Hitting probabilities for systems of non-linear
stochastic heat equations with multiplicative noise
Robert C. Dalang1,4, Davar Khoshnevisan2,5, and Eulalia Nualart3
Abstract
We consider a system of d non-linear stochastic heat equations in spatial dimension
1 driven by d-dimensional space-time white noise. The non-linearities appear both
as additive drift terms and as multipliers of the noise. Using techniques of Malliavin
calculus, we establish upper and lower bounds on the one-point density of the solution
u(t, x), and upper bounds of Gaussian-type on the two-point density of (u(s, y), u(t, x)).
In particular, this estimate quantifies how this density degenerates as (s, y) → (t, x).
From these results, we deduce upper and lower bounds on hitting probabilities of the
process {u(t , x)}t∈R+,x∈[0,1], in terms of respectively Hausdorff measure and Newtonian
capacity. These estimates make it possible to show that points are polar when d ≥ 7
and are not polar when d ≤ 5. We also show that the Hausdorff dimension of the range
of the process is 6 when d > 6, and give analogous results for the processes t 7→ u(t, x)
and x 7→ u(t, x). Finally, we obtain the values of the Hausdorff dimensions of the level
sets of these processes.
AMS 2000 subject classifications: Primary: 60H15, 60J45; Secondary: 60H07, 60G60.
Key words and phrases. Hitting probabilities, stochastic heat equation, space-time white
noise, Malliavin calculus.
Institut de Mathématiques, Ecole Polytechnique Fédérale de Lausanne, Station 8, CH-1015 Lausanne,
Switzerland. robert.dalang@epfl.ch
Department of Mathematics, The University of Utah, 155 S. 1400 E. Salt Lake City, UT 84112-0090,
USA. davar@math.utah.edu
Institut Galilée, Université Paris 13, 93430 Villetaneuse, France. nualart@math.univ-paris13.fr
Supported in part by the Swiss National Foundation for Scientific Research.
Research supported in part by a grant from the US National Science Foundation.
http://arxiv.org/abs/0704.1312v1
1 Introduction and main results
Consider the following system of non-linear stochastic partial differential equations (spde’s)
(t , x) =
(t , x) +
σi,j(u(t , x))Ẇ
j(t , x) + bi(u(t , x)), (1.1)
for 1 ≤ i ≤ d, t ∈ [0 , T ], and x ∈ [0 , 1], where u := (u1 , . . . , ud), with initial conditions
u(0 , x) = 0 for all x ∈ [0 , 1], and Neumann boundary conditions
(t , 0) =
(t , 1) = 0, 0 ≤ t ≤ T. (1.2)
Here, Ẇ := (Ẇ 1 , . . . , Ẇ d) is a vector of d independent space-time white noises on [0 , T ]×
[0 , 1]. For all 1 ≤ i, j ≤ d, bi, σij : Rd → R are globally Lipschitz functions. We set b = (bi),
σ = (σij). Equation (1.1) is formal: the rigorous formulation of Walsh [W86] will be
recalled in Section 2.
The objective of this paper is to develop a potential theory for the Rd-valued process
u = (u(t, x), t ≥ 0, x ∈ (0, 1)). In particular, given A ⊂ Rd, we want to determine whether
the process u visits (or hits) A with positive probability.
The only potential-theoretic result that we are aware of for systems of non-linear spde’s
with multiplicative noise (σ non-constant) is Dalang and Nualart [DN04], who study
the case of the reduced hyperbolic spde on R2+ (essentially equivalent to the wave equation
in spatial dimension 1):
∂2Xit
∂t1∂t2
σi,j(Xt)
∂t1∂t2
+ bi(Xt),
where t = (t1 , t2) ∈ R2+, and Xit = 0 if t1t2 = 0, for all 1 ≤ i ≤ d. There, Dalang and
Nualart used Malliavin calculus to show that the solution (Xt) of this spde satisfies
K−1Capd−4(A) ≤ P{∃t ∈ [a, b]2 : Xt ∈ A} ≤ KCapd−4(A),
where Capβ denotes the capacity with respect to the Newtonian β-kernel Kβ(·) (see (1.6)).
This result, particularly the upper bound, relies heavily on properties of the underlying
two-parameter filtration and uses Cairoli’s maximal inequality for two-parameter processes.
Hitting probabilities for systems of linear heat equations have been obtained inMueller
and Tribe [MT03]. For systems of non-linear stochastic heat equations with additive noise,
that is, σ in (1.1) is a constant matrix, so (1.1) becomes
(t , x) =
(t , x) +
σi,j Ẇ
j(t , x) + bi(u(t , x)), (1.3)
estimates on hitting probabilities have been obtained in Dalang, Khoshnevisan and
Nualart [DKN07]. That paper develops some general results that lead to upper and lower
bounds on hitting probabilities for continuous two-parameter random fields, and then uses
these, together with a careful analysis of the linear equation (b ≡ 0, σ ≡ Id, where Id
denotes the d × d identity matrix) and Girsanov’s theorem, to deduce bounds on hitting
probabilities for the solution to (1.3).
In this paper, we make use of the general results of [DKN07], but then, in order to handle
the solution of (1.1), we use a very different approach. Indeed, the results of [DKN07] require
in particular information about the probability density function pt,x of the random vector
u(t, x). In the case of multiplicative noise, estimates on pt,x can be obtained via Malliavin
calculus.
We refer in particular on the results of Bally and Pardoux [BP98], who used Malli-
avin calculus in the case d = 1 to prove that for any t > 0, k ∈ N and 0 ≤ x1 < · · · < xk ≤ 1,
the law of (u(t , x1), . . . , u(t , xk)) is absolutely continuous with respect to Lebesgue measure,
with a smooth and strictly positive density on {σ 6= 0}k, provided σ and b are infinitely
differentiable functions which are bounded together with their derivatives of all orders. A
Gaussian-type lower bound for this density is established by Kohatsu-Higa [K03] under
a uniform ellipticity condition. Morien [M98] showed that the density function is also
Hölder-continuous as a function of (t , x).
In this paper, we shall use techniques of Malliavin calculus to establish the following
theorem. Let pt,x(z) denote the probability density function of the R
d-valued random vector
u(t , x) = (u1(t , x), . . . , ud(t , x)) and for (s, y) 6= (t, x), let ps,y; t,x(z1, z2) denote the joint
density of the R2d-valued random vector
(u(s , y), u(t , x)) = (u1(s , y), . . . , ud(s , y), u1(t , x), . . . , ud(t , x)) (1.4)
(the existence of pt,x(·) is essentially a consequence of the result of Bally and Pardoux
[BP98], see our Corollary 4.3; the existence of ps,y; t,x(·, ·) is a consequence of Theorems 3.1
and 6.3).
Consider the following two hypotheses on the coefficients of the system (1.1):
P1 The functions σij and bi are bounded and infinitely differentiable with bounded partial
derivatives of all orders, for 1 ≤ i, j ≤ d.
P2 The matrix σ is uniformly elliptic, that is, ‖σ(x)ξ‖2 ≥ ρ2 > 0 for some ρ > 0, for all
x ∈ Rd, ξ ∈ Rd, ‖ξ‖ = 1 (‖ · ‖ denotes the Euclidean norm on Rd).
Theorem 1.1. Assume P1 and P2. Fix T > 0 and let I ⊂ (0, T ] and J ⊂ (0, 1) be two
compact nonrandom intervals.
(a) The density pt,x(z) is uniformly bounded over z ∈ Rd, t ∈ I and x ∈ J .
(b) There exists c > 0 such that for any t ∈ I, x ∈ J and z ∈ Rd,
pt,x(z) ≥ ct−d/4 exp
ct1/2
(c) For all η > 0, there exists c > 0 such that for any s, t ∈ I, x, y ∈ J , (s, y) 6= (t, x) and
z1, z2 ∈ Rd,
ps,y; t,x(z1, z2) ≤ c(|t− s|1/2 + |x− y|)−(d+η)/2 exp
− ‖z1 − z2‖
c(|t− s|1/2 + |x− y|)
. (1.5)
(d) There exists c > 0 such that for any t ∈ I, x, y ∈ J , x 6= y and z1, z2 ∈ Rd,
pt,y; t,x(z1, z2) ≤ c(|x− y|)−d/2 exp
−‖z1 − z2‖
c|x− y|
The main technical effort in this paper is to obtain the upper bound in (c). Indeed,
it is not difficult to check that for fixed (s, y; t, x), (z1, z2) 7→ ps,y; t,x(z1, z2) behaves like
a Gaussian density function. However, for (s, y) = (t, x), the R2d-valued random vector
(u(s , y), u(t , x)) is concentrated on a d-dimensional subspace in R2d and therefore does not
have a density with respect to Lebesgue measure in R2d. So the main effort is to estimate
how this density blows up as (s, y) → (t, x). This is achieved by a detailed analysis of
the behavior of the Malliavin matrix of (u(s , y), u(t , x)) as a function of (s, y; t, x), using a
perturbation argument. The presence of η in statement (c) may be due to the method of
proof. When t = s, it is possible to set η = 0 as in Theorem 1.1(d).
This paper is organized as follows. After introducing some notation and stating our main
results on hitting probabilities (Theorems 1.2 and 1.6), we assume Theorem 1.1 and use the
theorems of [DKN07] to prove these results in Section 2. In Section 3, we recall some basic
facts of Malliavin calculus and state and prove two results that are tailored to our needs
(Propositions 3.4 and 3.5). In Section 4, we establish the existence, smoothness and uniform
boundedness of the one-point density function pt,x, proving Theorem 1.1(a). In Section 5,
we establish a lower bound on pt,x, which proves Theorem 1.1(b). This upper (respectively
lower) bound is a fairly direct extension to d ≥ 1 of a result of Bally and Pardoux [BP98]
(respectively Kohatsu-Higa [K03]) when d = 1. In Section 6, we establish Theorem 1.1(c)
and (d). The main steps are as follows.
The upper bound on the two-point density function ps,y; t,x involves a bloc-decomposition
of the Malliavin matrix of the R2d-valued random vector (u(s, y), u(s, y) − u(t, x)). The
entries of this matrix are of different orders of magnitude, depending on which bloc they
are in: see Theorem 6.3. Assuming Theorem 6.3, we prove Theorem 1.1(c) and (d) in
Section 6.3. The exponential factor in (1.5) is obtained from an exponential martingale
inequality, while the factor (|t − s|1/2 + |x − y|)−(d+η)/2 comes from an estimate of the
iterated Skorohod integrals that appear in Corollary 3.3 and from the block structure of
the Malliavin matrix.
The proof of Theorem 6.3 is presented in Section 6.4: this is the main technical effort in
this paper. We need bounds on the inverse of the Malliavin matrix. Bounds on its cofactors
are given in Proposition 6.5, while bounds on negative moments of its determinant are given
in Proposition 6.6. The determinant is equal to the product of the 2d eigenvalues of the
Malliavin matrix. It turns out that at least d of these eigenvalues are of order 1 (“large
eigenvalues”) and do not contribute to the upper bound in (1.5), and at most d are of the
same order as the smallest eigenvalue (“small eigenvalues”), that is, of order |t−s|1/2+|x−y|.
If we did not distinguish between these two types of eigenvalues, but estimated all of them
by the smallest eigenvalue, we would obtain a factor of (|t− s|1/2 + |x− y|)−d+η/2 in (1.5),
which would not be the correct order. The estimates on the smallest eigenvalue are obtained
by refining a technique that appears in [BP98]; indeed, we obtain a precise estimate on the
density whereas they only showed existence. The study of the large eigenvalues does not
seem to appear elsewhere in the literature.
Coming back to potential theory, let us introduce some notation. For all Borel sets
F ⊂ Rd, we define P(F ) to be the set of all probability measures with compact support
contained in F . For all integers k ≥ 1 and µ ∈ P(Rk), we let Iβ(µ) denote the β-dimensional
energy of µ, that is,
Iβ(µ) :=
Kβ(‖x− y‖)µ(dx)µ(dy),
where ‖x‖ denotes the Euclidian norm of x ∈ Rk,
Kβ(r) :=
r−β if β > 0,
log(N0/r) if β = 0,
1 if β < 0,
(1.6)
and N0 is a sufficiently large constant (see Dalang, Khoshnevisan, and Nualart
[DKN07, (1.5)].
For all β ∈ R, integers k ≥ 1, and Borel sets F ⊂ Rk, Capβ(F ) denotes the β-dimensional
capacity of F , that is,
Capβ(F ) :=
µ∈P(F )
Iβ(µ)
where 1/∞ := 0. Note that if β < 0, then Capβ(·) ≡ 1.
Given β ≥ 0, the β-dimensional Hausdorff measure of F is defined by
Hβ(F ) = lim
(2ri)
β : F ⊆
B(xi , ri), sup
ri ≤ ǫ
, (1.7)
where B(x , r) denotes the open (Euclidean) ball of radius r > 0 centered at x ∈ Rd. When
β < 0, we define Hβ(F ) to be infinite.
Throughout, we consider the following parabolic metric: For all s, t ∈ [0 , T ] and x, y ∈
[0 , 1],
∆((t , x) ; (s , y)) := |t− s|1/2 + |x− y|. (1.8)
Clearly, this is a metric on R2 which generates the usual Euclidean topology on R2. Then
we obtain an energy form
I∆β (µ) :=
Kβ(∆((t , x) ; (s , y)))µ(dt dx)µ(ds dy),
and a corresponding capacity
Cap∆β (F ) :=
µ∈P(F )
I∆β (µ)
For the Hausdorff measure, we write
β (F ) = lim
(2ri)
β : F ⊆
B∆((ti , xi) , ri), sup
ri ≤ ǫ
where B∆((t , x) , r) denotes the open ∆-ball of radius r > 0 centered at (t , x) ∈ [0 , T ] ×
[0 , 1].
Using Theorem 1.1 together with results fromDalang, Khoshnevisan, and Nualart
[DKN07], we shall prove the following result. Let u(E) denote the (random) range of E
under the map (t, x) 7→ u(t, x), where E is some Borel-measurable subset of R2.
Theorem 1.2. Assume P1 and P2. Fix T > 0, M > 0, and η > 0. Let I ⊂ (0, T ] and
J ⊂ (0, 1) be two fixed non-trivial compact intervals.
(a) There exists c > 0 depending on M, I, J and η such that for all compact sets A ⊆
[−M,M ]d,
c−1 Capd−6+η(A) ≤ P{u(I × J) ∩A 6= ∅} ≤ cHd−6−η(A).
(b) For all t ∈ (0, T ], there exists c1 > 0 depending on T , M and J , and c2 > 0 depending
on T , M , J and η > 0 such that for all compact sets A ⊆ [−M,M ]d,
c1 Capd−2(A) ≤ P{u({t} × J) ∩A 6= ∅} ≤ c2 Hd−2−η(A).
(c) For all x ∈ (0, 1), there exists c > 0 depending on M, I and η such that for all compact
sets A ⊆ [−M,M ]d,
c−1 Capd−4+η(A) ≤ P{u(I × {x}) ∩A 6= ∅} ≤ cHd−4−η(A).
Remark 1.3. (i) Because of the inequalities between capacity and Hausdorff measure,
the right-hand sides of Theorem 1.2 can be replaced by cCapd−6−η(A), cCapd−2−η(A)
and cCapd−4−η(A) in (a), (b) and (c), respectively (cf. Kahane [K85, p. 133]).
(ii) Theorem 1.2 also holds if we consider Dirichlet boundary conditions (i.e. ui(t, 0) =
ui(t, 1) = 0, for t ∈ [0, T ]) instead of Neumann boundary conditions.
(iii) In the upper bounds of Theorem 1.2, the condition in P1 that σ and b are bounded
can be removed, but their derivatives of all orders must exist and be bounded.
As a consequence of Theorem 1.2, we deduce the following result on the polarity of
points. Recall that a Borel set A ⊆ Rd is called polar for u if P{u((0, T ] × (0, 1)) ∩ A 6=
∅} = 0; otherwise, A is called nonpolar.
Corollary 1.4. Assume P1 and P2.
(a) Singletons are nonpolar for (t, x) 7→ u(t, x) when d ≤ 5, and are polar when d ≥ 7
(the case d = 6 is open).
(b) Fix t ∈ (0, T ]. Singletons are nonpolar for x 7→ u(t, x) when d = 1, and are polar
when d ≥ 3 (the case d = 2 is open).
(c) Fix x ∈ (0, 1). Singletons are not polar for t 7→ u(t, x) when d ≤ 3 and are polar when
d ≥ 5 (the case d = 4 is open).
Another consequence of Theorem 1.2 is the Hausdorff dimension of the range of the
process u.
Corollary 1.5. Assume P1 and P2.
(a) If d > 6, then dim
(u((0, T ] × (0, 1))) = 6 a.s.
(b) Fix t ∈ R+. If d > 2, then dimH(u({t} × (0, 1))) = 2 a.s.
(c) Fix x ∈ (0, 1). If d > 4, then dim
(u(R+ × {x})) = 4 a.s.
As in Dalang, Khoshnevisan, and Nualart [DKN07], it is also possible to use
Theorem 1.1 to obtain results concerning level sets of u. Define
L (z ;u) := {(t , x) ∈ I × J : u(t , x) = z} ,
T (z ;u) = {t ∈ I : u(t , x) = z for some x ∈ J} ,
X (z ;u) = {x ∈ J : u(t , x) = z for some t ∈ I} ,
Lx(z ;u) := {t ∈ I : u(t , x) = z} ,
t(z ;u) := {x ∈ J : u(t , x) = z} .
We note that L (z ;u) is the level set of u at level z, T (z ;u) (resp. X (z ;u)) is the projection
of L (z ;u) onto I (resp. J), and Lx(z ;u) (resp. L
t(z ;u)) is the x-section (resp. t-section)
of L (z ;u).
Theorem 1.6. Assume P1 and P2. Then for all η > 0 and R > 0 there exists a positive
and finite constant c such that the following holds for all compact sets E ⊂ (0, T ] × (0, 1),
F ⊂ (0, T ], G ⊂ (0, 1), and for all z ∈ B(0 , R):
(a) c−1 Cap∆(d+η)/2(E) ≤ P{L (z ;u) ∩E 6= ∅} ≤ cH ∆(d−η)/2(E);
(b) c−1Cap(d−2+η)/4(F ) ≤ P{T (z ;u) ∩ F 6= ∅} ≤ cH(d−2−η)/4(F );
(c) c−1 Cap(d−4+η)/2(G) ≤ P{X (z ;u) ∩G 6= ∅} ≤ cH(d−4−η)/2(G);
(d) for all x ∈ (0, 1), c−1 Cap(d+η)/4(F ) ≤ P{Lx(z ;u) ∩ F 6= ∅} ≤ cH(d−η)/4(F );
(e) for all t ∈ (0, T ], c−1 Capd/2(G) ≤ P{L t(z ;u) ∩G 6= ∅} ≤ cH(d−η)/2(G).
Corollary 1.7. Assume P1 and P2. Choose and fix z ∈ Rd.
(a) If 2 < d < 6, then dim
T (z ;u) = 1
(6− d) a.s. on {T (z ;u) 6= ∅}.
(b) If 4 < d < 6 (i.e. d = 5), then dim
X (z ;u) = 1
(6− d) a.s. on {X (z ;u) 6= ∅}.
(c) If 1 ≤ d < 4, then dim
Lx(z ;u) =
(4− d) a.s. on {Lx(z ;u) 6= ∅}.
(d) If d = 1, then dim
t(z ;u) = 1
(2− d) = 1
a.s. on {L t(z ;u) 6= ∅}.
In addition, all four right-most events have positive probability.
Remark 1.8. The results of the two theorems and corollaries above should be compared
with those of Dalang, Khsohnevisan and Nualart [DKN07].
2 Proof of Theorems 1.2, 1.6 and their corollaries (assuming
Theorem 1.1)
We first recall that equation (1.1) is formal: a rigorous formulation, following Walsh [W86],
is as follows. Let W i = (W i(s, x))s∈R+, x∈[0,1], i = 1, ..., d, be independent Brownian sheets
defined on a probability space (Ω,F ,P), and set W = (W 1, ...,W d). For t ≥ 0, let Ft =
σ{W (s, x), s ∈ [0, t], x ∈ [0, 1]}. We say that a process u = {u(t, x), t ∈ [0, T ], x ∈ [0, 1]}
is adapted to (Ft) if u(t, x) is Ft-measurable for each (t, x) ∈ [0, T ]× [0, 1]. We say that u
is a solution of (1.1) if u is adapted to (Ft) and if for i ∈ {1, . . . , d},
ui(t, x) =
Gt−r(x , v)
σi,j(u(r , v))W
j(dr , dv)
Gt−r(x , v) bi(u(r , v)) drdv,
(2.1)
where Gt(x , y) denotes the Green kernel for the heat equation with Neumann boundary
conditions (see Walsh [W86, Chap 3]), and the stochastic integral in (2.1) is interpreted
as in [W86].
