ vt
∇Xsσ(s,Xs)∇Xt
dBs is the Malliavin weight of first order
Z ntk :=Eτk
(g(X n
T )Nn,τkτn
+ hn−1∑
m=k+1
f (tm+1,X ntm ,Y
ntm ,Z
ntm )Nn,τk
τm
),
Nn,τkτm
=1
tm − tk
m∑l=k+1
∇X ntl−1
σ(tl−1,X ntl−1
)∇X ntk
(Bntl − Bn
tl−1)
But Z ntk 6= Z n
tk , and E|Z ntk − Z n
tk |2 → 0 only for g′′ α-Hölder, f sufficiently
smooth
Introduction Numerical scheme and convergence results Numerical Examples Generalization to a diffusion process
Convergence Results
Theorem (C. Geiss, C.L., A. Luoto)Let b, σ and f satisfy the above assumptions. Let g′′ be a locallyα-Hölder continuous function and assume additionally that all first andsecond partial derivatives w.r.t. the variables x , y , z of b(t , x), σ(t , x) andf (t , x , y , z) exist and are bounded Lipschitz functions w.r.t. thesevariables, uniformly in time. Then for all t ∈ [0,T ) and large enough n, wehave
E0,x |Yt − Y nt |2 ≤ Cψ(x)h
12
E0,x |Zt − Z nt |2 ≤ Cψ(x)h
12∧α,
where ψ(x) := K (1 + |x |p0+1).
Introduction Numerical scheme and convergence results Numerical Examples Generalization to a diffusion process
Key results : regularity for fractional smoothness
Theorem (C. Geiss, S. Geiss, E. Gobet (2012))If g is α-Hölder and f Lipschitz, it holds for 0 ≤ t < s < T and x ∈ R,
‖Ys − Yt‖Lp(Pt,x ) ≤ c4ψ(x)(∫ s
t(T − r )α−1dr
) 12
,
‖Zs − Zt‖Lp(Pt,x ) ≤ c5ψ(x)(∫ s
t(T − r )α−2dr
) 12
.
Introduction Numerical scheme and convergence results Numerical Examples Generalization to a diffusion process
Properties of the associated PDEut (t , x) + σ2(t,x)
2 uxx (t , x) + b(t , x)ux (t , x) + f (t , x ,u(t , x), (σux )(t , x)) = 0,
t ∈ [0,T ), x ∈ R,u(T , x) = g(x), x ∈ R
Properties of u and ux (Zhang (2005)) uxx (C. Geiss, C.L, A.Luoto)
1. Yt = u(t ,Xt ) and Z t,xs = ux (s,X t,x
s )σ(s,X t,xs )
whereu(t , x) = Et,x
(g(XT ) +
∫ Tt f (r ,Xr ,Yr ,Zr )dr
),
2. u is continuous on [0,T ]× R, ux and uxx are continuous on[0,T )× R,
3. |u(t , x)| ≤ c1ψ(x), |ux (t , x)| ≤ c2ψ(x)
(T−t)1−α
2, |uxx (t , x)| ≤ c3ψ(x)
(T−t)1−α2,
∂ ixu(t , x) = Et,x
[g(XT )N t,i
T +∫ T
t f (r ,Xr ,Yr ,Zr )Nt,ir dr
], where N t,i
r denotes
the Malliavin weight of the i th order
Introduction Numerical scheme and convergence results Numerical Examples Generalization to a diffusion process
Conclusion
By using the Skorohod embedding to approximate the Brownian motion,we manage to prove,
• if f is Lipschitz and g is locally α-Hölder and YT = g(BT ) that• the rate of convergence = α
4 for the L2-error on Y and < α4 for the
L2-error on Z ,• if YT = g(XT ), X a diffusion process with nice enough b and σ, and
• g to be locally C2,α
• f has w.r.t. x , y , z Lipschitz continuous second partial derivatives,then
• the rate of convergence = 14 for the L2-error on Y and = 1
4 ∧ α2 for the
L2-error on Z