# Regularity of stopping times of diffusion processes in Besov spaces ```STUDIA MATHEMATICA 151 (1) (2002)
Regularity of stopping times of
diffusion processes in Besov spaces
by
Xicheng Zhang (Wuhan and Lisboa)
Abstract. We prove that the exit times of diffusion processes from a bounded open
α
set Ω almost surely belong to the Besov space Bp,q
(Ω) provided that pα &lt; 1 and 1 ≤ q &lt; ∞.
1. Introduction and statement of results. Recently, many authors
devoted their efforts to the study of stopping times. In , Airault, Malliavin
and Ren studied the smoothness of stopping times of diffusion processes
in Wiener space. In , Pedersen and Peskir computed the expectation of
the Azéma–Yor stopping times. In , Knight and Maisonneuve gave two
characterizations of stopping times via martingales and Markov processes.
On the other hand, in , , Boufoussi and Roynette studied the regularity
of Brownian local time Lxt as a function of x ∈ R, and they proved that
it is almost everywhere in Besov–Orlicz spaces on R. Motivated by their
work, we study the smoothness of stopping times regarded as a function of
starting points in Besov spaces. We emphasize that in  the authors proved
that for an elliptic diffusion process, the exit time from an open set is in the
fractional Sobolev spaces Dαp provided that pα &lt; 1. In the case of Brownian
motion, they also showed that the result is almost optimal. Here we borrow
some methods from  to prove our main result.
α
For any 0 &lt; α &lt; 1, p &gt; 1 and q ≥ 1, we use Bp,q
to denote the usual
d
α
Besov spaces in R , and the norm in Bp,q is denoted by k &middot; kα,p,q . We refer to
[10, p. 189] for the detailed definition. The Besov spaces over an arbitrary
domain Ω are defined as restriction of the corresponding spaces over Rd
to Ω. That is to say,
(1)
α (Ω) :=
kf kBp,q
inf
α (Rd )
g|Ω =f, g∈Bp,q
kgkα,p,q .
When Ω is a bounded C 2 domain, another norm equivalent to (1) is given
2000 Mathematics Subject Classification: 60H07, 60G05, 46E35.
Key words and phrases: Besov spaces, stopping times.

24
X. C. Zhang
by (cf. [10, p. 324])
(2)
kf k∗Bp,q
α (Ω)
= kf kLp (Ω) +
kf (x + h) − f (x)kqLp (Ωh )
Rd
|h|d+αq
dh
1/q
,
where Ωh = Ω ∩ {x ∈ Rd : x + h ∈ Ω} and | &middot; | is the usual norm in Rd .
Let (W, H, &micro;) be the classical Wiener space. W and H respectively stand
for the completions of C0∞ ([0, ∞), Rd ) with respect to the norms
∞
1/2
|w(t)|
kwkW = sup
and kwkH =
|w0 (t)|2 dt
,
t≥0 1 + t
0
&micro; is the Wiener measure.
In this context, we consider the following diffusion process:

d
X


 dXi (t, x) =
σk,i (X(t, x))dwk (t) + bi (X(t, x))dt, i = 1, . . . , d,
(3)
k=1



X(0, x) = x
where x ∈ Rd , σ : Rd → Rd &times; Rd and b : Rd → Rd are Cb2 functions, and
w(&middot;) is the standard d-dimensional Wiener process. The second order elliptic
differential operator A on Rd associated with this diffusion process is given
by
d
d
X
1 X
2
bi (x)∂i ,
ai,j (x)∂i,j +
A=
2 i,j=1
i=1
where a = σσ T ∈ Cb2 .
Let Ω be a bounded connected open set in Rd with C 2 boundary (or ∂Ω
is a regularly imbedded C 2 submanifold of Rd ). It is well known that there
is a function (cf. [5, p. 59]) % : Rd → R satisfying:
(i) % is C 2 ;
(ii) {x ∈ Rd : %(x) &lt; 0} = Ω;
(iii) ∇%(x) 6= 0 on ∂Ω = {x ∈ Rd : %(x) = 0}, where ∇ stands for
% is called a defining function for Ω. From the definition of %, it is not
hard to find that there exist two strictly positive constants C1 and ε such
that
(4)
C1 ≤ |∇%(x)|
∀x ∈ Ω ε ,
where Ωε := {x : −ε &lt; %(x) &lt; ε} is a bounded set and Ω ε is the closure
of Ωε .
For x ∈ Ω, we define the exit time as follows:
(5)
τx (w) := inf{t ≥ 0 : X(t, w, x) 6∈ Ω} = inf{t ≥ 0 : %(X(t, w, x)) = 0}.
Regularity of stopping times
25
For fixed T &gt; 0, setting
τxT (w) = min{τx (w), T },
we prove the following result:
Theorem 1. In addition to the conditions σ, b ∈ Cb2 , assume also that
there exist two positive constants C2 , C3 such that
C2 |ξ|2 ≤ ξaξ T ≤ C3 |ξ|2
(6)
for all ξ ∈ Rd . Then for any p ≥ 1 and 0 &lt; α &lt; 1, there exists a constant
C = C(T, p, α) such that
E|τxT (w) − τyT (w)|p ≤ C|x − y|α
(7)
∀x, y ∈ Ω.