Adapting the results from [W86] to the case d ≥ 1, one can show that there exists a
unique continuous process u = {u(t, x), t ∈ [0, T ], x ∈ [0, 1]} adapted to (Ft) that is a
solution of (1.1). Moreover, it is shown in Bally, Millet, and Sanz-Solé [BMS95] that
for any s, t ∈ [0, T ] with s ≤ t, x, y ∈ [0, 1], and p > 1,
E[|u(t, x)− u(s, y)|p] ≤ CT,p(∆((t , x) ; (s , y)))p/2 , (2.2)
where ∆ is the parabolic metric defined in (1.8). In particular, for any 0 < α < 1/2, u is
a.s. α-Hölder continuous in x and α/2-Hölder continuous in t.
Assuming Theorem 1.1, we now prove Theorems 1.2, 1.6 and their corollaries.
Proof of Theorem 1.2. (a) In order to prove the upper bound we use Dalang, Khosh-
nevisan, and Nualart [DKN07, Theorem 3.3]. Indeed, Theorem 1.1(a) and (2.2) imply
that the hypotheses (i) and (ii), respectively, of this theorem, are satisfied, and so the
conclusion (with β = d− η) is too.
In order to prove the lower bound, we shall use of [DKN07, Theorem 2.1]. This requires
checking hypotheses A1 and A2 in that paper. Hypothesis A1 is a lower bound on the
one-point density function pt,x(z), which is an immediate consquence of Theorem 1.1(b).
Hypothesis A2 is an upper bound on the two-point density function ps,y;t,x(z1, z2), which
involves a parameter β; we take β = d + η. In this case, Hypothesis A2 is an immediate
consequence of Theorem 1.1(c). Therefore, the lower bound in Theorem 1.2(a) follows from
[DKN07, Theorem 2.1]. This proves (a).
(b) For the upper bound, we again refer to [DKN07, Theorem 3.3] (see also [DKN07,
Theorem 3.1]). For the lower bound, which involves Capd−2(A) instead of Capd−2+η(A), we
refer to [DKN07, Remark 2.5] and observe that hypotheses A1t and A2t there are satisfied
with β = d (by Theorem 1.1(d)). This proves (b).
(c) As in (a), the upper bound follows from [DKN07, Theorem 3.3] with β = d − η
(see also [DKN07, Theorem 3.1(3)]), and the lower bound follows from [DKN07, Theorem
2.1(3)], with β = d+ η. Theorem 1.2 is proved.
Proof of Corollary 1.4. We first prove (a). Let z ∈ Rd. If d ≤ 5, then there is η > 0 such
that d − 6 + η < 0, and thus Capd−6+η({z}) = 1. Hence, the lower bound of Theorem
1.2 (a) implies that {z} is not polar. On the other hand, if d > 6, then for small η > 0,
d− 6− η > 0. Therefore, Hd−6−η({z}) = 0 and the upper bound of Theorem 1.2(a) implies
that {z} is polar. This proves (a). One proves (b) and (c) exactly along the same lines
using Theorem 1.2(b) and (c).
Proof of Theorem 1.6. For the upper bounds in (a)-(e), we use Dalang, Khoshnevisan,
and Nualart [DKN07, Theorem 3.3] whose assumptions we verified above with β = d−η;
these upper bounds then follow immediately from [DKN07, Theorem 3.2].
For the lower bounds in (a)-(d), we use [DKN07, Theorem 2.4] since we have shown
above that the assumptions of this theorem, with β = d+ η, are satisfied by Theorem 1.1.
For the lower bound in (e), we refer to [DKN07, Remark 2.5] and note that by Theorem
1.1(d), Hypothesis A2t there is satisfied with β = d. This proves Theorem 1.6.
Proof of Corollaries 1.5 and 1.7. The final positive-probability assertion in Corollary 1.7
is an immediate consequence of Theorem 1.6 and Taylor’s theorem Khoshnevisan [K02,
Corollary 2.3.1 p. 523].
Let E be a random set. When it exists, the codimension of E is the real number
β ∈ [0 , d] such that for all compact sets A ⊂ Rd,
P{E ∩A 6= ∅}
> 0 whenever dim
(A) > β,
= 0 whenever dim
(A) < β.
See Khoshnevisan [K02, Chap.11, Section 4]. When it is well defined, we write the said
codimension as codim(E). Theorems 1.2 and 1.6 imply that for d ≥ 1: codim(u(R+ ×
(0, 1))) = (d − 6)+; codim(u({t} × (0, 1))) = (d − 2)+; codim(u(R+ × {x})) = (d − 4)+;
codim(T (z)) = (d−2
)+; codim(X (z)) = (d−4
)+; codim(Lx(z)) =
; and codim(L t(z)) =
. According to Theorem 4.7.1 of Khoshnevisan [K02, Chapter 11], given a random set
E in Rn whose codimension is strictly between 0 and n,
E + codim E = n a.s. on {E 6= ∅}. (2.3)
This implies the statements of Corollaries 1.5 and 1.7.
3 Elements of Malliavin calculus
In this section, we introduce, following Nualart [N95] (see also Sanz-Solé [S05]), some
elements of Malliavin calculus. Let S denote the class of smooth random variables of the
F = f(W (h1), ...,W (hn)),
where n ≥ 1, f ∈ C∞P (Rn), the set of real-valued functions f such that f and all its partial
derivatives have at most polynomial growth, hi ∈ H := L2([0, T ] × [0, 1],Rd), and W (hi)
denotes the Wiener integral
W (hi) =
hi(t, x) ·W (dx, dt), 1 ≤ i ≤ n.
Given F ∈ S , its derivative is defined to be the Rd-valued stochastic process DF =
(Dt,xF = (D
t,xF, ...,D
t,xF ), (t, x) ∈ [0, T ]× [0, 1]) given by
Dt,xF =
(W (h1), ...,W (hn))hi(t, x).
More generally, we can define the derivative DkF of order k of F by setting
DkαF =
i1,...,ik=1
· · · ∂
f(W (h1), ...,W (hn))hi1(α1)⊗ · · · ⊗ hik(αk),
where α = (α1, ..., αk), and αi = (ti, xi), 1 ≤ i ≤ k.
For p, k ≥ 1, the space Dk,p is the closure of S with respect to the seminorm ‖ · ‖p
defined by
= E[|F |p] +
E[‖DjF‖p
where
‖DjF‖2
i1,...,ij=1
dx1 · · ·
(t1,x1)
· · ·D(ij)
(tj ,xj)
We set (D∞)d = ∩p≥1 ∩k≥1 Dk,p.
The derivative operator D on L2(Ω) has an adjoint, termed the Skorohod integral and
denoted by δ, which is an unbounded operator on L2(Ω,H ). Its domain, denoted by
Dom δ, is the set of elements u ∈ L2(Ω,H ) such that there exists a constant c such that
|E[〈DF, u〉H ]| ≤ c‖F‖0,2, for any F ∈ D1,2.
If u ∈ Dom δ, then δ(u) is the element of L2(Ω) characterized by the following duality
relation:
E[Fδ(u)] = E
t,xF uj(t, x) dtdx
, for all F ∈ D1,2.
A first application of Malliavin calculus to the study of probability laws is the following
global criterion for smoothness of densities.
Theorem 3.1. [N95, Thm.2.1.2 and Cor.2.1.2] or [S05, Thm.5.2] Let F = (F 1, ..., F d) be
an Rd-valued random vector satisfying the following two conditions:
(i) F ∈ (D∞)d;
(ii) the Malliavin matrix of F defined by γF = (〈DF i,DF j〉H )1≤i,j≤d is invertible a.s.
and (det γF )
−1 ∈ Lp(Ω) for all p ≥ 1.
Then the probability law of F has an infinitely differentiable density function.
A random vector F that satisfies conditions (i) and (ii) of Theorem 3.1 is said to be
nondegenerate. For a nondegenerate random vector, the following integration by parts
formula plays a key role.
Proposition 3.2. [N98, Prop.3.2.1] or [S05, Prop.5.4] Let F = (F 1, ..., F d) ∈ (D∞)d be a
nondegenerate random vector, let G ∈ D∞ and let g ∈ C∞P (Rd). Fix k ≥ 1. Then for any
multi-index α = (α1, ..., αk) ∈ {1, . . . , d}k, there is an element Hα(F,G) ∈ D∞ such that
E[(∂αg)(F )G] = E[g(F )Hα(F,G)].
In fact, the random variables Hα(F,G) are recursively given by
Hα(F,G) = H(αk)(F,H(α1,...,αk−1)(F,G)),
H(i)(F,G) =
δ(G (γ−1F )i,j DF
Proposition 3.2 with G = 1 and α = (1, ..., d) implies the following expression for the
density of a nondegenerate random vector.
Corollary 3.3. [N98, Corollary 3.2.1] Let F = (F 1, ..., F d) ∈ (D∞)d be a nondegenerate
random vector and let pF (z) denote the density of F . Then for every subset σ of the set of
indices {1, ..., d},
pF (z) = (−1)d−|σ|E[1{F i>zi,i∈σ, F i<zi,i 6∈σ}H(1,...,d)(F, 1)],
where |σ| is the cardinality of σ, and, in agreement with Proposition 3.2,
H(1,...,d)(F, 1) = δ((γ
F DF )
dδ((γ−1F DF )
d−1δ(· · · δ((γ−1F DF )
1) · · · ))).
The next result gives a criterion for uniform boundedness of the density of a nondegen-
erate random vector.
Proposition 3.4. For all p > 1 and ℓ ≥ 1, let c1 = c1(p) > 0 and c2 = c2(ℓ, p) ≥ 0 be fixed.
Let F ∈ (D∞)d be a nondegenerate random vector such that
(a) E[(det γF )
−p] ≤ c1;
(b) E[‖Dl(F i)‖p
] ≤ c2, i = 1, ..., d.
Then the density of F is uniformly bounded, and the bound does not depend on F but only
on the constants c1(p) and c2(ℓ, p).
Proof. The proof of this result uses the same arguments as in the proof of Dalang and
Nualart [DN04, Lemma 4.11]. Therefore, we will only give the main steps.
Fix z ∈ Rd. Thanks to Corollary 3.3 and the Cauchy-Schwarz inequality we find that
|pF (z)| ≤ ‖H(1,...,d)(F, 1)‖0,2.
Using the continuity of the Skorohod integral δ (cf. Nualart [N95, Proposition 3.2.1]
and Nualart [N98, (1.11) and p.131]) and Hölder’s inequality for Malliavin norms (cf.
Watanabe [W84, Proposition 1.10, p.50]), we obtain
‖H(1,...,d)(F, 1)‖0,2 ≤ c‖H(1,...,d−1)(F, 1)‖1,4
‖(γ−1F )d,j‖1,8 ‖D(F
j)‖1,8. (3.1)
In agreement with hypothesis (b), ‖D(F j)‖m,p ≤ c. In order to bound the second factor in
(3.1), note that
‖(γ−1F )i,j‖m,p =
E[|(γ−1F )i,j|
E[‖Dk(γ−1F )i,j‖
. (3.2)
For the first term in (3.2), we use Cramer’s formula to get that
|(γ−1F )i,j| = |(det γF )
−1(AF )i,j |,
where AF denotes the cofactor matrix of γF . By means of Cauchy-Schwarz inequality and
hypotheses (a) and (b) we find that
E[((γ−1F )i,j)
p] ≤ cd,p{E[(det γF )−2p]}1/2 × {E[‖D(F )‖4p(d−1)H ]}
≤ cd,p,
where none of the constants depend on F . For the second term on the right-hand side of
(3.2), we iterate the equality (cf. Nualart [N95, Lemma 2.1.6])
D(γ−1F )i,j = −
k,ℓ=1
(γ−1F )i,kD(γF )k,ℓ(γ
F )ℓ,j, (3.3)
in the same way as in the proof of Dalang and Nualart [DN04, Lemma 4.11]. Then,
appealing again to hypotheses (a) and (b) and iterating the inequality (3.1) to bound the
first factor on the right-hand side of (3.2), we obtain the uniform boundedness of pF (z).
We finish this section with a result that will be used later on to bound negative moments
of a random variable, as is needed to check hypothesis (a) of Proposition 3.4.
Proposition 3.5. Suppose Z ≥ 0 is a random variable for which we can find ǫ0 ∈ (0, 1),
processes {Yi,ǫ}ǫ∈(0,1) (i = 1, 2), and constants c > 0 and 0 ≤ α2 ≤ α1 with the property that
Z ≥ min(cǫα1 − Y1,ǫ cǫα2 − Y2,ǫ) for all ǫ ∈ (0, ǫ0). Also suppose that we can find βi > αi
(i = 1, 2), not depending on ǫ0, such that
C(q) := sup
0<ǫ<1
E[|Y1,ǫ|q]
E[|Y2,ǫ|q]
<∞ for all q ≥ 1.
Then for all p ≥ 1, there exists a constant c′ ∈ (0,∞), not depending on ǫ0, such that
E[|Z|−p] ≤ c′ǫ−pα10 .
Remark 3.6. This lemma is of interest mainly when β2 ≤ α1.
Proof. Define k := (2/c)ǫ
0 . Suppose that y ≥ k, and let ǫ := (2/c)1/α1y−1/α1 . Then
0 < ǫ ≤ ǫ0, y−1 = (c/2)ǫα1 , and for all q ≥ 1,
Z−1 > y
Z < y−1
Y1,ǫ ≥
Y2,ǫ ≥ cǫα2 −
≤ C(q)
2qǫq(β1−α1) + ǫqβ2
ǫα2 − 1
The inequality ǫα2 − (1/2)ǫα1 ≥ (1/2)ǫα2 implies that
Z−1 > y
≤ C(q)
2qǫq(β1−α1) + 2qǫq(β2−α2)
≤ ay−qb,
where a and b are positive and finite constants that do not depend on y, ǫ0 or q. We apply
this with q := (p/b) + 1 to find that for all p ≥ 1,
|Z|−p
yp−1P
Z−1 > y
≤ kp + ap
y−b−1 dy = kp +
Because k ≥ (2/c) and b > 0, it follows that E[|Z|−p] ≤ (1 + c1(ap/b))kp, where c1 :=
(c/2)b+p. This is the desired result.
4 Existence, smoothness and uniform boundedness of the
one-point density
Let u = {u(t, x), t ∈ [0, T ], x ∈ [0, 1]} be the solution of equation (2.1). In this section,
we prove the existence, smoothness and uniform boundedness of the density of the random
vector u(t, x). In particular, this will prove Theorem 1.1(a).
The first result concerns the Malliavin differentiability of u and the equations satisfied by
its derivatives. We refer to Bally and Pardoux [BP98, Proposition 4.3, (4.16), (4.17)] for
its proof in dimension one. As we work coordinate by coordinate, the following proposition
follows in the same way and its proof is therefore omitted.
Proposition 4.1. Assume P1. Then u(t, x) ∈ (D∞)d for any t ∈ [0, T ] and x ∈ [0, 1].
Moreover, its iterated derivative satisfies
D(k1)r1,v1 · · ·D
rn,vn
(ui(t, x))
Gt−rl(x, vl)
D(k1)r1,v1 · · ·D
(kl−1)
rl−1,vl−1D
(kl+1)
rl+1,vl+1 · · ·D(kn)rn,vn(σikl(u(rl, vl)))
r1∨···∨rn
Gt−θ(x, η)
D(kl)rl,vl(σij(u(θ, η)))W
j(dθ, dη)
r1∨···∨rn
Gt−θ(x, η)
D(kl)rl,vl(bi(u(θ, η))) dθdη
if t ≤ r1 ∨ · · · ∨ rn and D(k1)r1,v1 · · ·D
rn,vn(ui(t, x)) = 0 otherwise. Finally, for any p > 1,
sup(t,x)∈[0,T ]×[0,1]E
∥Dn(ui(t, x))
< +∞. (4.1)
Note that, in particular, the first-order Malliavin derivative satisfies, for r < t,
D(k)r,v (ui(t, x)) = Gt−r(x, v)σik(u(r, v)) + ai(k, r, v, t, x), (4.2)
where
ai(k, r, v, t, x) =
Gt−θ(x, η)D
r,v (σij(u(θ, η)))W
j(dθ, dη)
Gt−θ(x, η)D
r,v (bi(u(θ, η))) dθdη,
(4.3)
and D
r,v (ui(t, x)) = 0 when r > t.
The next result proves property (a) in Proposition 3.4 when F is replaced by u(t, x).
Proposition 4.2. Assume P1 and P2. Let I and J two compact intervals as in Theorem
1.1. Then, for any p ≥ 1,
(det γu(t,x))
is uniformly bounded over (t, x) ∈ I × J .
Proof. This proof follows Nualart [N98, Proof of (3.22)], where it is shown that for fixed
(t, x), E[(detγu(t,x))
−p] < +∞. Our emphasis here is on the uniform bound over (t, x) ∈
I × J . Assume that I = [t1, t2] and J = [x1, x2], where 0 < t1 < t2 ≤ T , 0 < x1 < x2 < 1.
Let (t, x) ∈ I × J be fixed. We write
det γu(t,x) ≥
infξ∈Rd:‖ξ‖=1 ξ
Tγu(t,x)ξ
Let ξ = (ξ1, ..., ξd) ∈ Rd with ‖ξ‖ = 1 and fix ǫ ∈ (0, 1). Note the inequality
(a+ b)2 ≥ 2
a2 − 2b2, (4.4)
valid for all a, b ≥ 0. Using (4.2) and the fact that γu(t,x) is a matrix whose entries are
inner-products, this implies that
ξTγu(t,x)ξ =
Dr,v(ui(t, x))ξi
t(1−ǫ)
Dr,v(ui(t, x))ξi
≥ I1 − I2,
where
t(1−ǫ)
Gt−r(x, v)σik(u(r, v))ξi
I2 =2
t(1−ǫ)
ai(k, r, v, t, x)ξi
and ai(k, r, v, t, x) is defined in (4.3). In accord with hypothesis P2 and thanks to Lemma
I1 ≥ c(tǫ)1/2, (4.5)
where c is uniform over (t, x) ∈ I × J .
Next we apply the Cauchy-Schwarz inequality to find that, for any q ≥ 1,
supξ∈Rd:‖ξ‖=1|I2|q
≤ c(E[|A1|q] + E[|A2|q]),
where
i,j,k=1
t(1−ǫ)
Gt−θ(x, η)D
r,v (σij(u(θ, η)))W
j(dθ, dη)
i,k=1
t(1−ǫ)
Gt−θ(x, η)D
r,v (bi(u(θ, η))) dθdη
We bound the q-th moment of A1 and A2 separately. As regards A1, we use Burkholder’s
inequality for martingales with values in a Hilbert space (Lemma 7.6) to obtain
E[|A1|q] ≤ c
k,i=1
t(1−ǫ)
t(1−ǫ)
, (4.6)
where
Θ := 1{θ>r}Gt−θ(x, η)
D(k)r,v (σij(u(θ, η)))
≤ c1{θ>r}Gt−θ(x, η)
D(k)r,v (ul(θ, η))
thanks to hypothesis P1. Hence,
E[|A1|q] ≤ c
t(1−ǫ)
dη G2t−θ(x, η)
∫ t∧θ
t(1−ǫ)
where Ψ :=
r,v (ul(θ, η)). We now apply Hölder’s inequality with respect to the
measure G2t−θ(x, η)dθdη to find that
E[|A1|q] ≤ C
t(1−ǫ)
dη G2t−θ(x, η)
t(1−ǫ)
dη G2t−θ(x, η)
t(1−ǫ)
Lemmas 7.3 and 7.5 assure that
E[|A1|q] ≤ CT (tǫ)
2 (tǫ)q/2
t(1−ǫ)
G2t−θ(x, η) dθdη ≤ CT (tǫ)q,
where CT is uniform over (t, x) ∈ I × J .
We next derive a similar bound for A2. By the Cauchy–Schwarz inequality,
E [|A2|q] ≤ c(tǫ)q
i,k=1
t(1−ǫ)
where Φ := Gt−θ(x, η)|D(k)r,v (bi(u(θ, η))) |. From here on, the q-th moment of A2 is estimated
as that of A1 was; cf. (4.6), and this yields E[|A2|q] ≤ CT (tǫ)2q.
Thus, we have proved that
supξ∈Rd:‖ξ‖=1|I2|q
≤ CT (tǫ)q, (4.7)
where the constant CT is clearly uniform over (t, x) ∈ I × J .
Finally, we apply Proposition 3.5 with Z := inf‖ξ‖=1(ξ
Tγu(t,x)ξ), Y1,ǫ = Y2,ǫ = sup‖ξ‖=1I2,
ǫ0 = 1, α1 = α2 = 1/2 and β1 = β2 = 1, to get
(detγu(t,x))
≤ CT ,
where all the constants are clearly uniform over (t, x) ∈ I×J . This is the desired result.
Corollary 4.3. Assume P1 and P2. Fix T > 0 and let I and J be a compact intervals
as in Theorem 1.1. Then, for any (t, x) ∈ (0, T ]× (0, 1), u(t, x) is a nondegenerate random
vector and its density funciton is infinitely differentiable and uniformly bounded over z ∈ Rd
and (t, x) ∈ I × J .
Proof of Theorem 1.1(a). This is a consequence of Propositions 4.1 and 4.2 together with
Theorem 3.1 and Proposition 3.4.
5 The Gaussian-type lower bound on the one-point density
The aim of this section is to prove the lower bound of Gaussian-type for the density of
u stated in Theorem 1.1(b). The proof of this result was given in Kohatsu-Higa [K03,
Theorem 10] for dimension 1, therefore we will only sketch the main steps.
Proof of Theorem 1.1(b). We follow [K03] and we show that for each (t, x), F = u(t, x) is a
d-dimensional uniformly elliptic random vector and then we apply [K03, Theorem 5]. Let
F in =
Gt−r(x, v)
σij(u(r, v))W
j(dr, dv) +
Gt−r(x, v) bi(u(r, v)) drdv,
1 ≤ i ≤ d, where 0 = t0 < t1 < · · · < tN = t is a sufficiently fine partition of [0, t]. Note
that Fn ∈ Ftn . Set g(s, y) = Gt−s(x, y). We shall need the following two lemmas.
Lemma 5.1. [K03, Lemma 7] Assume P1 and P2. Then:
(i) ‖F in‖k,p ≤ ck,p, 1 ≤ i ≤ d;
(ii) ‖((γFn(tn−1))ij)−1‖p,tn−1 ≤ cp(∆n−1(g))−1 = cp(‖g‖2L2([tn−1,tn]×[0,1]))
where γFn(tn−1) denotes the conditional Malliavin matrix of Fn given Ftn−1 and ‖ · ‖p,tn−1
denotes the conditional Lp-norm.
We define
un−1i (s1, y1) =
∫ tn−1
Gs1−s2(y1, y2)
σij(u(s2, y2))W
j(ds1, dy2)
∫ tn−1
Gs1−s2(y1, y2) bi(u(s2, y2)) ds2dy2, 1 ≤ i ≤ d.
Note that un−1 ∈ Ftn−1 . As in [K03], the following holds.
Lemma 5.2. [K03, Lemma 8] Under hypothesis P1, for s ∈ [tn−1, tn],
‖ui(s, y)− un−1i (s, y)‖n,p,tn−1 ≤ (s− tn−1)
1/8, 1 ≤ i ≤ d,
where ‖ · ‖n,p,tn−1 denotes the conditional Malliavin norm given Ftn−1 .
The rest of the proof of Theorem 1.1(b) follows along the same lines as in [K03] for
d = 1. We only sketch the remaining main points where the fact that d > 1 is important.
In order to obtain the expansion of F in−F in−1 as in [K03, Lemma 9], we proceed as follows.
By the mean value theorem,
F in − F in−1 =
Gt−r(x, v)
σij(u
n−1(r, v))W j(dr, dv)
Gt−r(x, v) bi(u(r, v)) drdv
Gt−r(x, v)
j,l=1
∂lσij(u(r, v, λ))dλ)(ul(r, v) − un−1l (r, v))W
j(dr, dv),
where u(r, v, λ) = (1−λ)u(r, v)+λun−1(r, v). Using the terminology of [K03], the first term
is a process of order 1 and the next two terms are residues of order 1 (as in [K03]). In the
next step, we write the residues of order 1 as the sum of processes of order 2 and residues
of order 2 and 3 as follows:
Gt−r(x, v) bi(u(r, v)) drdv
Gt−r(x, v) bi(u
n−1(r, v)) drdv
Gt−r(x, v)
∂lbi(u(r, v, λ))dλ)(ul(r, v) − un−1l (r, v)) drdv
Gt−r(x, v)
j,l=1
∂lσij(u(r, v, λ))dλ)(ul(r, v) − un−1l (r, v))W
j(dr, dv)
Gt−r(x, v)
j,l=1
∂lσij(u
n−1(r, v))(ul(r, v) − un−1l (r, v))W
j(dr, dv)
Gt−r(x, v)
j,l,l′=1
∂l∂l′σij(u(r, v, λ))dλ)
× (ul(r, v) − un−1l (r, v))(ul′ (r, v) − u
(r, v))W j(dr, dv).
It is then clear that the remainder of the proof of [K03, Lemma 9] follows for d > 1 along
the same lines as in [K03], working coordinate by coordinate.
Finally, in order to complete the proof of the proposition, it suffices to verify the hy-
potheses of [K03, Theorem 5]. Again the proof follows as in the proof of [K03, Theorem
10], working coordinate by coordinate. We will only sketch the proof of his (H2c), where
hypothesis P2 is used:
(∆n−1(g))
(Gt−r(x, v))
2‖σ(un−1(r, v))ξ‖2 drdv
≥ ρ2(∆n−1(g))−1
(Gt−r(x, v))
2 drdv = ρ2 > 0,
by the definition of g. This concludes the proof of Theorem 1.1 (b).
6 The Gaussian-type upper bound on the two-point density
Let ps,y; t,x(z1, z2) denote the joint density of the 2d-dimensional random vector
(u1(s, y), ..., ud(s, y), u1(t, x), ..., ud(t, x)),
for s, t ∈ (0, T ], x, y ∈ (0, 1), (s, y) 6= (t, x) and z1, z2 ∈ Rd (the existence of this joint
density will be a consequence of Theorem 3.1, Proposition 4.1 and Theorem 6.3).
The next subsections lead to the proofs of Theorem 1.1(c) and (d).
6.1 Bounds on the increments of the Malliavin derivatives
In this subsection, we prove an upper bound for the Sobolev norm of the derivative of the
increments of our process u. For this, we will need the following preliminary estimate.
Lemma 6.1. For any s, t ∈ [0, T ], s ≤ t, and x, y ∈ [0, 1],
(g(r, v))2 drdv ≤ CT (|t− s|1/2 + |x− y|),
where
g(r, v) := gt,x,s,y(r, v) = 1{r≤t}Gt−r(x, v) − 1{r≤s}Gs−r(y, v).
Proof. Using Bally, Millet, and Sanz-Solé [BMS95, Lemma B.1] with α = 2, we see
(g(r, v))2 drdv
(Gt−r(x, v))
2 drdv + 2
(Gt−r(x, v) −Gs−r(x, v))2 drdv
(Gs−r(x, v)−Gs−r(y, v))2 drdv
≤ CT (|t− s|1/2 + |x− y|).
Proposition 6.2. Assuming P1, for any s, t ∈ [0, T ], s ≤ t, x, y ∈ [0, 1], p > 1, m ≥ 1,
∥Dm(ui(t, x)− ui(s, y))
|t− s|1/2 + |x− y|
, i = 1, ..., d.
Proof. Let m = 1. Consider the function g(r, v) defined in Lemma 6.1. Using the integral
equation (4.2) satisfied by the first-order Malliavin derivative, we find that
∥D(ui(t, x)− ui(s, y))
E[|I1|p/2] + E[|I2|p/2] + E[|I3|p/2]
where
dv (g(r, v)σik(u(r, v)))
j,k=1
g(θ, η)D(k)r,v (σij(u(θ, η)))W
j(dθ, dη)
g(θ, η)D(k)r,v (bi(u(θ, η)))dθdη
We bound the p/2-moments of I1, I2 and I3 separately.
By hypothesis P1 and Lemma 6.1, E[|I1|p/2] ≤ CT (|t − s|1/2 + |x − y|)p/2. Using
Burkholder’s inequality for Hilbert-space-valued martingales (Lemma 7.6) and hypothesis
P1, we obtain
E[|I2|p/2] ≤ C
dη (g(θ, η))2
where Θ :=
r,v (ul(θ, η)). From Hölder’s inequality with respect to the measure
(g(θ, η))2dθdη, we see that this is bounded above by
(g(θ, η))2dθdη
× sup(θ,η)∈[0,T ]×[0,1]
dη(g(θ, η))2
≤ CT (|t− s|1/2 + |x− y|)p/2,
thanks to (4.1) and Lemma 6.1.
We next derive a similar bound for I3. By the Cauchy–Schwarz inequality,
E[|I3|p/2] ≤ CT
dη (g(θ, η))2
From here on, the p/2-moment of I3 is estimated as was that of I2, and this yields E[|I3|p/2] ≤
CT (|t − s|1/2 + |x − y|)p/2. This proves the desired result for m = 1. The case m > 1
follows using the stochastic differential equation satisfied by the iterated Malliavin deriva-
tives (Proposition 4.1), Hölder’s and Burkholder’s inequalities, hypothesis P1, (4.1) and
Lemma 6.1 in the same way as we did for m = 1, to obtain the desired bound.
6.2 Study of the Malliavin matrix
For s, t ∈ [0, T ], s ≤ t, and x, y ∈ [0, 1] consider the 2d-dimensional random vector
Z := (u1(s, y), ..., ud(s, y), u1(t, x)− u1(s, y), ..., ud(t, x)− ud(s, y)). (6.1)
Let γZ the Malliavin matrix of Z. Note that γZ = ((γZ)m,l)m,l=1,...,2d is a symmetric 2d×2d
random matrix with four d× d blocs of the form
... γ
· · ·
... · · ·
... γ
where
Z = (〈D(ui(s, y)),D(uj(s, y))〉H )i,j=1,...,d,
Z = (〈D(ui(s, y)),D(uj(t, x)− uj(s, y))〉H )i,j=1,...,d,
Z = (〈D(ui(t, x)− ui(s, y)),D(uj(s, y))〉H )i,j=1,...,d,
Z = (〈D(ui(t, x)− ui(s, y)),D(uj(t, x)− uj(s, y))〉H )i,j=1,...,d.
We let (1) denote the set of indices {1, ..., d}×{1, ..., d}, (2) the set {1, ..., d}×{d+1, ..., 2d},
(3) the set {d+ 1, ..., 2d} × {1, ..., d} and (4) the set {d+ 1, ..., 2d} × {d+ 1, ..., 2d}.
The following theorem gives an estimate on the Sobolev norm of the entries of the inverse
of the matrix γZ , which depends on the position of the entry in the matrix.
Theorem 6.3. Fix η, T > 0. Assume P1 and P2. Let I and J be two compact intervals
as in Theorem 1.1.
(a) For any (s, y) ∈ I × J , (t, x) ∈ I × J , s ≤ t, (s, y) 6= (t, x), k ≥ 0, p > 1,
‖(γ−1Z )m,l‖k,p ≤
ck,p,η,T (|t− s|1/2 + |x− y|)−dη if (m, l) ∈ (1),
ck,p,η,T (|t− s|1/2 + |x− y|)−1/2−dη if (m, l) ∈ (2) or (3),
ck,p,η,T (|t− s|1/2 + |x− y|)−1−dη if (m, l) ∈ (4).
(b) For any s = t ∈ (0, T ], (t, y) ∈ I × J , (t, x) ∈ I × J , x 6= y, k ≥ 0, p > 1,
‖(γ−1Z )m,l‖k,p ≤
ck,p,T if (m, l) ∈ (1) ,
ck,p,T |x− y|−1/2 if (m, l) ∈ (2) or (3),
ck,p,T |x− y|−1 if (m, l) ∈ (4).
(Note the slight improvements in the exponents in case (b) where s = t.)
The proof of this theorem is deferred to Section 6.4. We assume it for the moment and
complete the proof of Theorem 1.1(c) and (d).
6.3 Proof of Theorem 1.1(c) and (d)
Fix two compact intervals I and J as in Theorem 1.1. Let (s, y), (t, x) ∈ I × J , s ≤ t,
(s, y) 6= (t, x), and z1, z2 ∈ Rd. Let Z be as in (6.1) and let pZ be the density of Z. Then
ps,y; t,x(z1, z2) = pZ(z1, z1 − z2).
Apply Corollary 3.3 with σ = {i ∈ {1, ..., d} : zi1 − zi2 ≥ 0} and Hölder’s inequality to see
pZ(z1, z1 − z2) ≤
|ui(t, x)− ui(s, y)| > |zi1 − zi2|
× ‖H(1,...,2d)(Z, 1)‖0,2.
Therefore, in order to prove the desired results (c) and (d) of Theorem 1.1, it suffices to
prove that:
|ui(t, x)− ui(s, y)| > |zi1 − zi2|
≤ c exp
− ‖z1 − z2‖
cT (|t− s|1/2 + |x− y|)
, (6.2)
‖H(1,...,2d)(Z, 1)‖0,2 ≤ cT (|t− s|1/2 + |x− y|)−(d+η)/2, (6.3)
and if s = t, then
‖H(1,...,2d)(Z, 1)‖0,2 ≤ cT |x− y|−d/2. (6.4)
Proof of (6.2). Let ũ denote the solution of (2.1) for b ≡ 0. Consider the continuous one-
parameter martingale (Mu = (M
u , ...,M
u ), 0 ≤ u ≤ t) defined by
M iu =
(Gt−r(x, v)−Gs−r(y, v))
j=1 σij(ũ(r, v))W
j(dr, dv) if 0 ≤ u ≤ s,
(Gt−r(x, v) −Gs−r(y, v))
j=1 σij(ũ(r, v))W
j(dr, dv)
Gt−r(x, v)
j=1 σij(ũ(r, v))W
j(dr, dv) if s ≤ u ≤ t,
for all i = 1, ..., d, with respect to the filtration (Fu, 0 ≤ u ≤ t). Notice that
M0 = 0, Mt = ũ(t, x)− ũ(s, y).
Moreover, by hypothesis P1 and Lemma 6.1,
〈M i〉t =
(Gt−r(x, v) −Gs−r(y, v))2
(σij(ũ(r, v)))
2 drdv
(Gt−r(x, v))
(σij(ũ(r, v)))
2 drdv
(g(r, v))2 drdv
≤ CT (|t− s|1/2 + |x− y|).
By the exponential martingale inequality Nualart [N95, A.5],
|ũi(t, x)− ũi(s, y)| > |zi1 − zi2|
≤ 2 exp
1 − zi2|2
CT (|t− s|1/2 + |x− y|)
. (6.5)
We will now treat the case b 6≡ 0 using Girsanov’s theorem. Consider the random
variable
Lt = exp
σ−1(u(r, v)) b(u(r, v)) ·W (dr, dv)
‖σ−1(u(r, v)) b(u(r, v))‖2 drdv
The following Girsanov’s theorem holds.
Theorem 6.4. [N94, Prop.1.6] E[Lt] = 1, and if P̃ denotes the probability measure on
(Ω,F ) defined by
(ω) = Lt(ω),
then W̃ (t, x) = W (t, x) +
σ−1(u(r, v)) b(u(r, v)) drdv is a standard Brownian sheet
under P̃.
Consequently, the law of u under P̃ coincides with the law of ũ under P. Consider now
the random variable
Jt = exp
σ−1(ũ(r, v)) b(ũ(r, v)) ·W (dr, dv)
‖σ−1(ũ(r, v)) b(ũ(r, v))‖2 drdv
Then, by Theorem 6.4, the Cauchy-Schwarz inequality and (6.5),
|ui(t, x)− ui(s, y)| > |zi1 − zi2|
1{|ui(t,x)−ui(s,y)||zi1−zi2|}
1{|ũi(t,x)−ũi(s,y)||zi1−zi2|}
|ũi(t, x)− ũi(s, y)||zi1 − zi2|
})1/2(
≤ 2 exp
1 − zi2|2
CT (|t− s|1/2 + |x− y|)
Now, hypothesis P1 and P2 give
t ] ≤ EP
2σ−1(ũ(r, v)) b(ũ(r, v)) ·W (dr, dv)
4 ‖σ−1(ũ(r, v)) b(ũ(r, v))‖2 drdv
× exp
‖σ−1(ũ(r, v)) b(ũ(r, v))‖2 drdv
since the second exponential is bounded and the first is an exponential martingale.
Therefore, we have proved that
|ui(t, x)− ui(s, y)| > |zi1 − zi2|
≤ C exp
1 − zi2|2
CT (|t− s|1/2 + |x− y|)
from which we conclude that
|ui(t, x)− ui(s, y)| > |zi1 − zi2|
≤ C exp
− ‖z1 − z2‖
CT (|t− s|1/2 + |x− y|)
This proves (6.2).
Proof of (6.3). As in (3.1), using the continuity of the Skorohod integral δ and Hölder’s
inequality for Malliavin norms, we obtain
‖H(1,...,2d)(Z, 1)‖0,2 ≤ C‖H(1,...,2d−1)(Z, 1)‖1,4
‖(γ−1Z )2d,j‖1,8 ‖D(Z
j)‖1,8
j=d+1
‖(γ−1Z )2d,j‖1,8 ‖D(Z
j)‖1,8
Notice that the entries of γ−1Z that appear in this expression belong to sets (3) and (4) of
indices, as defined before Theorem 6.3. From Theorem 6.3(a) and Propositions 4.1 and 6.2,
we find that this is bounded above by
CT ‖H(1,...,2d−1)(Z, 1)‖1,4
(|t−s|1/2+ |x−y|)−
j=d+1
(|t−s|1/2+ |x−y|)−1−dη+
that is, by
CT ‖H(1,...,2d−1)(Z, 1)‖1,4(|t− s|1/2 + |x− y|)−1/2−dη .
Iterating this procedure d times (during which we only encounter coefficients (γ−1Z )m,l for
(m, l) in blocs (3) and (4), cf. Theorem 6.3(a)), we get, for some integers m0, k0 > 0,
‖H(1,...,2d)(Z, 1)‖0,2 ≤ CT ‖H(1,...,d)(Z, 1)‖m0 ,k0(|t− s|1/2 + |x− y|)−d/2−d
Again, using the continuity of δ and Hölder’s inequality for the Malliavin norms, we obtain
‖H(1,...,d)(Z, 1)‖m,k ≤ C‖H(1,...,d−1)(Z, 1)‖m1 ,k1
‖(γ−1Z )d,j‖m2,k2 ‖D(Z
j)‖m3,k3
j=d+1
‖(γ−1Z )d,j‖m4,k4 ‖D(Z
j)‖m5,k5
for some integers mi, ki > 0, i = 1, ..., 5. This time, the entries of γ
Z that appear in this
expression come from the sets (1) and (2) of indices. We appeal again to Theorem 6.3(a)
and Propositions 4.1 and 6.2 to get
‖H(1,...,d)(Z, 1)‖m,k ≤ CT ‖H(1,...,d−1)(Z, 1)‖m1 ,k1(|t− s|1/2 + |x− y|)−dη.
Finally, iterating this procedure d times (during which we encounter coefficients (γ−1Z )m,l for
(m, l) in blocs (1) and (2) only, cf. Theorem 6.3(a)), and choosing η′ = 4d2η, we conclude
‖H(1,...,2d)(Z, 1)‖0,2 ≤ CT (|t− s|1/2 + |x− y|)−(d+η
′)/2,
which proves (6.3) and concludes the proof of Theorem 1.1(c).
Proof of (6.4). In order to prove (6.4), we proceed exactly along the same lines as in the
proof of (6.3) but we appeal to Theorem 6.3(b). This concludes the proof of Theorem
1.1(d).
6.4 Proof of Theorem 6.3
Let Z as in (6.1). Since the inverse of the matrix γZ is the inverse of its determinant
multiplied by its cofactor matrix, we examine these two factors separately.
Proposition 6.5. Fix T > 0 and let I and J be compact intervals as in Theorem 1.1.
Assuming P1, for any (s, y), (t, x) ∈ I × J , (s, y) 6= (t, x), p > 1,
E[|(AZ)m,l|p]1/p ≤
cp,T (|t− s|1/2 + |x− y|)d if (m, l) ∈ (1),
cp,T (|t− s|1/2 + |x− y|)d−
2 if (m, l) ∈ (2) or (3),
cp,T (|t− s|1/2 + |x− y|)d−1 if (m, l) ∈ (4),
where AZ denotes the cofactor matrix of γZ .
Proof. We consider the four different cases.
• If (m, l) ∈ (1), we claim that
|(AZ)m,l| ≤ C
‖D(u(s, y))‖2k
× ‖D(u(t, x)− u(s, y))‖2(d−1−k)
× ‖D(u(s, y))‖2(d−1−k)
× ‖D(u(t, x) − u(s, y))‖2(k+1)
(6.6)
Indeed, let A
Z = (a
m̄,l̄
)m̄,l̄=1,...,2d−1 be the (2d−1)× (2d−1)-matrix obtained by removing
from γZ its row m and column l. Then
(AZ)m,l = det ((AZ)
m,l) =
π permutation of (1,...,2d−1)
1,π(1)
· · · am,l
2d−1,π(2d−1).
Each term of this sum contains one entry from each row and column of A
Z . If there are
k entries taken from bloc (1) of γZ , these occupy k rows and columns of A
Z . Therefore,
d−1−k entries must come from the d−1 remaining rows of bloc (2), and the same number
from the columns of bloc (3). Finally, there remain k+1 entries to be taken from bloc (4).
Therefore,
|(AZ)m,l| ≤ C
(product of k entries from (1))
× (product of d− 1− k entries from (2))
× (product of d− 1− k entries from (3))× (product of k + 1 entries from (4))
Adding all the terms and using the particular form of these terms establishes (6.6).
Regrouping the various factors in (6.6), applying the Cauchy-Schwarz inequality and
using (4.1) and Proposition 6.2, we obtain
|(AZ)m,l|p
‖D(u(s, y))‖2(d−1)p
× ‖D(u(t, x) − u(s, y))‖2dp
≤ CT (|t− s|1/2 + |x− y|)dp.