Furthermore, in the case of one-dimensional Brownian motion, for x &lt; 1,
set τx (w) = inf{t ≥ 0 : wt + x = 1} and τxT (w) = τx (w) ∧ T ; then for any
a &lt; 1, there exists a constant C = C(T, a, p) such that
E|τxT (w) − τyT (w)|p ≥ C|x − y|
(8)
∀a &lt; x, y &lt; 1.
From this theorem we easily deduce the following results:
Corollary 2. For almost all w ∈ W , if pα &lt; 1 and 1 ≤ q &lt; ∞, then
α
∈ Bp,q
(Ω).
τxT (w)
Set f (x) = E(τxT ). Since |f (x) − f (y)| ≤ E|τxT (w) − τyT (w)| ≤ C|x − y|α ,
we obtain
α
Corollary 3. For 0 &lt; α &lt; 1 and 1 &lt; p &lt; ∞, we have f ∈ Bp,q
(Ω).
2. Proof of Theorem 1. Henceforth, we make a convention: C denotes
a positive constant whose value may change in different occurrences.
First of all, from equation (3), we observe that for m &gt; 1,
E|X(s, x) − X(t, y)|m ≤ C(|x − y|m + |s − t|m/2 )
for all s, t ∈ [0, T ], x, y ∈ Rd (cf. ).
By the Kolmogorov criterion (cf. ), if we take m &gt; (d + 3)/(1 − α), then
(9)
max |X(s, w, x)) − X(s, w, y)| ≤ B(w)|x − y|α+1/m ,
0≤s≤T
where E|B(w)|m &lt; ∞.
By condition (i), we know that
(10)
|∇%(x)| &lt; C4
For z ∈ ∂Ω, if we define
∀x ∈ Ωε ∪ Ω.
ηzε (w) := inf{t : X(t, w, z) 6∈ Ωε } = inf{t : |%(X(t, w, z))| = ε},
ε
T
then for s &lt; [τxT (w) + ηX(τ
T (w),w,x) (w)] ∧ τy (w), we have
x
X(s, w, x) ∈ Ωε ∪ Ω,
X(s, w, y) ∈ Ω.
26
X. C. Zhang
Thus by the mean value theorem and (9), (10), we have
(11) |%(X(s, w, x)) − %(X(s, w, y))| ≤ |∇%(η(w))| &middot; |X(s, w, x) − X(s, w, y)|
≤ C4 B(w)|x − y|α+1/m .
where η(w) ∈ Ωε ∪ Ω.
Now we estimate the &micro;{|τxT (w) − τyT (w)| &gt; λ} for λ &lt; T . Note that if
τyT (w) &gt; τxT (w) for some w ∈ W , then
τyT (w) = τxT (w) + inf{s : %(X(τxT (w) + s, w, y)) = 0}.
Hence
{τyT (w) − τxT (w) &gt; λ} ⊂ { max %(X(τxT (w) + s, w, y)) &lt; 0, τxT (w) &lt; T − λ}.
0≤s≤λ
ε
(w) &lt; τxT (w)+s,
On the other hand, setting u(s) = τxT (w)+s∧ηX(τ
T
x (w),w,x)
from (11), we find that
max |%(X(u(s), w, x)) − %(X(u(s), w, y))| ≤ C4 B(w)|x − y|α+1/m
0≤s≤λ
a.s.
Consequently, we have
&micro;{τyT (w) − τxT (w) &gt; λ}
≤ &micro;{ max %(X(τxT (w) + s, w, y)) &lt; 0, τxT (w) &lt; T − λ}
0≤s≤λ
= &micro;{ max %(X(τxT (w) + s, w, y)) &lt; 0, τxT (w) &lt; T − λ,
0≤s≤λ
max %(X(u(s), w, x)) − max %(X(u(s), w, y)) ≤ B(w)|x − y|α+1/m }
0≤s≤λ
0≤s≤λ
≤ &micro;{ max %(X(u(s), w, x)) &lt; B(w)|x − y|α+1/m , τxT (w) &lt; T − λ}
0≤s≤λ
≤ &micro;{ max %(X(u(s), w, x)) &lt; B(w)|x − y|α+1/m , τxT (w) &lt; T − λ,
0≤s≤λ
B(w) ≤ |x − y|−1/m } + &micro;{B(w) ≥ |x − y|−1/m }
≤ &micro;{ max %(X(u(s), w, x)) &lt; |x − y|α , τxT (w) &lt; T − λ}
0≤s≤λ
+ &micro;{B(w) ≥ |x − y|−1/m }
ε
≤ &micro;{ max %(X(τx (w) + s ∧ ηX(τ
(w), w, x)) &lt; |x − y|α }
x (w),w,x)
0≤s≤λ
+ &micro;{B(w) ≥ |x − y|−1/m }
:= I1 + I2 .
The Chebyshev inequality yields
I2 ≤ E|B(w)|m &middot; |x − y| ≤ C|x − y|.