• If (m, l) ∈ (2) or (m, l) ∈ (3), then using the same arguments as above, we obtain
|(AZ)m,l|
‖D(u(s, y))‖2(d−1−k)
× ‖D(u(s, y))‖k
× ‖D(u(t, x) − u(s, y))‖k
× ‖D(u(s, y))‖k+1
× ‖D(u(t, x)− u(s, y))‖k+1
× ‖D(u(t, x)− u(s, y))‖2(d−1−k)
‖D(u(s, y))‖2d−1
× ‖D(u(t, x) − u(s, y))‖2d−1
from which we conclude, using (4.1) and Proposition 6.2, that
|(AZ)m,l|p
≤ CT (|t− s|1/2 + |x− y|)(d−
• If (i, j) ∈ (4), we obtain
|(AZ)m,l| ≤ C
‖D(u(s, y))‖2(k+1)
× ‖D(u(s, y))‖2(d−1−k)
× ‖D(u(t, x) −D(u(s, y)))‖2(d−1−k)
× ‖D(u(t, x) −D(u(s, y)))‖2k
‖D(u(s, y))‖2d
× ‖D(u(t, x) − u(s, y))‖2d−2
from which we conclude that
|(AZ)m,l|p
≤ CT (|t− s|1/2 + |x− y|)(d−1)p.
This concludes the proof of the proposition.
Proposition 6.6. Fix η, T > 0. Assume P1 and P2. Let I and J be compact intervals as
in Theorem 1.1.
(a) There exists C depending on T and η such that for any (s, y), (t, x) ∈ I × J , (s, y) 6=
(t, x), p > 1,
det γZ
)−p]1/p ≤ C(|t− s|1/2 + |x− y|)−d(1+η). (6.7)
(b) There exists C only depending on T such that for any s = t ∈ I, x, y ∈ J , x 6= y,
p > 1,
det γZ
)−p]1/p ≤ C(|x− y|)−d.
Assuming this proposition, we will be able to conclude the proof of Theorem 6.3, after
establishing the following estimate on the derivative of the Malliavin matrix.
Proposition 6.7. Fix T > 0. Let I and J be compact intervals as in Theorem 1.1. As-
suming P1, for any (s, y), (t, x) ∈ I × J , (s, y) 6= (t, x), p > 1 and k ≥ 1,
‖Dk(γZ)m,l‖pH ⊗k
]1/p ≤
ck,p,T if (m, l) ∈ (1),
ck,p,T (|t− s|1/2 + |x− y|)1/2 if (m, l) ∈ (2) or (3),
ck,p,T (|t− s|1/2 + |x− y|) if (m, l) ∈ (4).
Proof. We consider the four different blocs.
• If (m, l) ∈ (4), proceeding as inDalang and Nualart [DN04, p.2131] and appealing
to Proposition 6.2 twice, we obtain
∥Dk(γZ)m,l
dv Dr,v(um(t, x)− um(s, y)) ·Dr,v(ul(t, x) − ul(s, y))
≤ (k + 1)p−1
dv DjDr,v(um(t, x)− um(s, y))
·Dk−jDr,v(ul(t, x)− ul(s, y))
≤ C̃T (k + 1)p−1
∥DjD(um(t, x)− um(s, y))
H ⊗(j+1)
])1/2
∥Dk−jD(ul(t, x)− ul(s, y))
H ⊗(k−j+1)
])1/2}
≤ CT (|t− s|1/2 + |x− y|)p.
• If (m, l) ∈ (2) or (m, l) ∈ (3), proceeding as above and appealing to (4.1) and Propo-
sition 6.2, we get
∥Dk(γZ)m,l
≤ C̃T (k + 1)p−1
∥DjD(um(t, x)− um(s, y))
H ⊗(j+1)
])1/2
∥Dk−jD(ul(s, y))
H ⊗(k−j+1)
])1/2}
≤ CT (|t− s|1/2 + |x− y|)p/2.
• If (m, l) ∈ (1), using (4.1), we obtain
∥Dk(γZ)m,l
≤ C̃T (k + 1)p−1
∥DjD(um(s, y))
H ⊗(j+1)
])1/2
∥Dk−jD(ul(s, y))
H ⊗(k−j+1)
])1/2}
≤ CT .
Proof of Theorem 6.3. When k = 0, the result follows directly using the fact that the
inverse of a matrix is the inverse of its determinant multiplied by its cofactor matrix and
the estimates of Propositions 6.5 and 6.6.
For k ≥ 1, we shall establish the following two properties.
(a) For any (s, y), (t, x) ∈ I × J , (s, y) 6= (t, x), s ≤ t, k ≥ 1 and p > 1,
E[‖Dk(γ−1Z )m,l‖
]1/p ≤
ck,p,η,T (|t− s|1/2 + |x− y|)−dη if (m, l) ∈ (1) ,
ck,p,η,T (|t− s|1/2 + |x− y|)−1/2−dη if (m, l) ∈ (2), (3),
ck,p,η,T (|t− s|1/2 + |x− y|)−1−dη if (m, l) ∈ (4).
(b) For any s = t ∈ I, x, y ∈ J , x 6= y, k ≥ 1 and p > 1,
E[‖Dk(γ−1Z )m,l‖
]1/p ≤
ck,p,T if (m, l) ∈ (1) ,
ck,p,T |x− y|−1/2 if (m, l) ∈ (2) or (3),
ck,p,T |x− y|−1 if (m, l) ∈ (4).
Since
‖(γ−1Z )m,l‖k,p =
E[|(γ−1Z )m,l|
E[‖Dj(γ−1Z )m,l‖
(a) and (b) prove the theorem.
We now prove (a) and (b). When k = 1, we will use (3.3) written as a matrix product:
D(γ−1Z ) = γ
Z D(γZ)γ
Z . (6.8)
Writing (6.8) in bloc product matrix notation with blocs (1), (2), (3) and (4), we get that
D((γ−1Z )
(1)) = (γ−1Z )
(1)D(γ
Z )(γ
(1) + (γ−1Z )
(1)D(γ
Z )(γ
+ (γ−1Z )
(2)D(γ
Z )(γ
(1) + (γ−1Z )
(2)D(γ
Z )(γ
D((γ−1Z )
(2)) = (γ−1Z )
(1)D(γ
Z )(γ
(2) + (γ−1Z )
(1)D(γ
Z )(γ
+ (γ−1Z )
(2)D(γ
Z )(γ
(2) + (γ−1Z )
(2)D(γ
Z )(γ
D((γ−1Z )
(3)) = (γ−1Z )
(3)D(γ
Z )(γ
(1) + (γ−1Z )
(3)D(γ
Z )(γ
+ (γ−1Z )
(4)D(γ
Z )(γ
(1) + (γ−1Z )
(4)D(γ
Z )(γ
D((γ−1Z )
(4)) = (γ−1Z )
(3)D(γ
Z )(γ
(2) + (γ−1Z )
(3)D(γ
Z )(γ
+ (γ−1Z )
(4)D(γ
Z )(γ
(2) + (γ−1Z )
(4)D(γ
Z )(γ
It now suffices to apply Hölder’s inequality to each block and use the estimates of the
case k = 0 and Proposition 6.7 to obtain the desired result for k = 1. For instance, for
(m, l) ∈ (1),
(γ−1Z )
(2)D(γ
Z )(γ
≤ sup
m1,l1
(γ−1Z )
m1,l1
]1/(2p)
m2,l2
m2,l2
]1/(4p)
× sup
m3,l3
(γ−1Z )
m3,l3
]1/(4p)
≤ c (|t− s|1/2 + |x− y|)−
−dη+1− 1
−dη = c (|t− s|1/2 + |x− y|)−2dη .
For k ≥ 1, in order to calculate Dk+1(γ(·)Z ), we will need to compute Dk(γ
Z D(γZ)γ
For bloc numbers i1, i2, i3 ∈ {1, 2, 3, 4} and k ≥ 1, we have
(γ−1Z )
(i1)D(γ
Z )(γ
j1+j2+j3=k
ji∈{0,...,k}
j1 j2 j3
(γ−1Z )
(γ−1Z )
Note that by Proposition 6.7, the norms of the derivatives Dj2
Z ) of γ
Z are of the
same order for all j2. Hence, we appeal again to Hölder’s inequality and Proposition 6.7,
and use a recursive argument in order to obtain the desired bounds.
Proof of Proposition 6.6. The main idea for the proof of Proposition 6.6 is to use a pertur-
bation argument. Indeed, for (t, x) close to (s, y), the matrix γZ is close to
... 0
· · ·
... · · ·
... 0
The matrix γ̂ has d eigenvectors of the form (λ̂1,0), ..., (λ̂d,0), where λ̂1, ..., λ̂d ∈ Rd are
eigenvectors of γ
Z = γu(s,y), and 0 = (0, ..., 0) ∈ Rd, and d other eigenvectors of the form
(0, ei) where e1, ..., ed is a basis of Rd. These last eigenvectors of γ̂ are associated with the
eigenvalue 0, while the former are associated with eigenvalues of order 1, as can be seen in
the proof of Proposition 4.2.
We now write
det γZ =
(ξi)T γZξ
i, (6.9)
where ξ = {ξ1, ..., ξ2d} is an orthonormal basis of R2d consisting of eigenvectors of γZ . We
then expect that for (t, x) close to (s, y), there will be d eigenvectors close to the subspace
generated by the (λ̂i,0), which will contribute a factor of order 1 to the product in (6.9),
and d other eigenvectors, close to the subspace generated by the (0, ei), that will each
contribute a factor of order (|t− s|1/2 + |x− y|)−1−η to the product. Note that if we do not
distinguish between these two types of eigenvectors, but simply bound below the product
by the smallest eigenvalue to the power 2d, following the approach used in the proof of
Proposition 4.2, then we would obtain C(|t− s|1/2 + |x − y|)−2dp in the right-hand side of
(6.7), which would not be the correct order.
We now carry out this somewhat involved perturbation argument. Consider the spaces
E1 = {(λ,0) : λ ∈ Rd,0 ∈ Rd} and E2 = {(0, µ) : µ ∈ Rd,0 ∈ Rd}. Note that every ξi can
be written as
ξi = (λi, µi) = αi(λ̃
i,0) +
1− α2i (0, µ̃
i), (6.10)
where λi, µi ∈ Rd, (λ̃i,0) ∈ E1, (0, µ̃i) ∈ E2, with ‖λ̃i‖ = ‖µ̃i‖ = 1 and 0 ≤ αi ≤ 1. Note
in particular that ‖ξi‖2 = ‖λi‖2 + ‖µi‖2 = 1 (norms of elements of Rd or R2d are Euclidean
norms).
Lemma 6.8. Given a sufficiently small α0 > 0, with probability one, there exist at least d
of these vectors, say ξ1, ..., ξd, such that α1 ≥ α0, ..., αd ≥ α0.
Proof. Observe that as ξ is an orthogonal family and for i 6= j, the Euclidean inner product
of ξi and ξj is
ξi · ξj = αiαj (λ̃i · λ̃j) +
1− α2i
1− α2j (µ̃
i · µ̃j) = 0.
For α0 > 0, let D = {i ∈ {1, ..., 2d} : αi < α0}. Then, for i, j ∈ D, i 6= j, if α0 < 12 , then
|µ̃i · µ̃j | = αiαj√
1− α2i
1− α2j
|λ̃i · λ̃j| ≤ α
1− α20
‖λ̃i‖‖λ̃j‖ ≤ 1
Since the diagonal terms of the matrix (µ̃i · µ̃j)i,j∈D are all equal to 1, for α0 sufficiently
small, it follows that det((µ̃i · µ̃j)i,j∈D) 6= 0. Therefore, {µ̃i, i ∈ D} is a linearly independent
family, and, as (0, µ̃i) ∈ E2, for i = 1, ..., 2d, we conclude that a.s., card(D) ≤ dim(E2) = d.
We can therefore assume that {1, ..., d} ⊂ Dc and so α1 ≥ α0,...,αd ≥ α0.
By Lemma 6.8 and Cauchy-Schwarz inequality one can write
det γZ
)−p]1/p ≤
(ξi)T γZξ
)−2p])1/(2p)
ξ=(λ,µ)∈R2d :
‖λ‖2+‖µ‖2=1
ξTγZξ
−2dp
)1/(2p)
With this, Propositions 6.9 and 6.15 below conclude the proof of Proposition 6.6.
6.4.1 Small Eigenvalues
Let I and J two compact intervals as in Theorem 1.1.
Proposition 6.9. Fix η, T > 0. Assume P1 and P2.
(a) There exists C depending on η and T such that for all s, t ∈ I, 0 < t−s < 1, x, y ∈ J ,
x 6= y, and p > 1,
ξ=(λ,µ)∈R2d :
‖λ‖2+‖µ‖2=1
ξTγZξ
−2dp
≤ C(|t− s|1/2 + |x− y|)−2dp(1+η).
(b) There exists C depending only on T such that for all s = t ∈ I, x, y ∈ J , x 6= y, and
p > 1,
ξ=(λ,µ)∈R2d:
‖λ‖2+‖µ‖2=1
ξTγZξ
−2dp
≤ C(|x− y|)−2dp.
Proof. We begin by proving (a). Since γZ is a matrix of inner products, we can write
ξTγZξ =
r,v (u(s, y)) + µi(D
r,v (u(t, x)) −D(k)r,v (u(s, y)))
Therefore, for ǫ ∈ (0, t− s),
ξTγZξ ≥ J1 + J2,
where
J1 :=
(λi − µi) [Gs−r(y, v)σik(u(r, v)) + ai(k, r, v, s, y)] +W
J2 :=
dvW 2,
ai(k, r, v, s, y) is defined in (4.3) and
[µiGt−r(x, v)σik(u(r, v)) + µiai(k, r, v, t, x)] .
From here on, the proof is divided into two cases.
Case 1. In the first case, we assume that |x− y|2 ≤ t− s. Choose and fix an ǫ ∈ (0, t − s).
Then we may write
‖ξ‖=1
ξTγZξ ≥ min
‖ξ‖=1 ,‖µ‖≥ǫη/2
J2 , inf
‖ξ‖=1 ,‖µ‖≤ǫη/2
We are going to prove that
‖ξ‖=1 ,‖µ‖≥ǫη/2
J2 ≥ ǫ
+η − Y1,ǫ,
‖ξ‖=1 ,‖µ‖≤ǫη/2
J1 ≥ ǫ1/2 − Y2,ǫ,
(6.11)
where, for all q ≥ 1,
E [|Y1,ǫ|q] ≤ c1(q)ǫq and E [|Y2,ǫ|q] ≤ c2(q)ǫq(
+η). (6.12)
We assume these, for the time being, and finish the proof of the proposition in Case 1. Then
we will return to proving (6.11) and (6.12).
We can combine (6.11) and (6.12) with Proposition 3.5 to find that
‖ξ‖=1
ξTγZξ
)−2pd
≤ c(t− s)−2pd(
(t− s)1/2 + |x− y|
]−2pd(1+2η)
whence follows the proposition in the case that |x− y|2 ≤ t− s. Now we complete our proof
of Case 1 by deriving (6.11) and (6.12).
Let us begin with the term that involves J2. Inequality (4.4) implies that
‖ξ‖=1 ,‖µ‖≥ǫη/2
J2 ≥ Ŷ1,ǫ − Y1,ǫ,
where
Ŷ1,ǫ :=
‖µ‖≥ǫη/2
µiσik(u(r, v))
G2t−r(x, v),
Y1,ǫ := 2 sup
‖µ‖≥ǫη/2
µiai(k, r, v, t, x)
In agreement with hypothesis P2, and thanks to Lemma 7.2,
Ŷ1,ǫ ≥ c inf
‖µ‖≥ǫη/2
‖µ‖2ǫ1/2 ≥ cǫ
Next we apply Lemma 6.11 below [with s := t] to find that E[|Y1,ǫ|q] ≤ cǫq. This proves the
bounds in (6.11) and (6.12) that concern J2 and Y1,ǫ.
In order to derive the second bound in (6.11), we appeal to (4.4) once more to find that
‖ξ‖=1 ,‖µ‖≤ǫη/2
J1 ≥ Ŷ2,ǫ − Y2,ǫ,
where
Ŷ2,ǫ :=
‖µ‖≤ǫη/2
(λi − µi)σik(u(r, v))
G2s−r(y, v),
Y2,ǫ := 2 (W1 +W2 +W3) ,
where
W1 := sup
‖µ‖≤ǫη/2
µiGt−r(x, v)σik(u(r, v))
W2 := sup
‖ξ‖=1
(λi − µi)ai(k, r, v, s, y)
W3 := sup
‖µ‖≤ǫη/2
µiai(k, r, v, t, x)
Hypothesis P2 and Lemma 7.2 together yield
Ŷ2,ǫ ≥ cǫ1/2. (6.13)
Next, we apply the Cauchy–Schwarz inequality to find that
E [|W1|q] ≤ sup
‖µ‖≤ǫη/2
‖µ‖2q × E
(σik(u(r, v)))
G2t−r(x, v)
≤ cǫqη
dv G2t−r(x, v)
thanks to hypothesis P1. In light of this, Lemma 7.4 implies that E [|W1|q] ≤ cǫq(
In order to bound the q-th moment of |W2|, we use the Cauchy–Schwarz inequality
together with hypothesis P1, and write
E [|W2|q] ≤ sup
‖µ‖≤ǫη/2
‖λ− µ‖2q × E
a2i (k, r, v, s, y)
a2i (k, r, v, s, y)
We apply Lemma 6.11 below [with s := t] to find that E [|W2|q] ≤ cǫq.
Similarly, we find using Lemma 6.11 that
E [|W3|q] ≤ sup
‖µ‖≤ǫη/2
‖µ‖2q × E
a2i (k, r, v, t, x)
≤ cǫqη (t− s+ ǫ)q/2ǫq/2
≤ cǫq(
The preceding bounds for W1, W2, and W3 prove, in conjunction, that E[|Y2,ǫ|q] ≤
c2(q)ǫ
+η). This and (6.13) together prove the bounds in (6.11) and (6.12) that concern
J1 and Y2,ǫ, whence follows the result in Case 1.
Case 2. Now we work on the second case where |x − y|2 ≥ t − s ≥ 0. Let ǫ > 0 be such
that (1+α)ǫ1/2 < 1
|x− y|, where α > 0 is large but fixed; its specific value will be decided
on later. Then
ξTγZξ ≥ I1 + I2 + I3,
where
I1 :=
dv (S1 + S2)
I2 :=
dv (S1 + S2)
I3 :=
(t−ǫ)∨s
dvS 22 ,
S1 :=
(λi − µi) [Gs−r(y, v)σi,k(u(r, v)) + ai(k, r, v, s, y)] ,
S2 :=
µi [Gt−r(x, v)σik(u(r, v)) + ai(k, r, v, t, x)] .
From here on, Case 2 is divided into two further sub-cases.
Sub-Case A. Suppose, in addition, that ǫ ≥ t− s. In this case, we are going to prove that
‖ξ‖=1
ξTγZξ ≥ cǫ1/2 − Z1,ǫ, (6.14)
where for all q ≥ 1,
E [|Z1,ǫ|q] ≤ c(q)ǫ3q/4. (6.15)
Apply (4.4) to find that
Ã1 −B(1)1 −B
where
Ã1 :=
[(λi − µi)Gs−r(y, v) + µiGt−r(x, v)] σik(u(r, v))
1 := 4‖λ− µ‖2
a2i (k, r, v, s, y), (6.16)
1 := 4‖µ‖
a2i (k, r, v, t, x). (6.17)
Using the inequality
(a− b)2 ≥ 2
a2 − 2ab, (6.18)
we see that
Ã1 ≥
A1 −B(3)1 ,
where
A1 :=
(λi − µi)Gs−r(y, v)σik(u(r, v))
1 := 2
(λi − µi)Gs−r(y, v)σik(u(r, v))
µiGt−r(x, v)σik(u(r, v))
We can combine terms to find that
A1 −B(1)1 −B
We proceed in like manner for I2, but obtain slightly sharper estimates as follows. Owing
to (6.18),
A2 −B(1)2 −B
where
A2 :=
µiGt−r(x, v)σik(u(r, v))
2 := 2
µiGt−r(x, v)σik(u(r, v))
µiai(k, r, v, t, x)
2 := 2
µiGt−r(x, v)σik(u(r, v))
(λi − µi)ai(k, r, v, s, y)
2 := 2
µiGt−r(x, v)σik(u(r, v))
(λi − µi)Gs−r(y, v)σik(u(r, v))
Finally, we appeal to (4.4) to find that
A3 −B3,
where
A3 :=
(t−ǫ)∨s
µiGt−r(x, v)σik(u(r, v))
B3 := 2
(t−ǫ)∨s
µiai(k, r, v, t, x)
. (6.19)
By hypothesis P2,
A1 +A2 +A3 ≥ ρ2
‖λ− µ‖2
dv G2s−r(y, v)
+ ‖µ‖2
dv G2t−r(x, v)
+ ‖µ‖2
dv G2t−r(x, v)
Note that we have used the defining assumption of Sub-Case A, namely, that ǫ ≥ t − s.
Next, we group the last two integrals and apply Lemma 7.2 to find that
A1 +A2 +A3 ≥ c
‖λ− µ‖2ǫ1/2 + ‖µ‖2
dv G2t−r(x, v)
‖λ− µ‖2 + ‖µ‖2
≥ cǫ1/2.
(6.20)
We are aiming for (6.14), and propose to bound the absolute moments of B
1 , B
i = 1, 2, 3 and B3, separately. According to Lemma 6.11 below with s = t,
‖ξ‖=1
|B3|q
≤ c(q)ǫq. (6.21)
Next we bound the absolute moments of B
1 , i = 1, 2, 3. Using hypothesis P1 and
Lemma 6.11, with t = s, we find that for all q ≥ 1,
‖ξ‖=1
≤ cǫq. (6.22)
In the same way, we see that
‖ξ‖=1
≤ c(t− s+ ǫ)q/2ǫq/2. (6.23)
We are in the sub-case A where t− s ≤ ǫ. Therefrom, we obtain the following:
‖ξ‖=1
≤ cǫq. (6.24)
Finally, we turn to bounding the absolute moments of B
1 . Hypothesis P1 assures us
dv Gs−r(y, v)Gt−r(x, v)
dv Gs−r(y, v)Gt−r(x, v)
dr Gt+s−2r(x, y),
thanks to the semi-group property Walsh [W86, (3.6)] (see (6.44) below). This and Lemma
7.1 together prove that
t− s+ 2u
− |x− y|
2(t− s+ 2u)
t− s+ 2u
2(t− s+ 2u)
since |x−y| ≥ 2(1+α)ǫ1/2 ≥ αǫ1/2. Now we can change variables [z := 2(t−s+2u)/(α2ǫ)],
and use the bounds 0 ≤ t− s ≤ ǫ to find that
∣ ≤ cǫ1/2Ψ(α), where Ψ(α) := α
∫ 6/α2
z−1/2e−1/z dz. (6.25)
Following exactly in the same way, we see that
∣ ≤ cǫ1/2Ψ(α). (6.26)
We can combine (6.22), (6.24) as follows:
‖ξ‖=1
≤ c(q)ǫq. (6.27)
On the other hand, we will see in Lemmas 6.13 and 6.14 below that
‖ξ‖=1
≤ c(q)ǫ3q/4. (6.28)
Now, by (6.20), (6.21), (6.25), (6.26), (6.27) and (6.28),
‖ξ‖=1
ξTγZξ
A1 +A2 +A3
−B(3)1 −B
2 +B3
≥ c1ǫ1/2 − c2Ψ(α)ǫ1/2 − Z1,ǫ,
where Z1,ǫ := B
1 + B
1 + B
2 + B
2 + B3 satisfies E[|Z1,ǫ|q] ≤ c1(q)ǫ3q/4. Because
limν→∞Ψ(ν) = 0, we can choose and fix α so large that c2Ψ(α) ≤ c1/4 for the c1 and c2 of
the preceding displayed equation. This yields,
‖ξ‖=1
ξTγZξ ≥ cǫ1/2 − Z1,ǫ, (6.29)
as in (6.14) and (6.15).
Sub-Case B. In this final (sub-) case we suppose that ǫ ≤ t− s ≤ |x − y|2. Choose and fix
0 < ǫ < t− s. During the course of our proof of Case 1, we established the following:
‖ξ‖=1
ξTγZξ ≥ min
+η − Y1,ǫ , cǫ1/2 − Y2,ǫ
where
E [|Y1,ǫ|q] ≤ c(q)ǫq and E [|Y2,ǫ|q] ≤ c(q)ǫq(
See (6.11) and (6.12). Consider this in conjunction with (6.14) to find that for all 0 < ǫ <
(1 + α)−2|x− y|2,
‖ξ‖=1
ξTγZξ ≥ min
+η − Y1,ǫ , cǫ1/2 − Y2,ǫ − Z1,ǫ1{t−s<ǫ}
Because of this and (6.15), Proposition 3.5 implies that
‖ξ‖=1
ξTγZξ
)−2pd
≤ c|x− y|2(−2dp)(
≤ c(|t− s|1/2 + |x− y|)−2dp(1+2η).
This concludes the proof of Proposition 6.9(a).
If t = s, then Sub–Case B does not arise, and so we get directly from (6.29) and
Proposition 3.5 that
‖ξ‖=1
ξTγZξ
)−2pd
≤ c|x− y|−2dp.
This proves (b) and concludes the proof of Proposition 6.9.
Remark 6.10. If σ and b are constant, then ai = 0, so η can be taken to be 0. This gives
the correct upper bound in the Gaussian case, which shows that the method of proof of
Proposition 6.9 is rather tight.
We finally prove three results that we have used in the proof of Proposition 6.9.
Lemma 6.11. Assume P1. For all T > 0 and q ≥ 1, there exists a constant c = c(q, T ) ∈
(0,∞) such that for every 0 < ǫ ≤ s ≤ t ≤ T and x ∈ [0, 1],
a2i (k, r, v, t, x)
≤ c(t− s+ ǫ)q/2ǫq/2.
Proof. Define
a2i (k, r, v, t, x).
Use (4.3) to write
E [|A|q] ≤ c (E [|A1|q] + E [|A2|q]) ,
where
A1 :=
i,j,k=1
Gt−θ(x, η)D
r,v (σij(u(θ, η))) W
j(dθ, dη)
A2 :=
i,k=1
dη Gt−θ(x, η)D
r,v (bi(u(θ, η)))
We bound the q-th moment of A1 and A2 separately.
As regards A1, we apply the Burkholder inequality for Hilbert-space-valued martingales
(Lemma 7.6) to find that
E [|A1|q] ≤ c
i,j,k=1
, (6.30)
where
Θ := 1{θ>r}Gt−θ(x, η)
D(k)r,v (σij(u(θ, η)))
≤ c1{θ>r}Gt−θ(x, η)
D(k)r,v (ul(θ, η))
thanks to hypothesis P1. Thus,
E [|A1|q] ≤ c
dη G2t−θ(x, η)
∫ s∧θ
D(k)r,v (ul(θ, η))
We apply Hölder’s inequality with respect to the measure G2t−θ(x, η) dθ dη to find that
E [|A1|q] ≤ c
dη G2t−θ(x, η)
dη G2t−θ(x, η)
∫ s∧θ
(6.31)
where Υ :=
r,v (ul(θ, η)). Lemma 7.3 assures us that
dη G2t−θ(x, η)
≤ c(t− s+ ǫ)(q−1)/2. (6.32)
On the other hand, Lemma 7.5 implies that
∫ s∧θ
≤ cǫq/2,
where c ∈ (0,∞) does not depend on (θ, η, s, t, ǫ, x). Consequently,
dη G2t−θ(x, η)
∫ s∧θ
≤ cǫq/2
dη G2t−θ(x, η)
≤ cǫq/2(t− s+ ǫ)1/2.
(6.33)
Equations (6.31), (6.32), and (6.33) together imply that
E [|A1|q] ≤ c(t− s+ ǫ)q/2ǫq/2. (6.34)
This is the desired bound for the q-th moment of A1. Next we derive a similar bound for
A2. This will finish the proof. By the Cauchy–Schwarz inequality
E [|A2|q] ≤ c(t− s+ ǫ)q
i,k=1
where Φ := Gt−θ(x, η)|D(k)r,v (bi(u(θ, η))) |. From here on, the q-th moment of A2 is estimated
as that of A1 was; cf. (6.30), and this yields E[|A2|q] ≤ c(t− s+ ǫ)3q/2ǫq/2. This completes
the proof.
Remark 6.12. It is possible to prove that E[|A1|] is at least a constant times (t−s+ǫ)1/2ǫ1/2.
In this sense, the preceding result is not improvable.
Lemma 6.13. Assume P1. Fix T > 0 and q ≥ 1. Then there exists c = c(q, T ) such that
for all x ∈ [0, 1], 0 ≤ s ≤ t ≤ T , and ǫ ∈ (0, 1),
µ∈Rd: ‖µ‖≤1
≤ cǫ3q/4.
Proof. Define
2 (k, i) :=
dv Gt−r(x, v) |ai(k, r, v, t, x)| .
Then, by the Cauchy–Schwarz inequality,
2 (k, i)
≤ J1J2, (6.35)
where
J1 :=
dv G2t−r(x, v)
J2 := E
dv a2i (k, r, v, t, x)
On one hand, according to Lemma 7.4,
J1 ≤ c
(t− s+ ǫ)q/4
. (6.36)
On the other hand, Lemma 6.11 assures us that
J2 ≤ c′(t− s+ ǫ)q/4ǫq/4. (6.37)
By combining (6.35), (6.36), and (6.37), we find that E[|B̂(1)2 (k, i)|q ] ≤ cǫ3q/4 for a constant
c ∈ (0,∞) that does not depend on ǫ. By hypothesis P1,
µ∈Rd: ‖µ‖≤1
2 (k, i)
≤ cǫ3q/4, (6.38)
as asserted.
Lemma 6.14. Assume P1. Fix T > 0 and q ≥ 1. Then there exists c = c(q, T ) such that
for any x ∈ [0, 1], 0 ≤ s ≤ t ≤ T , and ǫ ∈ (0, 1),
ξ=(λ,µ)∈R2d : ‖ξ‖=1
≤ cǫ3q/4.
Proof. Define
2 (k, i) :=
dv Gt−r(x, v) |ai(k, r, v, s, y)| .
Then, by the Cauchy–Schwarz inequality,
2 (k, i)
≤ J1J2,
where
J1 :=
dv G2t−r(x, v)
J2 := E
dv a2i (k, r, v, s, y)
According to (6.36),
J1 ≤ c
(t− s+ ǫ)q/4
≤ c′ǫq/4.
On the other hand, Lemma 6.11, with t = s, assures us that J2 ≤ c′′ǫq/2. It follows that the
q-th absolute moment of B̂
2 (k, i) is at most cǫ
3q/4. An appeal to the triangle inequality
finishes the proof; see (6.38) where a similar argument was worked out in detail.
6.4.2 Large Eigenvalues
Proposition 6.15. Assume P1 and P2. Fix T > 0 and p > 1. Then there exists C =
C(p, T ) such that for all 0 ≤ s < t ≤ T with t− s < 1
, x, y ∈ (0, 1), x 6= y,
(ξi)TγZξ
where ξ1, ..., ξd are the vectors from Lemma 6.8.
Proof. Let ξ1, . . . , ξd, written as in (6.10), be such that α1 ≥ α0, . . . , αd ≥ α0 for some
α0 > 0. In order to simplify the exposition, we assume that 0 < α = α1 = · · · = αd ≤ 1,
since the general case follows along the same lines. Let 0 < ǫ < s ≤ t. As in the proof of
Proposition 6.9, we note first that
i=1(ξ
i)TγZξ
i is bounded below by
αλ̃iGs−r(y, v)
+ µ̃i
1− α2 (Gt−r(x, v) −Gs−r(y, v))
σik(u(r, v))
+ αλ̃iai(k, r, v, s, y)
+ µ̃i
1− α2 (ai(k, r, v, t, x) − ai(k, r, v, s, y))
s∨(t−ǫ)
1− α2 Gt−r(x, v)σik(u(r, v))
+ µ̃i
1− α2 ai(k, r, v, t, x)
(6.39)
We intend to use Proposition 3.5 with ε0 > 0 fixed, so we seek lower bounds for this
expression for 0 < ε < ε0.
Case 1. t− s ≤ ǫ. Then, by (4.4), the expression in (6.39) is bounded below by
(f1(s, t, ǫ, α, λ̃, µ̃, x, y) + f2(s, t, ǫ, α, λ̃, µ̃, x, y))− 2Iǫ,
where, from hypothesis P2,
f1 ≥ cρ2
αλ̃Gs−r(y, v) +
1− α2 µ̃(Gt−r(x, v) −Gs−r(y, v))
, (6.40)
f2 ≥ cρ2
s∨(t−ǫ)
1− α2 Gt−r(x, v)
, (6.41)
and Iǫ = I1,ǫ + I2,ǫ + I3,ǫ, where
I1,ǫ :=
αλ̃i − µ̃i
1− α2
ai(k, r, v, s, y)
I2,ǫ :=
1− α2 ai(k, r, v, t, x)
I3,ǫ :=
1− α2 ai(k, r, v, t, x)
There are obvious similarities between the terms I1,ǫ and B
1 in (6.16). Thus, we apply
the same method that was used to bound E[|B(1)1 |q] to deduce that E[|I1,ǫ|q] ≤ c(q)ǫq. Since
I2,ǫ is similar to B
1 from (6.17) and t− s ≤ ǫ, we see using (6.24) that E[|I2,ǫ|q] ≤ c(q)ǫq.
Finally, using the similarity between I3,ǫ and B3 in (6.19), we see that E[|I3,ǫ|q] ≤ c(q)ǫq.
We claim that there exists α0 > 0, ǫ0 > 0 and c0 > 0 such that
f1 + f2 ≥ c0
ǫ for all α ∈ [α0, 1], ǫ ∈ (0, ǫ0], s, t ∈ [1, 2], x, y ∈ [0, 1]. (6.42)
This will imply in particular that for ǫ ≥ t− s,
i ≥ c0ǫ1/2 − 2Iǫ,
where E[|Iǫ|q] ≤ c(q)ǫq.
In order to prove (6.42), first define
pt(x, y) := (4πt)
−1/2e−(x−y)
2/(4t).
In addition, let g1(s, t, ǫ, α, λ̃, µ̃, x, y) and g2(s, t, ǫ, α, λ̃, µ̃, x, y) be defined by the same ex-
pressions as the right-hand sides of (6.40) and (6.41), but with Gs−r(x, v) replaced by
ps−r(x− v), and
replaced by
Observe that g1 ≥ 0, g2 ≥ 0, and if g1 = 0, then for all v ∈ Rd,
∥αps−r(y − v)λ̃+
1− α2 (pt−r(x− v)− ps−r(y − v))µ̃
∥ = 0. (6.43)
If, in addition, λ̃ = µ̃, then we get that for all v ∈ Rd,
1− α2
ps−r(y − v) +
1− α2pt−r(x− v) = 0.
We take Fourier transforms to deduce from this that for all ξ ∈ Rd,
1− α2
eiξy = −
1− α2eiξxe(s−t)ξ2 .
If x = y, then it follows that s = t and α −
1− α2 = −
1− α2. Hence, if α 6= 0, x = y
and λ̃ = µ̃, then g1 > 0. We shall make use of this observation shortly.
Because ‖λ̃‖ = ‖µ̃‖ = 1, f1 is bounded below by
α2G2s−r(y, v) +
1− α2
(Gt−r(x, v) −Gs−r(y, v))2
1− α2Gs−r(y, v)(Gt−r(x, v)−Gs−r(y, v))(λ̃ · µ̃)
= cρ2
1− α2
G2s−r(y, v)) +
1− α2
G2t−r(x, v)
1− α2
1− α2Gs−r(y, v)Gt−r(x, v)
1− α2Gs−r(y, v)(Gt−r(x, v)−Gs−r(y, v))(λ̃ · µ̃− 1)
Recall the semigroup property
dv Gs−r(y, v)Gt−r(x, v) = Gs+t−2r(x, y) (6.44)
(see Walsh [W86, (3.6)]). We set h := t− s and change variables [r̄ := s− r] to obtain the
following bound:
f1 ≥ cρ2
1− α2
G2r(y, y) +
1− α2
G2h+2r(x, x)
1− α2
1− α2Gh+2r(x, y)
1− α2(Gh+2r(x, y)−G2r(y, y))
λ̃ · µ̃− 1
Recall ([W86, p.318]), that
Gt(x, y) = pt(x, y) +Ht(x, y),
where Ht(x, y) is a continuous function that is uniformly bounded over (t, x, y) ∈ (0,∞)×
(0, 1) × (0, 1). Therefore, f1 ≥ cρ2g̃1 − cǫ, where
g̃1 := g̃1(h, ǫ, α, λ̃, µ̃, x, y)
1− α2
p2r(y, y) +
1− α2
p2h+2r(x, x)
1− α2
1− α2ph+2r(x, y)
1− α2 (ph+2r(x, y)− p2r(y, y))
λ̃ · µ̃− 1
We can recognize that
ph+2r(x, y)− p2r(y, y) =
exp(−(x− y)2/(4(h + 2r)))
4π(h + 2r)
4π(2r)
Also, λ̃ · µ̃− 1 ≤ 0. Thus,
g̃1 ≥ ĝ1,
where
ĝ1 := ĝ1(h, ǫ, α, x, y)
1− α2
p2r(y, y) +
1− α2
p2h+2r(x, x)
1− α2
1− α2ph+2r(x, y)
Therefore,
ĝ1 =
1− α2)2 1√
1− α2
8π(h + r)
+ 2(α −
1− α2)
1− α2ph+2r(x, y)
On the other hand, by (6.44) above,
∫ ǫ∧(t−s)
1− α2
G2r(y, y)
≥ g̃2 :=
∫ ǫ∧h
1− α2
p2r(y, y)− Cǫ
1− α2
ǫ ∧ h− Cǫ.
Finally, we conclude that
f1 + f2 ≥ ĝ1 + g̃2 − 2Cǫ
1− α2
1− α2√
h+ ǫ−
1− α2
1− α2
dr ph+2r(x, y)
1− α2√
ǫ ∧ h− 2Cǫ.
Now we consider two different cases.
Case (i). Suppose α−
1− α2 ≥ 0, that is, α ≥ 2−1/2. Then
ǫ−1/2 (ĝ1 + g̃2) ≥ φ1
− 2Cǫ1/2,
where
φ1(α , z) :=
1− α2
1− α2
1 + z +
1− α2
1 ∧ z
Clearly,
α≥2−1/2
φ1(α, z) ≥ inf
α>2−1/2
1− α2
1− α2
> φ0 > 0.
Thus,
α≥2−1/2, h≥0, 0<ǫ≤ǫ0
ǫ−1/2 (ĝ1 + g̃2) > 0.
Case (ii). Now we consider the case where α −
1− α2 < 0, that is, α < 2−1/2. In this
case,
ǫ−1/2 (ĝ1 + g̃2) ≥ ψ1
− 2Cǫ1/2,
where
ψ1(α , z) :=
1− α2
1− α2
1 + z +
1− α2 − α
1− α2
2 + z +
1− α2
1 ∧ z
Note that ψ1(α, z) > 0 if α 6= 0. This corresponds to the observation made in the lines
following (6.43). Moreover, for α ≥ α0 > 0, limz↓0 ψ1(α, z) ≥ (2π)−1/2α20, and
ψ1(α , z) ≥ inf
1− α2)2 +
1− α2
Therefore,
α∈[α0,2−1/2], z≥0
ψ1(α , z) > 0.
This concludes the proof of the claim (6.42).
Case 2. t− s > ǫ. In accord with (6.39), we are interested in
1≥α≥α0
i := min(E1,ǫ, E2,ǫ),
where
E1,ǫ := inf
α0≤α≤
E2,ǫ := inf√
1−ǫη≤α≤1
Clearly,
E1,ǫ ≥
f2 − 2I3,ǫ.
Since α ≤
1− ǫη is equivalent to
1− α2 ≥ ǫη/2, we use hypothesis P2 to deduce that
f2 ≥ cρ2ǫη
dv G2t−r(x, v) ≥ cρ2ǫ
Therefore,
E1,ǫ ≥ cρ2ǫ
+η − I3,ǫ,
and we have seen that I3,ǫ has the desirable property E [|I3,ǫ|q] ≤ c(q)ǫq.
In order to estimate E2,ǫ, we observe using (6.39) that
E2,ǫ ≥
f̃1 − J̃1,ǫ − J̃2,ǫ − J̃3,ǫ − J̃4,ǫ,
where
f̃1 ≥ α2
λ̃iσik(u(r, v))
G2s−r(y, v),
J̃1,ǫ = 2
1− α2
µ̃iσik(u(r, v))
G2t−r(x, v),
J̃2,ǫ = 2
1− α2
µ̃iσik(u(r, v))
G2s−r(y, v),
J̃3,ǫ = 2
αλ̃i − µ̃i
1− α2
ai(k, r, v, s, y)
J̃4,ǫ = 2
1− α2
µ̃iai(k, r, v, t, x)
Because α2 ≥ 1−ǫη and ǫ ≤ t−s ≤ 1
, hypothesis P2 and Lemma 7.2 imply that f̃1 ≥ cǫ1/2.
On the other hand, since 1−α2 ≤ ǫη, we can use hypothesis P1 and Lemma 7.4 to see that
J̃1,ǫ
≤ c(q)ǫqηǫq/2 = c(q)ǫ(
+η)q,
and similarly, using Lemma 7.3, E[|J̃2,ǫ|q] ≤ c(q)ǫ(
+η)q. The term J̃3,ǫ is equal to 2I1,ε, so
E[|J̃3,ǫ|q] ≤ cεq, and J̃4,ε is similar to B(2)1 from (6.17), so we find using (6.23) that
J̃4,ǫ
≤ cǫqη(t− s+ ǫ)q/2ǫq/2 ≤ cǫ(
+η)q.