Regularity of stopping times
27
By the strong Markov property, we continue to get
ε
I1 = &micro;{ max %(X(s ∧ ηX(τ
(w), θτx (w) (w), X(τx (w), w, x)))
x (w),w,x)
0≤s≤λ
=
∂Ω
&micro;{ max %(X(s ∧ ηzε (w), θτx (w) (w), z)) &lt; |x − y|α }
&lt; |x − y|α }
0≤s≤λ
&times; &micro;{X(τx (w), w, x) ∈ dz}
where θτx (w) (w)(s) = w(s + τx (w)) − w(τx (w)).
On the other hand, for fixed z ∈ ∂Ω, by the Ito formula ( or ), there
exists an abstract Brownian motion b(s) = bz (s) such that
t
t
%(X(t, z)) = [(∇%)T a∇%]1/2 (X(s, z)) db(s) + A%(X(s, z)) ds.
0
0
Set
t
t
ξ(t, z) = [(∇%)T a∇%]1/2 (X(s ∧ ηzε , z)) db(s) + A%(X(s ∧ ηzε , z)) ds.
0
0
Then for t &lt; ηzε , we have
%(X(t, w, z)) = ξ(t, w, z).
By the Girsanov theorem, ξ(t, z) is a martingale under the new probability
db
&micro; = M d&micro; where
T
T
1
ε
ε
2
M = exp − (A%)(X(s ∧ ηz , z)) db(s) −
(A%(X(s ∧ ηz , z))) ds .
2
0
0
The increasing process of the martingale ξ(t, z) is
t
β(t, z) = [(∇%)T a∇%](X(s ∧ ηzε , z)) ds.
0
From (4), (6) and (10), it is easy to see that
C5 t ≤ β(t, z) ≤ C6 t.
By a change of clock, we deduce that γt (w) := ξ(βt−1 , z) is a Brownian
motion under &micro;
b, starting at zero. Hence
h
2
e−x /(2t)
dx ≤ Cht−1/2 .
&micro;
b{ max γs &lt; h} = 2
1/2
0≤s≤t
(2πt)
0
28
X. C. Zhang
So we have
&micro;
b{ max %(X(s ∧ ηzε (w), w, z)) &lt; |x − y|α } = &micro;
b{ max ξ(s, z) &lt; |x − y|α }
0≤s≤λ
0≤s≤λ
≤&micro;
b{ max γs &lt; |x − y|α } ≤ C|x − y|α λ−1/2 .
0≤s≤C5 λ
Thus, by the Hölder inequality, for any q &gt; 1, we have
&micro;{ max %(X(s ∧ ηzε (w), w, z)) &lt; |x − y|α }
0≤s≤λ
Since M ∈
T
≤C
p
(q−1)/q
M q/(q−1) db
&micro;
|x − y|α/q λ−1/(2q) .
Lp , we see that for any 0 &lt; λ &lt; T ,
&micro;{ max %(X(s ∧ ηzε (w), w, z)) &lt; |x − y|α } ≤ C|x − y|α/q λ−1/(2q) .
0≤s≤λ
Lastly, I1 ≤ C|x − y|α/q λ−1/(2q) . So
E|τxT (w) − τyT (w)|p = pλp−1 &micro;{|τxT (w) − τyT (w)| &gt; λ} dλ
R
T
≤ C|x − y|
α/q
0
pλp−1 λ−1/(2q) dλ ≤ C|x − y|α/q .
In view of the arbitrariness of q &gt; 1, we let q tend to 1, and (7) follows.
Next we look at the second part of the theorem. Assume that x &lt; y and
T /3 &lt; λ &lt; T /2; we obviously have
{τxT (w) − τyT (w) &gt; λ} = {τx (w) − τy (w) &gt; λ} ∩ {τy (w) &lt; T − λ}.
Thanks to the independence of τx (w) − τy (w) and τy (w) (cf. [11, p. 165,
Problem 8.22]), we have
&micro;{τxT (w) − τyT (w) &gt; λ}
= &micro;{τx (w) − τy (w) &gt; λ}&micro;{τy (w) &lt; T − λ}
= &micro;{ max w(s + τy ) − w(τy ) &lt; y − x}&micro;{τy (w) &lt; T − λ}
0≤s≤λ
= &micro;{ max w(s) &lt; y − x}&micro;{τy (w) &lt; T − λ}
0≤s≤λ
2
=√
2πλ
y−x
e
0
≥ C(y − x).
−r 2 /(2λ)
2
∞
dr &middot; p
e−r
2π(T − λ) 1−y
Consequently,
E|τxT (w) − τyT (w)|p ≥ C|x − y|.
2
/(2(T −λ))
dr
Regularity of stopping times
29
Acknowledgements. I would like to express my hearty thanks to Professor Jiagang Ren for his valuable suggestions. I would also like to express
my deep thanks to the referee for his/her useful advice.
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Department of Mathematics
Huazhong University of Science and Technology
Wuhan, Hubei 430074, P.R. China
E-mail: xczhang@hust.edu.cn
Grupo de Fisica-Matematica