We conclude that when t − s > ǫ, then E2,ǫ ≥ cǫ1/2 − J̃ǫ, where E[|J̃ǫ|q] ≤ c(q)ǫ(
+η)q.
Therefore, when t− s > ǫ,
1≥α≥α0
i ≥ min
+η − I3,ǫ , cǫ
2 − J̃ǫ
Putting together the results of Case 1 and Case 2, we see that for 0 < ǫ ≤ 1
‖ξ‖=1, 1≥α≥α0
i ≥ min
+η − I3,ǫ, cǫ
2 − 2Iǫ1{ǫ≥t−s} − J̃ǫ1{ǫ<t−s}
We take into account the bounds on moments of I3,ǫ, Iǫ and J̃ǫ, and then use Proposition
3.5 to conclude the proof of Proposition 6.15.
7 Appendix
On several occasions, we have appealed to the following technical estimates on the Green
kernel of the heat equation.
Lemma 7.1. [BP98, (A.1)] There exists C > 0 such that for any 0 < s < t and x, y ∈ [0, 1],
x 6= y,
Gt−s(x, y) ≤ C
2π(t− s)
−|x− y|
2(t− s)
Lemma 7.2. [BP98, (A.3)] There exists C > 0 such that for any t ≥ ǫ > 0 and x ∈ [0, 1],
G2t−s(x, y)dyds ≥ C
Lemma 7.3. [BP98, (A.5)] There exists C > 0 such that for any ǫ > 0, q < 3
, t ≥ ǫ and
x ∈ [0, 1],
t−s(x, y)dyds ≤ Cǫ3/2−q.
Lemma 7.4. There exists C > 0 such that for all 0 < a < b and x ∈ [0, 1],
G2s(x, y) dyds ≤ C
b− a√
Proof. Using Lemma 7.1 and the change of variables z =
, we see that
G2s(x, y) dyds ≤ C
ds = 2C̃(
which concludes the proof.
The next result is a straightforward extension to d ≥ 1 of Morien [M98, Lemma 4.2]
for d = 1.
Lemma 7.5. Assume P1. For all q ≥ 1, T > 0 there exists C > 0 such that for all
T ≥ t ≥ s ≥ ǫ > 0 and 0 ≤ y ≤ 1,
D(k)r,v (ui(t, y))
 ≤ Cǫq/2.
The next result is Burkholder’s inequality for Hilbert-space-valued martingales.
Lemma 7.6. [BP98, eq.(4.18)] Let Hs,t be a predictable L
2(([0, t]× [0, 1])m)-valued process,
m ≥ 1. Then, for any p > 1, there exists C > 0 such that
([0,t]×[0,1])m
Hs,y(α)W (dy, ds)
([0,t]×[0,1])m
H2s,y(α)dα
Acknowledgement. The authors thank V. Bally for several stimulating discussions.
References
[BMS95] Bally, V., Millet, A. and Sanz-Solé, M. (1995), Approximation and sup-
port theorem in Hölder norm for parabolic stochastic partial differential equations,
The Annals of Probability, 23, 178-222.
[BP98] Bally, V. and Pardoux, E. (1998), Malliavin calculus for white noise driven
parabolic SPDEs, Potential Analysis, 9, 27-64.
[DN04] Dalang, R.C. and Nualart, E. (2004), Potential theory for hyperbolic SPDEs,
The Annals of Probability, 32, 2099-2148.
[DKN07] Dalang, R.C., Khoshnevisan, D. and Nualart, E. (2007), Hitting prob-
abilities for systems of non-linear stochastic heat equations with additive noise,
Submitted for publication. See http://arxiv.org/pdf/math.PR/0702710.
[K85] Kahane, J.-P. (1985), Some random series of functions, Cambridge University
Press.
[K02] Khoshnevisan, D. (2002), Multiparameter processes. An introduction to random
fields, Springer-Verlag.
[K03] Kohatsu-Higa, A. (2003), Lower bound estimates for densities of uniformly
elliptic random variables on Wiener space, Probab. Theory Related Fields, 126,
421-457.
[M98] Morien, P.-L. (1998), The Hölder and the Besov regularity of the density for
the solution of a parabolic stochastic partial differential equation, Bernouilli, 5,
275-298.
http://arxiv.org/pdf/math.PR/0702710
[MT03] Mueller, C. and Tribe, R. (2002), Hitting properties of the random string,
Electronic Journal of Probability, 7, 1-29.
[N94] Nualart, D. and Pardoux, E. (1994), Markov field properties of solutions
of white noise driven quasi-linear parabolic PDEs, Stochastics and Stochastics
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[N95] Nualart, D. (1995), The Malliavin calculus and related topics, Springer-Verlag.
[N98] Nualart, D. (1998), Analysis on Wiener space and anticipating stochastic calcu-
lus, Ecole d’Eté de Probabilités de Saint-Flour XXV, Lect. Notes in Math. 1690,
Springer-Verlag, 123-227.
[S05] Sanz-Solé, M. (2005), Malliavin calculus with applications to stochastic partial
differential equations, EPFL Press.
[W86] Walsh, J.B. (1986), An Introduction to Stochastic Partial Differential Equa-
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Springer-Verlag, 266-437.
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avin Calculus, Tata Institute of Fundamental Research Lectures on Math. and
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	Introduction and main results
	Proof of Theorems 1.2, 1.6 and their corollaries (assuming Theorem 1.1)
	Elements of Malliavin calculus
	Existence, smoothness and uniform boundedness of the one-point density
	The Gaussian-type lower bound on the one-point density
	The Gaussian-type upper bound on the two-point density
	Bounds on the increments of the Malliavin derivatives
	Study of the Malliavin matrix
	Proof of Theorem 1.1(c) and (d)
	Proof of Theorem 6.3
	Small Eigenvalues
	Large Eigenvalues
	Appendix

  We consider a system of d non-linear stochastic heat equations in spatial
dimension 1 driven by d-dimensional space-time white noise. The non-linearities
appear both as additive drift terms and as multipliers of the noise. Using
techniques of Malliavin calculus, we establish upper and lower bounds on the
one-point density of the solution u(t,x), and upper bounds of Gaussian-type on
the two-point density of (u(s,y),u(t,x)). In particular, this estimate
quantifies how this density degenerates as (s,y) converges to (t,x). From these
results, we deduce upper and lower bounds on hitting probabilities of the
process {u(t,x)}_{t \in \mathbb{R}_+, x \in [0,1]}, in terms of respectively
Hausdorff measure and Newtonian capacity. These estimates make it possible to
show that points are polar when d >6 and are not polar when d<6. We also show
that the Hausdorff dimension of the range of the process is 6 when d>6, and
give analogous results for the processes t \mapsto u(t,x) and x \mapsto u(t,x).
Finally, we obtain the values of the Hausdorff dimensions of the level sets of
these processes.

Introduction and main results
Consider the following system of non-linear stochastic partial differential equations (spde’s)
(t , x) =
(t , x) +
σi,j(u(t , x))Ẇ
j(t , x) + bi(u(t , x)), (1.1)
for 1 ≤ i ≤ d, t ∈ [0 , T ], and x ∈ [0 , 1], where u := (u1 , . . . , ud), with initial conditions
u(0 , x) = 0 for all x ∈ [0 , 1], and Neumann boundary conditions
(t , 0) =
(t , 1) = 0, 0 ≤ t ≤ T. (1.2)
Here, Ẇ := (Ẇ 1 , . . . , Ẇ d) is a vector of d independent space-time white noises on [0 , T ]×
[0 , 1]. For all 1 ≤ i, j ≤ d, bi, σij : Rd → R are globally Lipschitz functions. We set b = (bi),
σ = (σij). Equation (1.1) is formal: the rigorous formulation of Walsh [W86] will be
recalled in Section 2.
The objective of this paper is to develop a potential theory for the Rd-valued process
u = (u(t, x), t ≥ 0, x ∈ (0, 1)). In particular, given A ⊂ Rd, we want to determine whether
the process u visits (or hits) A with positive probability.
The only potential-theoretic result that we are aware of for systems of non-linear spde’s
with multiplicative noise (σ non-constant) is Dalang and Nualart [DN04], who study
the case of the reduced hyperbolic spde on R2+ (essentially equivalent to the wave equation
in spatial dimension 1):
∂2Xit
∂t1∂t2
σi,j(Xt)
∂t1∂t2
+ bi(Xt),
where t = (t1 , t2) ∈ R2+, and Xit = 0 if t1t2 = 0, for all 1 ≤ i ≤ d. There, Dalang and
Nualart used Malliavin calculus to show that the solution (Xt) of this spde satisfies
K−1Capd−4(A) ≤ P{∃t ∈ [a, b]2 : Xt ∈ A} ≤ KCapd−4(A),
where Capβ denotes the capacity with respect to the Newtonian β-kernel Kβ(·) (see (1.6)).
This result, particularly the upper bound, relies heavily on properties of the underlying
two-parameter filtration and uses Cairoli’s maximal inequality for two-parameter processes.
Hitting probabilities for systems of linear heat equations have been obtained inMueller
and Tribe [MT03]. For systems of non-linear stochastic heat equations with additive noise,
that is, σ in (1.1) is a constant matrix, so (1.1) becomes
(t , x) =
(t , x) +
σi,j Ẇ
j(t , x) + bi(u(t , x)), (1.3)
estimates on hitting probabilities have been obtained in Dalang, Khoshnevisan and
Nualart [DKN07]. That paper develops some general results that lead to upper and lower
bounds on hitting probabilities for continuous two-parameter random fields, and then uses
these, together with a careful analysis of the linear equation (b ≡ 0, σ ≡ Id, where Id
denotes the d × d identity matrix) and Girsanov’s theorem, to deduce bounds on hitting
probabilities for the solution to (1.3).
In this paper, we make use of the general results of [DKN07], but then, in order to handle
the solution of (1.1), we use a very different approach. Indeed, the results of [DKN07] require
in particular information about the probability density function pt,x of the random vector
u(t, x). In the case of multiplicative noise, estimates on pt,x can be obtained via Malliavin
calculus.
We refer in particular on the results of Bally and Pardoux [BP98], who used Malli-
avin calculus in the case d = 1 to prove that for any t > 0, k ∈ N and 0 ≤ x1 < · · · < xk ≤ 1,
the law of (u(t , x1), . . . , u(t , xk)) is absolutely continuous with respect to Lebesgue measure,
with a smooth and strictly positive density on {σ 6= 0}k, provided σ and b are infinitely
differentiable functions which are bounded together with their derivatives of all orders. A
Gaussian-type lower bound for this density is established by Kohatsu-Higa [K03] under
a uniform ellipticity condition. Morien [M98] showed that the density function is also
Hölder-continuous as a function of (t , x).
In this paper, we shall use techniques of Malliavin calculus to establish the following
theorem. Let pt,x(z) denote the probability density function of the R
d-valued random vector
u(t , x) = (u1(t , x), . . . , ud(t , x)) and for (s, y) 6= (t, x), let ps,y; t,x(z1, z2) denote the joint
density of the R2d-valued random vector
(u(s , y), u(t , x)) = (u1(s , y), . . . , ud(s , y), u1(t , x), . . . , ud(t , x)) (1.4)
(the existence of pt,x(·) is essentially a consequence of the result of Bally and Pardoux
[BP98], see our Corollary 4.3; the existence of ps,y; t,x(·, ·) is a consequence of Theorems 3.1
and 6.3).
Consider the following two hypotheses on the coefficients of the system (1.1):
P1 The functions σij and bi are bounded and infinitely differentiable with bounded partial
derivatives of all orders, for 1 ≤ i, j ≤ d.
P2 The matrix σ is uniformly elliptic, that is, ‖σ(x)ξ‖2 ≥ ρ2 > 0 for some ρ > 0, for all
x ∈ Rd, ξ ∈ Rd, ‖ξ‖ = 1 (‖ · ‖ denotes the Euclidean norm on Rd).
Theorem 1.1. Assume P1 and P2. Fix T > 0 and let I ⊂ (0, T ] and J ⊂ (0, 1) be two
compact nonrandom intervals.
(a) The density pt,x(z) is uniformly bounded over z ∈ Rd, t ∈ I and x ∈ J .
(b) There exists c > 0 such that for any t ∈ I, x ∈ J and z ∈ Rd,
pt,x(z) ≥ ct−d/4 exp
ct1/2
(c) For all η > 0, there exists c > 0 such that for any s, t ∈ I, x, y ∈ J , (s, y) 6= (t, x) and
z1, z2 ∈ Rd,
ps,y; t,x(z1, z2) ≤ c(|t− s|1/2 + |x− y|)−(d+η)/2 exp
− ‖z1 − z2‖
c(|t− s|1/2 + |x− y|)
. (1.5)
(d) There exists c > 0 such that for any t ∈ I, x, y ∈ J , x 6= y and z1, z2 ∈ Rd,
pt,y; t,x(z1, z2) ≤ c(|x− y|)−d/2 exp
−‖z1 − z2‖
c|x− y|
The main technical effort in this paper is to obtain the upper bound in (c). Indeed,
it is not difficult to check that for fixed (s, y; t, x), (z1, z2) 7→ ps,y; t,x(z1, z2) behaves like
a Gaussian density function. However, for (s, y) = (t, x), the R2d-valued random vector
(u(s , y), u(t , x)) is concentrated on a d-dimensional subspace in R2d and therefore does not
have a density with respect to Lebesgue measure in R2d. So the main effort is to estimate
how this density blows up as (s, y) → (t, x). This is achieved by a detailed analysis of
the behavior of the Malliavin matrix of (u(s , y), u(t , x)) as a function of (s, y; t, x), using a
perturbation argument. The presence of η in statement (c) may be due to the method of
proof. When t = s, it is possible to set η = 0 as in Theorem 1.1(d).
This paper is organized as follows. After introducing some notation and stating our main
results on hitting probabilities (Theorems 1.2 and 1.6), we assume Theorem 1.1 and use the
theorems of [DKN07] to prove these results in Section 2. In Section 3, we recall some basic
facts of Malliavin calculus and state and prove two results that are tailored to our needs
(Propositions 3.4 and 3.5). In Section 4, we establish the existence, smoothness and uniform
boundedness of the one-point density function pt,x, proving Theorem 1.1(a). In Section 5,
we establish a lower bound on pt,x, which proves Theorem 1.1(b). This upper (respectively
lower) bound is a fairly direct extension to d ≥ 1 of a result of Bally and Pardoux [BP98]
(respectively Kohatsu-Higa [K03]) when d = 1. In Section 6, we establish Theorem 1.1(c)
and (d). The main steps are as follows.
The upper bound on the two-point density function ps,y; t,x involves a bloc-decomposition
of the Malliavin matrix of the R2d-valued random vector (u(s, y), u(s, y) − u(t, x)). The
entries of this matrix are of different orders of magnitude, depending on which bloc they
are in: see Theorem 6.3. Assuming Theorem 6.3, we prove Theorem 1.1(c) and (d) in
Section 6.3. The exponential factor in (1.5) is obtained from an exponential martingale
inequality, while the factor (|t − s|1/2 + |x − y|)−(d+η)/2 comes from an estimate of the
iterated Skorohod integrals that appear in Corollary 3.3 and from the block structure of
the Malliavin matrix.
The proof of Theorem 6.3 is presented in Section 6.4: this is the main technical effort in
this paper. We need bounds on the inverse of the Malliavin matrix. Bounds on its cofactors
are given in Proposition 6.5, while bounds on negative moments of its determinant are given
in Proposition 6.6. The determinant is equal to the product of the 2d eigenvalues of the
Malliavin matrix. It turns out that at least d of these eigenvalues are of order 1 (“large
eigenvalues”) and do not contribute to the upper bound in (1.5), and at most d are of the
same order as the smallest eigenvalue (“small eigenvalues”), that is, of order |t−s|1/2+|x−y|.
If we did not distinguish between these two types of eigenvalues, but estimated all of them
by the smallest eigenvalue, we would obtain a factor of (|t− s|1/2 + |x− y|)−d+η/2 in (1.5),
which would not be the correct order. The estimates on the smallest eigenvalue are obtained
by refining a technique that appears in [BP98]; indeed, we obtain a precise estimate on the
density whereas they only showed existence. The study of the large eigenvalues does not
seem to appear elsewhere in the literature.
Coming back to potential theory, let us introduce some notation. For all Borel sets
F ⊂ Rd, we define P(F ) to be the set of all probability measures with compact support
contained in F . For all integers k ≥ 1 and µ ∈ P(Rk), we let Iβ(µ) denote the β-dimensional
energy of µ, that is,
Iβ(µ) :=
Kβ(‖x− y‖)µ(dx)µ(dy),
where ‖x‖ denotes the Euclidian norm of x ∈ Rk,
Kβ(r) :=
r−β if β > 0,
log(N0/r) if β = 0,
1 if β < 0,
(1.6)
and N0 is a sufficiently large constant (see Dalang, Khoshnevisan, and Nualart
[DKN07, (1.5)].
For all β ∈ R, integers k ≥ 1, and Borel sets F ⊂ Rk, Capβ(F ) denotes the β-dimensional
capacity of F , that is,
Capβ(F ) :=
µ∈P(F )
Iβ(µ)
where 1/∞ := 0. Note that if β < 0, then Capβ(·) ≡ 1.
Given β ≥ 0, the β-dimensional Hausdorff measure of F is defined by
Hβ(F ) = lim
(2ri)
β : F ⊆
B(xi , ri), sup
ri ≤ ǫ
, (1.7)
where B(x , r) denotes the open (Euclidean) ball of radius r > 0 centered at x ∈ Rd. When
β < 0, we define Hβ(F ) to be infinite.
Throughout, we consider the following parabolic metric: For all s, t ∈ [0 , T ] and x, y ∈
[0 , 1],
∆((t , x) ; (s , y)) := |t− s|1/2 + |x− y|. (1.8)
Clearly, this is a metric on R2 which generates the usual Euclidean topology on R2. Then
we obtain an energy form
I∆β (µ) :=
Kβ(∆((t , x) ; (s , y)))µ(dt dx)µ(ds dy),
and a corresponding capacity
Cap∆β (F ) :=
µ∈P(F )
I∆β (µ)
For the Hausdorff measure, we write
β (F ) = lim
(2ri)
β : F ⊆
B∆((ti , xi) , ri), sup
ri ≤ ǫ
where B∆((t , x) , r) denotes the open ∆-ball of radius r > 0 centered at (t , x) ∈ [0 , T ] ×
[0 , 1].
Using Theorem 1.1 together with results fromDalang, Khoshnevisan, and Nualart
[DKN07], we shall prove the following result. Let u(E) denote the (random) range of E
under the map (t, x) 7→ u(t, x), where E is some Borel-measurable subset of R2.
Theorem 1.2. Assume P1 and P2. Fix T > 0, M > 0, and η > 0. Let I ⊂ (0, T ] and
J ⊂ (0, 1) be two fixed non-trivial compact intervals.
(a) There exists c > 0 depending on M, I, J and η such that for all compact sets A ⊆
[−M,M ]d,
c−1 Capd−6+η(A) ≤ P{u(I × J) ∩A 6= ∅} ≤ cHd−6−η(A).
(b) For all t ∈ (0, T ], there exists c1 > 0 depending on T , M and J , and c2 > 0 depending
on T , M , J and η > 0 such that for all compact sets A ⊆ [−M,M ]d,
c1 Capd−2(A) ≤ P{u({t} × J) ∩A 6= ∅} ≤ c2 Hd−2−η(A).
(c) For all x ∈ (0, 1), there exists c > 0 depending on M, I and η such that for all compact
sets A ⊆ [−M,M ]d,
c−1 Capd−4+η(A) ≤ P{u(I × {x}) ∩A 6= ∅} ≤ cHd−4−η(A).
Remark 1.3. (i) Because of the inequalities between capacity and Hausdorff measure,
the right-hand sides of Theorem 1.2 can be replaced by cCapd−6−η(A), cCapd−2−η(A)
and cCapd−4−η(A) in (a), (b) and (c), respectively (cf. Kahane [K85, p. 133]).
(ii) Theorem 1.2 also holds if we consider Dirichlet boundary conditions (i.e. ui(t, 0) =
ui(t, 1) = 0, for t ∈ [0, T ]) instead of Neumann boundary conditions.
(iii) In the upper bounds of Theorem 1.2, the condition in P1 that σ and b are bounded
can be removed, but their derivatives of all orders must exist and be bounded.
As a consequence of Theorem 1.2, we deduce the following result on the polarity of
points. Recall that a Borel set A ⊆ Rd is called polar for u if P{u((0, T ] × (0, 1)) ∩ A 6=
∅} = 0; otherwise, A is called nonpolar.
Corollary 1.4. Assume P1 and P2.
(a) Singletons are nonpolar for (t, x) 7→ u(t, x) when d ≤ 5, and are polar when d ≥ 7
(the case d = 6 is open).
(b) Fix t ∈ (0, T ]. Singletons are nonpolar for x 7→ u(t, x) when d = 1, and are polar
when d ≥ 3 (the case d = 2 is open).
(c) Fix x ∈ (0, 1). Singletons are not polar for t 7→ u(t, x) when d ≤ 3 and are polar when
d ≥ 5 (the case d = 4 is open).
Another consequence of Theorem 1.2 is the Hausdorff dimension of the range of the
process u.
Corollary 1.5. Assume P1 and P2.
(a) If d > 6, then dim
(u((0, T ] × (0, 1))) = 6 a.s.
(b) Fix t ∈ R+. If d > 2, then dimH(u({t} × (0, 1))) = 2 a.s.
(c) Fix x ∈ (0, 1). If d > 4, then dim
(u(R+ × {x})) = 4 a.s.
As in Dalang, Khoshnevisan, and Nualart [DKN07], it is also possible to use
Theorem 1.1 to obtain results concerning level sets of u. Define
L (z ;u) := {(t , x) ∈ I × J : u(t , x) = z} ,
T (z ;u) = {t ∈ I : u(t , x) = z for some x ∈ J} ,
X (z ;u) = {x ∈ J : u(t , x) = z for some t ∈ I} ,
Lx(z ;u) := {t ∈ I : u(t , x) = z} ,
t(z ;u) := {x ∈ J : u(t , x) = z} .
We note that L (z ;u) is the level set of u at level z, T (z ;u) (resp. X (z ;u)) is the projection
of L (z ;u) onto I (resp. J), and Lx(z ;u) (resp. L
t(z ;u)) is the x-section (resp. t-section)
of L (z ;u).
Theorem 1.6. Assume P1 and P2. Then for all η > 0 and R > 0 there exists a positive
and finite constant c such that the following holds for all compact sets E ⊂ (0, T ] × (0, 1),
F ⊂ (0, T ], G ⊂ (0, 1), and for all z ∈ B(0 , R):
(a) c−1 Cap∆(d+η)/2(E) ≤ P{L (z ;u) ∩E 6= ∅} ≤ cH ∆(d−η)/2(E);
(b) c−1Cap(d−2+η)/4(F ) ≤ P{T (z ;u) ∩ F 6= ∅} ≤ cH(d−2−η)/4(F );
(c) c−1 Cap(d−4+η)/2(G) ≤ P{X (z ;u) ∩G 6= ∅} ≤ cH(d−4−η)/2(G);
(d) for all x ∈ (0, 1), c−1 Cap(d+η)/4(F ) ≤ P{Lx(z ;u) ∩ F 6= ∅} ≤ cH(d−η)/4(F );
(e) for all t ∈ (0, T ], c−1 Capd/2(G) ≤ P{L t(z ;u) ∩G 6= ∅} ≤ cH(d−η)/2(G).
Corollary 1.7. Assume P1 and P2. Choose and fix z ∈ Rd.
(a) If 2 < d < 6, then dim
T (z ;u) = 1
(6− d) a.s. on {T (z ;u) 6= ∅}.
(b) If 4 < d < 6 (i.e. d = 5), then dim
X (z ;u) = 1
(6− d) a.s. on {X (z ;u) 6= ∅}.
(c) If 1 ≤ d < 4, then dim
Lx(z ;u) =
(4− d) a.s. on {Lx(z ;u) 6= ∅}.
(d) If d = 1, then dim
t(z ;u) = 1
(2− d) = 1
a.s. on {L t(z ;u) 6= ∅}.
In addition, all four right-most events have positive probability.
Remark 1.8. The results of the two theorems and corollaries above should be compared
with those of Dalang, Khsohnevisan and Nualart [DKN07].
2 Proof of Theorems 1.2, 1.6 and their corollaries (assuming
Theorem 1.1)
We first recall that equation (1.1) is formal: a rigorous formulation, following Walsh [W86],
is as follows. Let W i = (W i(s, x))s∈R+, x∈[0,1], i = 1, ..., d, be independent Brownian sheets
defined on a probability space (Ω,F ,P), and set W = (W 1, ...,W d). For t ≥ 0, let Ft =
σ{W (s, x), s ∈ [0, t], x ∈ [0, 1]}. We say that a process u = {u(t, x), t ∈ [0, T ], x ∈ [0, 1]}
is adapted to (Ft) if u(t, x) is Ft-measurable for each (t, x) ∈ [0, T ]× [0, 1]. We say that u
is a solution of (1.1) if u is adapted to (Ft) and if for i ∈ {1, . . . , d},
ui(t, x) =
Gt−r(x , v)
σi,j(u(r , v))W
j(dr , dv)
Gt−r(x , v) bi(u(r , v)) drdv,
(2.1)
where Gt(x , y) denotes the Green kernel for the heat equation with Neumann boundary
conditions (see Walsh [W86, Chap 3]), and the stochastic integral in (2.1) is interpreted
as in [W86].
Adapting the results from [W86] to the case d ≥ 1, one can show that there exists a
unique continuous process u = {u(t, x), t ∈ [0, T ], x ∈ [0, 1]} adapted to (Ft) that is a
solution of (1.1). Moreover, it is shown in Bally, Millet, and Sanz-Solé [BMS95] that
for any s, t ∈ [0, T ] with s ≤ t, x, y ∈ [0, 1], and p > 1,
E[|u(t, x)− u(s, y)|p] ≤ CT,p(∆((t , x) ; (s , y)))p/2 , (2.2)
where ∆ is the parabolic metric defined in (1.8). In particular, for any 0 < α < 1/2, u is
a.s. α-Hölder continuous in x and α/2-Hölder continuous in t.
Assuming Theorem 1.1, we now prove Theorems 1.2, 1.6 and their corollaries.
Proof of Theorem 1.2. (a) In order to prove the upper bound we use Dalang, Khosh-
nevisan, and Nualart [DKN07, Theorem 3.3]. Indeed, Theorem 1.1(a) and (2.2) imply
that the hypotheses (i) and (ii), respectively, of this theorem, are satisfied, and so the
conclusion (with β = d− η) is too.
In order to prove the lower bound, we shall use of [DKN07, Theorem 2.1]. This requires
checking hypotheses A1 and A2 in that paper. Hypothesis A1 is a lower bound on the
one-point density function pt,x(z), which is an immediate consquence of Theorem 1.1(b).
Hypothesis A2 is an upper bound on the two-point density function ps,y;t,x(z1, z2), which
involves a parameter β; we take β = d + η. In this case, Hypothesis A2 is an immediate
consequence of Theorem 1.1(c). Therefore, the lower bound in Theorem 1.2(a) follows from
[DKN07, Theorem 2.1]. This proves (a).
(b) For the upper bound, we again refer to [DKN07, Theorem 3.3] (see also [DKN07,
Theorem 3.1]). For the lower bound, which involves Capd−2(A) instead of Capd−2+η(A), we
refer to [DKN07, Remark 2.5] and observe that hypotheses A1t and A2t there are satisfied
with β = d (by Theorem 1.1(d)). This proves (b).
(c) As in (a), the upper bound follows from [DKN07, Theorem 3.3] with β = d − η
(see also [DKN07, Theorem 3.1(3)]), and the lower bound follows from [DKN07, Theorem
2.1(3)], with β = d+ η. Theorem 1.2 is proved.
Proof of Corollary 1.4. We first prove (a). Let z ∈ Rd. If d ≤ 5, then there is η > 0 such
that d − 6 + η < 0, and thus Capd−6+η({z}) = 1. Hence, the lower bound of Theorem
1.2 (a) implies that {z} is not polar. On the other hand, if d > 6, then for small η > 0,
d− 6− η > 0. Therefore, Hd−6−η({z}) = 0 and the upper bound of Theorem 1.2(a) implies
that {z} is polar. This proves (a). One proves (b) and (c) exactly along the same lines
using Theorem 1.2(b) and (c).
Proof of Theorem 1.6. For the upper bounds in (a)-(e), we use Dalang, Khoshnevisan,
and Nualart [DKN07, Theorem 3.3] whose assumptions we verified above with β = d−η;
these upper bounds then follow immediately from [DKN07, Theorem 3.2].
For the lower bounds in (a)-(d), we use [DKN07, Theorem 2.4] since we have shown
above that the assumptions of this theorem, with β = d+ η, are satisfied by Theorem 1.1.
For the lower bound in (e), we refer to [DKN07, Remark 2.5] and note that by Theorem
1.1(d), Hypothesis A2t there is satisfied with β = d. This proves Theorem 1.6.
Proof of Corollaries 1.5 and 1.7. The final positive-probability assertion in Corollary 1.7
is an immediate consequence of Theorem 1.6 and Taylor’s theorem Khoshnevisan [K02,
Corollary 2.3.1 p. 523].
Let E be a random set. When it exists, the codimension of E is the real number
β ∈ [0 , d] such that for all compact sets A ⊂ Rd,
P{E ∩A 6= ∅}
> 0 whenever dim
(A) > β,
= 0 whenever dim
(A) < β.
See Khoshnevisan [K02, Chap.11, Section 4]. When it is well defined, we write the said
codimension as codim(E). Theorems 1.2 and 1.6 imply that for d ≥ 1: codim(u(R+ ×
(0, 1))) = (d − 6)+; codim(u({t} × (0, 1))) = (d − 2)+; codim(u(R+ × {x})) = (d − 4)+;
codim(T (z)) = (d−2
)+; codim(X (z)) = (d−4
)+; codim(Lx(z)) =
; and codim(L t(z)) =
. According to Theorem 4.7.1 of Khoshnevisan [K02, Chapter 11], given a random set
E in Rn whose codimension is strictly between 0 and n,
E + codim E = n a.s. on {E 6= ∅}. (2.3)
This implies the statements of Corollaries 1.5 and 1.7.
3 Elements of Malliavin calculus
In this section, we introduce, following Nualart [N95] (see also Sanz-Solé [S05]), some
elements of Malliavin calculus. Let S denote the class of smooth random variables of the
F = f(W (h1), ...,W (hn)),
where n ≥ 1, f ∈ C∞P (Rn), the set of real-valued functions f such that f and all its partial
derivatives have at most polynomial growth, hi ∈ H := L2([0, T ] × [0, 1],Rd), and W (hi)
denotes the Wiener integral
W (hi) =
hi(t, x) ·W (dx, dt), 1 ≤ i ≤ n.
Given F ∈ S , its derivative is defined to be the Rd-valued stochastic process DF =
(Dt,xF = (D
t,xF, ...,D
t,xF ), (t, x) ∈ [0, T ]× [0, 1]) given by
Dt,xF =
(W (h1), ...,W (hn))hi(t, x).
More generally, we can define the derivative DkF of order k of F by setting
DkαF =
i1,...,ik=1
· · · ∂
f(W (h1), ...,W (hn))hi1(α1)⊗ · · · ⊗ hik(αk),
where α = (α1, ..., αk), and αi = (ti, xi), 1 ≤ i ≤ k.
For p, k ≥ 1, the space Dk,p is the closure of S with respect to the seminorm ‖ · ‖p
defined by
= E[|F |p] +
E[‖DjF‖p
where
‖DjF‖2
i1,...,ij=1
dx1 · · ·
(t1,x1)
· · ·D(ij)
(tj ,xj)
We set (D∞)d = ∩p≥1 ∩k≥1 Dk,p.
The derivative operator D on L2(Ω) has an adjoint, termed the Skorohod integral and
denoted by δ, which is an unbounded operator on L2(Ω,H ). Its domain, denoted by
Dom δ, is the set of elements u ∈ L2(Ω,H ) such that there exists a constant c such that
|E[〈DF, u〉H ]| ≤ c‖F‖0,2, for any F ∈ D1,2.
If u ∈ Dom δ, then δ(u) is the element of L2(Ω) characterized by the following duality
relation:
E[Fδ(u)] = E
t,xF uj(t, x) dtdx
, for all F ∈ D1,2.
A first application of Malliavin calculus to the study of probability laws is the following
global criterion for smoothness of densities.
Theorem 3.1. [N95, Thm.2.1.2 and Cor.2.1.2] or [S05, Thm.5.2] Let F = (F 1, ..., F d) be
an Rd-valued random vector satisfying the following two conditions:
(i) F ∈ (D∞)d;
(ii) the Malliavin matrix of F defined by γF = (〈DF i,DF j〉H )1≤i,j≤d is invertible a.s.
and (det γF )
−1 ∈ Lp(Ω) for all p ≥ 1.
Then the probability law of F has an infinitely differentiable density function.
A random vector F that satisfies conditions (i) and (ii) of Theorem 3.1 is said to be
nondegenerate. For a nondegenerate random vector, the following integration by parts
formula plays a key role.
Proposition 3.2. [N98, Prop.3.2.1] or [S05, Prop.5.4] Let F = (F 1, ..., F d) ∈ (D∞)d be a
nondegenerate random vector, let G ∈ D∞ and let g ∈ C∞P (Rd). Fix k ≥ 1. Then for any
multi-index α = (α1, ..., αk) ∈ {1, . . . , d}k, there is an element Hα(F,G) ∈ D∞ such that
E[(∂αg)(F )G] = E[g(F )Hα(F,G)].
In fact, the random variables Hα(F,G) are recursively given by
Hα(F,G) = H(αk)(F,H(α1,...,αk−1)(F,G)),
H(i)(F,G) =
δ(G (γ−1F )i,j DF
Proposition 3.2 with G = 1 and α = (1, ..., d) implies the following expression for the
density of a nondegenerate random vector.
Corollary 3.3. [N98, Corollary 3.2.1] Let F = (F 1, ..., F d) ∈ (D∞)d be a nondegenerate
random vector and let pF (z) denote the density of F . Then for every subset σ of the set of
indices {1, ..., d},
pF (z) = (−1)d−|σ|E[1{F i>zi,i∈σ, F i<zi,i 6∈σ}H(1,...,d)(F, 1)],
where |σ| is the cardinality of σ, and, in agreement with Proposition 3.2,
H(1,...,d)(F, 1) = δ((γ
F DF )
dδ((γ−1F DF )
d−1δ(· · · δ((γ−1F DF )
1) · · · ))).
The next result gives a criterion for uniform boundedness of the density of a nondegen-
erate random vector.
Proposition 3.4. For all p > 1 and ℓ ≥ 1, let c1 = c1(p) > 0 and c2 = c2(ℓ, p) ≥ 0 be fixed.
Let F ∈ (D∞)d be a nondegenerate random vector such that
(a) E[(det γF )
−p] ≤ c1;
(b) E[‖Dl(F i)‖p
] ≤ c2, i = 1, ..., d.
Then the density of F is uniformly bounded, and the bound does not depend on F but only
on the constants c1(p) and c2(ℓ, p).
Proof. The proof of this result uses the same arguments as in the proof of Dalang and
Nualart [DN04, Lemma 4.11]. Therefore, we will only give the main steps.
Fix z ∈ Rd. Thanks to Corollary 3.3 and the Cauchy-Schwarz inequality we find that
|pF (z)| ≤ ‖H(1,...,d)(F, 1)‖0,2.
Using the continuity of the Skorohod integral δ (cf. Nualart [N95, Proposition 3.2.1]
and Nualart [N98, (1.11) and p.131]) and Hölder’s inequality for Malliavin norms (cf.
Watanabe [W84, Proposition 1.10, p.50]), we obtain
‖H(1,...,d)(F, 1)‖0,2 ≤ c‖H(1,...,d−1)(F, 1)‖1,4
‖(γ−1F )d,j‖1,8 ‖D(F
j)‖1,8. (3.1)
In agreement with hypothesis (b), ‖D(F j)‖m,p ≤ c. In order to bound the second factor in
(3.1), note that
‖(γ−1F )i,j‖m,p =
E[|(γ−1F )i,j|
E[‖Dk(γ−1F )i,j‖
. (3.2)
For the first term in (3.2), we use Cramer’s formula to get that
|(γ−1F )i,j| = |(det γF )
−1(AF )i,j |,
where AF denotes the cofactor matrix of γF . By means of Cauchy-Schwarz inequality and
hypotheses (a) and (b) we find that
E[((γ−1F )i,j)
p] ≤ cd,p{E[(det γF )−2p]}1/2 × {E[‖D(F )‖4p(d−1)H ]}
≤ cd,p,
where none of the constants depend on F . For the second term on the right-hand side of
(3.2), we iterate the equality (cf. Nualart [N95, Lemma 2.1.6])
D(γ−1F )i,j = −
k,ℓ=1
(γ−1F )i,kD(γF )k,ℓ(γ
F )ℓ,j, (3.3)
in the same way as in the proof of Dalang and Nualart [DN04, Lemma 4.11]. Then,
appealing again to hypotheses (a) and (b) and iterating the inequality (3.1) to bound the
first factor on the right-hand side of (3.2), we obtain the uniform boundedness of pF (z).
We finish this section with a result that will be used later on to bound negative moments
of a random variable, as is needed to check hypothesis (a) of Proposition 3.4.
Proposition 3.5. Suppose Z ≥ 0 is a random variable for which we can find ǫ0 ∈ (0, 1),
processes {Yi,ǫ}ǫ∈(0,1) (i = 1, 2), and constants c > 0 and 0 ≤ α2 ≤ α1 with the property that
Z ≥ min(cǫα1 − Y1,ǫ cǫα2 − Y2,ǫ) for all ǫ ∈ (0, ǫ0). Also suppose that we can find βi > αi
(i = 1, 2), not depending on ǫ0, such that
C(q) := sup
0<ǫ<1
E[|Y1,ǫ|q]
E[|Y2,ǫ|q]
<∞ for all q ≥ 1.
Then for all p ≥ 1, there exists a constant c′ ∈ (0,∞), not depending on ǫ0, such that
E[|Z|−p] ≤ c′ǫ−pα10 .
Remark 3.6. This lemma is of interest mainly when β2 ≤ α1.
Proof. Define k := (2/c)ǫ
0 . Suppose that y ≥ k, and let ǫ := (2/c)1/α1y−1/α1 . Then
0 < ǫ ≤ ǫ0, y−1 = (c/2)ǫα1 , and for all q ≥ 1,
Z−1 > y
Z < y−1
Y1,ǫ ≥
Y2,ǫ ≥ cǫα2 −
≤ C(q)
2qǫq(β1−α1) + ǫqβ2
ǫα2 − 1
The inequality ǫα2 − (1/2)ǫα1 ≥ (1/2)ǫα2 implies that
Z−1 > y
≤ C(q)
2qǫq(β1−α1) + 2qǫq(β2−α2)
≤ ay−qb,
where a and b are positive and finite constants that do not depend on y, ǫ0 or q. We apply
this with q := (p/b) + 1 to find that for all p ≥ 1,
|Z|−p
yp−1P
Z−1 > y
≤ kp + ap
y−b−1 dy = kp +
Because k ≥ (2/c) and b > 0, it follows that E[|Z|−p] ≤ (1 + c1(ap/b))kp, where c1 :=
(c/2)b+p. This is the desired result.
4 Existence, smoothness and uniform boundedness of the
one-point density
Let u = {u(t, x), t ∈ [0, T ], x ∈ [0, 1]} be the solution of equation (2.1). In this section,
we prove the existence, smoothness and uniform boundedness of the density of the random
vector u(t, x). In particular, this will prove Theorem 1.1(a).
The first result concerns the Malliavin differentiability of u and the equations satisfied by
its derivatives. We refer to Bally and Pardoux [BP98, Proposition 4.3, (4.16), (4.17)] for
its proof in dimension one. As we work coordinate by coordinate, the following proposition
follows in the same way and its proof is therefore omitted.
Proposition 4.1. Assume P1. Then u(t, x) ∈ (D∞)d for any t ∈ [0, T ] and x ∈ [0, 1].
Moreover, its iterated derivative satisfies
D(k1)r1,v1 · · ·D
rn,vn
(ui(t, x))
Gt−rl(x, vl)
D(k1)r1,v1 · · ·D
(kl−1)
rl−1,vl−1D
(kl+1)
rl+1,vl+1 · · ·D(kn)rn,vn(σikl(u(rl, vl)))
r1∨···∨rn
Gt−θ(x, η)
D(kl)rl,vl(σij(u(θ, η)))W
j(dθ, dη)
r1∨···∨rn
Gt−θ(x, η)
D(kl)rl,vl(bi(u(θ, η))) dθdη
if t ≤ r1 ∨ · · · ∨ rn and D(k1)r1,v1 · · ·D
rn,vn(ui(t, x)) = 0 otherwise. Finally, for any p > 1,
sup(t,x)∈[0,T ]×[0,1]E
∥Dn(ui(t, x))
< +∞. (4.1)
Note that, in particular, the first-order Malliavin derivative satisfies, for r < t,
D(k)r,v (ui(t, x)) = Gt−r(x, v)σik(u(r, v)) + ai(k, r, v, t, x), (4.2)
where
ai(k, r, v, t, x) =
Gt−θ(x, η)D
r,v (σij(u(θ, η)))W
j(dθ, dη)
Gt−θ(x, η)D
r,v (bi(u(θ, η))) dθdη,
(4.3)
and D
r,v (ui(t, x)) = 0 when r > t.
The next result proves property (a) in Proposition 3.4 when F is replaced by u(t, x).
Proposition 4.2. Assume P1 and P2. Let I and J two compact intervals as in Theorem
1.1. Then, for any p ≥ 1,
(det γu(t,x))
is uniformly bounded over (t, x) ∈ I × J .
Proof. This proof follows Nualart [N98, Proof of (3.22)], where it is shown that for fixed
(t, x), E[(detγu(t,x))
−p] < +∞. Our emphasis here is on the uniform bound over (t, x) ∈
I × J . Assume that I = [t1, t2] and J = [x1, x2], where 0 < t1 < t2 ≤ T , 0 < x1 < x2 < 1.
Let (t, x) ∈ I × J be fixed. We write
det γu(t,x) ≥
infξ∈Rd:‖ξ‖=1 ξ
Tγu(t,x)ξ
Let ξ = (ξ1, ..., ξd) ∈ Rd with ‖ξ‖ = 1 and fix ǫ ∈ (0, 1). Note the inequality
(a+ b)2 ≥ 2
a2 − 2b2, (4.4)
valid for all a, b ≥ 0. Using (4.2) and the fact that γu(t,x) is a matrix whose entries are
inner-products, this implies that
ξTγu(t,x)ξ =
Dr,v(ui(t, x))ξi
t(1−ǫ)
Dr,v(ui(t, x))ξi
≥ I1 − I2,
where
t(1−ǫ)
Gt−r(x, v)σik(u(r, v))ξi
I2 =2
t(1−ǫ)
ai(k, r, v, t, x)ξi
and ai(k, r, v, t, x) is defined in (4.3). In accord with hypothesis P2 and thanks to Lemma
I1 ≥ c(tǫ)1/2, (4.5)
where c is uniform over (t, x) ∈ I × J .
Next we apply the Cauchy-Schwarz inequality to find that, for any q ≥ 1,
supξ∈Rd:‖ξ‖=1|I2|q
≤ c(E[|A1|q] + E[|A2|q]),
where
i,j,k=1
t(1−ǫ)
Gt−θ(x, η)D
r,v (σij(u(θ, η)))W
j(dθ, dη)
i,k=1
t(1−ǫ)
Gt−θ(x, η)D
r,v (bi(u(θ, η))) dθdη
We bound the q-th moment of A1 and A2 separately. As regards A1, we use Burkholder’s
inequality for martingales with values in a Hilbert space (Lemma 7.6) to obtain
E[|A1|q] ≤ c
k,i=1
t(1−ǫ)
t(1−ǫ)
, (4.6)
where
Θ := 1{θ>r}Gt−θ(x, η)
D(k)r,v (σij(u(θ, η)))
≤ c1{θ>r}Gt−θ(x, η)
D(k)r,v (ul(θ, η))
thanks to hypothesis P1. Hence,
E[|A1|q] ≤ c
t(1−ǫ)
dη G2t−θ(x, η)
∫ t∧θ
t(1−ǫ)
where Ψ :=
r,v (ul(θ, η)). We now apply Hölder’s inequality with respect to the
measure G2t−θ(x, η)dθdη to find that
E[|A1|q] ≤ C
t(1−ǫ)
dη G2t−θ(x, η)
t(1−ǫ)
dη G2t−θ(x, η)
t(1−ǫ)
Lemmas 7.3 and 7.5 assure that
E[|A1|q] ≤ CT (tǫ)
2 (tǫ)q/2
t(1−ǫ)
G2t−θ(x, η) dθdη ≤ CT (tǫ)q,
where CT is uniform over (t, x) ∈ I × J .
We next derive a similar bound for A2. By the Cauchy–Schwarz inequality,
E [|A2|q] ≤ c(tǫ)q
i,k=1
t(1−ǫ)
where Φ := Gt−θ(x, η)|D(k)r,v (bi(u(θ, η))) |. From here on, the q-th moment of A2 is estimated
as that of A1 was; cf. (4.6), and this yields E[|A2|q] ≤ CT (tǫ)2q.
Thus, we have proved that
supξ∈Rd:‖ξ‖=1|I2|q
≤ CT (tǫ)q, (4.7)
where the constant CT is clearly uniform over (t, x) ∈ I × J .
Finally, we apply Proposition 3.5 with Z := inf‖ξ‖=1(ξ
Tγu(t,x)ξ), Y1,ǫ = Y2,ǫ = sup‖ξ‖=1I2,
ǫ0 = 1, α1 = α2 = 1/2 and β1 = β2 = 1, to get
(detγu(t,x))
≤ CT ,
where all the constants are clearly uniform over (t, x) ∈ I×J . This is the desired result.
Corollary 4.3. Assume P1 and P2. Fix T > 0 and let I and J be a compact intervals
as in Theorem 1.1. Then, for any (t, x) ∈ (0, T ]× (0, 1), u(t, x) is a nondegenerate random
vector and its density funciton is infinitely differentiable and uniformly bounded over z ∈ Rd
and (t, x) ∈ I × J .
Proof of Theorem 1.1(a). This is a consequence of Propositions 4.1 and 4.2 together with
Theorem 3.1 and Proposition 3.4.
5 The Gaussian-type lower bound on the one-point density
The aim of this section is to prove the lower bound of Gaussian-type for the density of
u stated in Theorem 1.1(b). The proof of this result was given in Kohatsu-Higa [K03,
Theorem 10] for dimension 1, therefore we will only sketch the main steps.
Proof of Theorem 1.1(b). We follow [K03] and we show that for each (t, x), F = u(t, x) is a
d-dimensional uniformly elliptic random vector and then we apply [K03, Theorem 5]. Let
F in =
Gt−r(x, v)
σij(u(r, v))W
j(dr, dv) +
Gt−r(x, v) bi(u(r, v)) drdv,
1 ≤ i ≤ d, where 0 = t0 < t1 < · · · < tN = t is a sufficiently fine partition of [0, t]. Note
that Fn ∈ Ftn . Set g(s, y) = Gt−s(x, y). We shall need the following two lemmas.
Lemma 5.1. [K03, Lemma 7] Assume P1 and P2. Then:
(i) ‖F in‖k,p ≤ ck,p, 1 ≤ i ≤ d;
(ii) ‖((γFn(tn−1))ij)−1‖p,tn−1 ≤ cp(∆n−1(g))−1 = cp(‖g‖2L2([tn−1,tn]×[0,1]))
where γFn(tn−1) denotes the conditional Malliavin matrix of Fn given Ftn−1 and ‖ · ‖p,tn−1
denotes the conditional Lp-norm.
We define
un−1i (s1, y1) =
∫ tn−1
Gs1−s2(y1, y2)
σij(u(s2, y2))W
j(ds1, dy2)
∫ tn−1
Gs1−s2(y1, y2) bi(u(s2, y2)) ds2dy2, 1 ≤ i ≤ d.
Note that un−1 ∈ Ftn−1 . As in [K03], the following holds.
Lemma 5.2. [K03, Lemma 8] Under hypothesis P1, for s ∈ [tn−1, tn],
‖ui(s, y)− un−1i (s, y)‖n,p,tn−1 ≤ (s− tn−1)
1/8, 1 ≤ i ≤ d,
where ‖ · ‖n,p,tn−1 denotes the conditional Malliavin norm given Ftn−1 .
The rest of the proof of Theorem 1.1(b) follows along the same lines as in [K03] for
d = 1. We only sketch the remaining main points where the fact that d > 1 is important.
In order to obtain the expansion of F in−F in−1 as in [K03, Lemma 9], we proceed as follows.
By the mean value theorem,
F in − F in−1 =
Gt−r(x, v)
σij(u
n−1(r, v))W j(dr, dv)
Gt−r(x, v) bi(u(r, v)) drdv
Gt−r(x, v)
j,l=1
∂lσij(u(r, v, λ))dλ)(ul(r, v) − un−1l (r, v))W
j(dr, dv),
where u(r, v, λ) = (1−λ)u(r, v)+λun−1(r, v). Using the terminology of [K03], the first term
is a process of order 1 and the next two terms are residues of order 1 (as in [K03]). In the
next step, we write the residues of order 1 as the sum of processes of order 2 and residues
of order 2 and 3 as follows:
Gt−r(x, v) bi(u(r, v)) drdv
Gt−r(x, v) bi(u
n−1(r, v)) drdv
Gt−r(x, v)
∂lbi(u(r, v, λ))dλ)(ul(r, v) − un−1l (r, v)) drdv
Gt−r(x, v)
j,l=1
∂lσij(u(r, v, λ))dλ)(ul(r, v) − un−1l (r, v))W
j(dr, dv)
Gt−r(x, v)
j,l=1
∂lσij(u
n−1(r, v))(ul(r, v) − un−1l (r, v))W
j(dr, dv)
Gt−r(x, v)
j,l,l′=1
∂l∂l′σij(u(r, v, λ))dλ)
× (ul(r, v) − un−1l (r, v))(ul′ (r, v) − u
(r, v))W j(dr, dv).
It is then clear that the remainder of the proof of [K03, Lemma 9] follows for d > 1 along
the same lines as in [K03], working coordinate by coordinate.
Finally, in order to complete the proof of the proposition, it suffices to verify the hy-
potheses of [K03, Theorem 5]. Again the proof follows as in the proof of [K03, Theorem
10], working coordinate by coordinate. We will only sketch the proof of his (H2c), where
hypothesis P2 is used:
(∆n−1(g))
(Gt−r(x, v))
2‖σ(un−1(r, v))ξ‖2 drdv
≥ ρ2(∆n−1(g))−1
(Gt−r(x, v))
2 drdv = ρ2 > 0,
by the definition of g. This concludes the proof of Theorem 1.1 (b).
6 The Gaussian-type upper bound on the two-point density
Let ps,y; t,x(z1, z2) denote the joint density of the 2d-dimensional random vector
(u1(s, y), ..., ud(s, y), u1(t, x), ..., ud(t, x)),
for s, t ∈ (0, T ], x, y ∈ (0, 1), (s, y) 6= (t, x) and z1, z2 ∈ Rd (the existence of this joint
density will be a consequence of Theorem 3.1, Proposition 4.1 and Theorem 6.3).
The next subsections lead to the proofs of Theorem 1.1(c) and (d).
6.1 Bounds on the increments of the Malliavin derivatives
In this subsection, we prove an upper bound for the Sobolev norm of the derivative of the
increments of our process u. For this, we will need the following preliminary estimate.
Lemma 6.1. For any s, t ∈ [0, T ], s ≤ t, and x, y ∈ [0, 1],
(g(r, v))2 drdv ≤ CT (|t− s|1/2 + |x− y|),
where
g(r, v) := gt,x,s,y(r, v) = 1{r≤t}Gt−r(x, v) − 1{r≤s}Gs−r(y, v).
Proof. Using Bally, Millet, and Sanz-Solé [BMS95, Lemma B.1] with α = 2, we see
(g(r, v))2 drdv
(Gt−r(x, v))
2 drdv + 2
(Gt−r(x, v) −Gs−r(x, v))2 drdv
(Gs−r(x, v)−Gs−r(y, v))2 drdv
≤ CT (|t− s|1/2 + |x− y|).
Proposition 6.2. Assuming P1, for any s, t ∈ [0, T ], s ≤ t, x, y ∈ [0, 1], p > 1, m ≥ 1,
∥Dm(ui(t, x)− ui(s, y))
|t− s|1/2 + |x− y|
, i = 1, ..., d.
Proof. Let m = 1. Consider the function g(r, v) defined in Lemma 6.1. Using the integral
equation (4.2) satisfied by the first-order Malliavin derivative, we find that
∥D(ui(t, x)− ui(s, y))
E[|I1|p/2] + E[|I2|p/2] + E[|I3|p/2]
where
dv (g(r, v)σik(u(r, v)))
j,k=1
g(θ, η)D(k)r,v (σij(u(θ, η)))W
j(dθ, dη)
g(θ, η)D(k)r,v (bi(u(θ, η)))dθdη
We bound the p/2-moments of I1, I2 and I3 separately.
By hypothesis P1 and Lemma 6.1, E[|I1|p/2] ≤ CT (|t − s|1/2 + |x − y|)p/2. Using
Burkholder’s inequality for Hilbert-space-valued martingales (Lemma 7.6) and hypothesis
P1, we obtain
E[|I2|p/2] ≤ C
dη (g(θ, η))2
where Θ :=
r,v (ul(θ, η)). From Hölder’s inequality with respect to the measure
(g(θ, η))2dθdη, we see that this is bounded above by
(g(θ, η))2dθdη
× sup(θ,η)∈[0,T ]×[0,1]
dη(g(θ, η))2
≤ CT (|t− s|1/2 + |x− y|)p/2,
thanks to (4.1) and Lemma 6.1.
We next derive a similar bound for I3. By the Cauchy–Schwarz inequality,
E[|I3|p/2] ≤ CT
dη (g(θ, η))2
From here on, the p/2-moment of I3 is estimated as was that of I2, and this yields E[|I3|p/2] ≤
CT (|t − s|1/2 + |x − y|)p/2. This proves the desired result for m = 1. The case m > 1
follows using the stochastic differential equation satisfied by the iterated Malliavin deriva-
tives (Proposition 4.1), Hölder’s and Burkholder’s inequalities, hypothesis P1, (4.1) and
Lemma 6.1 in the same way as we did for m = 1, to obtain the desired bound.
6.2 Study of the Malliavin matrix
For s, t ∈ [0, T ], s ≤ t, and x, y ∈ [0, 1] consider the 2d-dimensional random vector
Z := (u1(s, y), ..., ud(s, y), u1(t, x)− u1(s, y), ..., ud(t, x)− ud(s, y)). (6.1)
Let γZ the Malliavin matrix of Z. Note that γZ = ((γZ)m,l)m,l=1,...,2d is a symmetric 2d×2d
random matrix with four d× d blocs of the form
... γ
· · ·
... · · ·
... γ
where
Z = (〈D(ui(s, y)),D(uj(s, y))〉H )i,j=1,...,d,
Z = (〈D(ui(s, y)),D(uj(t, x)− uj(s, y))〉H )i,j=1,...,d,
Z = (〈D(ui(t, x)− ui(s, y)),D(uj(s, y))〉H )i,j=1,...,d,
Z = (〈D(ui(t, x)− ui(s, y)),D(uj(t, x)− uj(s, y))〉H )i,j=1,...,d.
We let (1) denote the set of indices {1, ..., d}×{1, ..., d}, (2) the set {1, ..., d}×{d+1, ..., 2d},
(3) the set {d+ 1, ..., 2d} × {1, ..., d} and (4) the set {d+ 1, ..., 2d} × {d+ 1, ..., 2d}.
The following theorem gives an estimate on the Sobolev norm of the entries of the inverse
of the matrix γZ , which depends on the position of the entry in the matrix.
Theorem 6.3. Fix η, T > 0. Assume P1 and P2. Let I and J be two compact intervals
as in Theorem 1.1.
(a) For any (s, y) ∈ I × J , (t, x) ∈ I × J , s ≤ t, (s, y) 6= (t, x), k ≥ 0, p > 1,
‖(γ−1Z )m,l‖k,p ≤
ck,p,η,T (|t− s|1/2 + |x− y|)−dη if (m, l) ∈ (1),
ck,p,η,T (|t− s|1/2 + |x− y|)−1/2−dη if (m, l) ∈ (2) or (3),
ck,p,η,T (|t− s|1/2 + |x− y|)−1−dη if (m, l) ∈ (4).
(b) For any s = t ∈ (0, T ], (t, y) ∈ I × J , (t, x) ∈ I × J , x 6= y, k ≥ 0, p > 1,
‖(γ−1Z )m,l‖k,p ≤
ck,p,T if (m, l) ∈ (1) ,
ck,p,T |x− y|−1/2 if (m, l) ∈ (2) or (3),
ck,p,T |x− y|−1 if (m, l) ∈ (4).
(Note the slight improvements in the exponents in case (b) where s = t.)
The proof of this theorem is deferred to Section 6.4. We assume it for the moment and
complete the proof of Theorem 1.1(c) and (d).
6.3 Proof of Theorem 1.1(c) and (d)
Fix two compact intervals I and J as in Theorem 1.1. Let (s, y), (t, x) ∈ I × J , s ≤ t,
(s, y) 6= (t, x), and z1, z2 ∈ Rd. Let Z be as in (6.1) and let pZ be the density of Z. Then
ps,y; t,x(z1, z2) = pZ(z1, z1 − z2).
Apply Corollary 3.3 with σ = {i ∈ {1, ..., d} : zi1 − zi2 ≥ 0} and Hölder’s inequality to see
pZ(z1, z1 − z2) ≤
|ui(t, x)− ui(s, y)| > |zi1 − zi2|
× ‖H(1,...,2d)(Z, 1)‖0,2.
Therefore, in order to prove the desired results (c) and (d) of Theorem 1.1, it suffices to
prove that:
|ui(t, x)− ui(s, y)| > |zi1 − zi2|
≤ c exp
− ‖z1 − z2‖
cT (|t− s|1/2 + |x− y|)
, (6.2)
‖H(1,...,2d)(Z, 1)‖0,2 ≤ cT (|t− s|1/2 + |x− y|)−(d+η)/2, (6.3)
and if s = t, then
‖H(1,...,2d)(Z, 1)‖0,2 ≤ cT |x− y|−d/2. (6.4)
Proof of (6.2). Let ũ denote the solution of (2.1) for b ≡ 0. Consider the continuous one-
parameter martingale (Mu = (M
u , ...,M
u ), 0 ≤ u ≤ t) defined by
M iu =
(Gt−r(x, v)−Gs−r(y, v))
j=1 σij(ũ(r, v))W
j(dr, dv) if 0 ≤ u ≤ s,
(Gt−r(x, v) −Gs−r(y, v))
j=1 σij(ũ(r, v))W
j(dr, dv)
Gt−r(x, v)
j=1 σij(ũ(r, v))W
j(dr, dv) if s ≤ u ≤ t,
for all i = 1, ..., d, with respect to the filtration (Fu, 0 ≤ u ≤ t). Notice that
M0 = 0, Mt = ũ(t, x)− ũ(s, y).
Moreover, by hypothesis P1 and Lemma 6.1,
〈M i〉t =
(Gt−r(x, v) −Gs−r(y, v))2
(σij(ũ(r, v)))
2 drdv
(Gt−r(x, v))
(σij(ũ(r, v)))
2 drdv
(g(r, v))2 drdv
≤ CT (|t− s|1/2 + |x− y|).
By the exponential martingale inequality Nualart [N95, A.5],
|ũi(t, x)− ũi(s, y)| > |zi1 − zi2|
≤ 2 exp
1 − zi2|2
CT (|t− s|1/2 + |x− y|)
. (6.5)
We will now treat the case b 6≡ 0 using Girsanov’s theorem. Consider the random
variable
Lt = exp
σ−1(u(r, v)) b(u(r, v)) ·W (dr, dv)
‖σ−1(u(r, v)) b(u(r, v))‖2 drdv
The following Girsanov’s theorem holds.
Theorem 6.4. [N94, Prop.1.6] E[Lt] = 1, and if P̃ denotes the probability measure on
(Ω,F ) defined by
(ω) = Lt(ω),
then W̃ (t, x) = W (t, x) +
σ−1(u(r, v)) b(u(r, v)) drdv is a standard Brownian sheet
under P̃.
Consequently, the law of u under P̃ coincides with the law of ũ under P. Consider now
the random variable
Jt = exp
σ−1(ũ(r, v)) b(ũ(r, v)) ·W (dr, dv)
‖σ−1(ũ(r, v)) b(ũ(r, v))‖2 drdv
Then, by Theorem 6.4, the Cauchy-Schwarz inequality and (6.5),
|ui(t, x)− ui(s, y)| > |zi1 − zi2|
1{|ui(t,x)−ui(s,y)||zi1−zi2|}
1{|ũi(t,x)−ũi(s,y)||zi1−zi2|}
|ũi(t, x)− ũi(s, y)||zi1 − zi2|
})1/2(
≤ 2 exp
1 − zi2|2
CT (|t− s|1/2 + |x− y|)
Now, hypothesis P1 and P2 give
t ] ≤ EP
2σ−1(ũ(r, v)) b(ũ(r, v)) ·W (dr, dv)
4 ‖σ−1(ũ(r, v)) b(ũ(r, v))‖2 drdv
× exp
‖σ−1(ũ(r, v)) b(ũ(r, v))‖2 drdv
since the second exponential is bounded and the first is an exponential martingale.
Therefore, we have proved that
|ui(t, x)− ui(s, y)| > |zi1 − zi2|
≤ C exp
1 − zi2|2
CT (|t− s|1/2 + |x− y|)
from which we conclude that
|ui(t, x)− ui(s, y)| > |zi1 − zi2|
≤ C exp
− ‖z1 − z2‖
CT (|t− s|1/2 + |x− y|)
This proves (6.2).
Proof of (6.3). As in (3.1), using the continuity of the Skorohod integral δ and Hölder’s
inequality for Malliavin norms, we obtain
‖H(1,...,2d)(Z, 1)‖0,2 ≤ C‖H(1,...,2d−1)(Z, 1)‖1,4
‖(γ−1Z )2d,j‖1,8 ‖D(Z
j)‖1,8
j=d+1
‖(γ−1Z )2d,j‖1,8 ‖D(Z
j)‖1,8
Notice that the entries of γ−1Z that appear in this expression belong to sets (3) and (4) of
indices, as defined before Theorem 6.3. From Theorem 6.3(a) and Propositions 4.1 and 6.2,
we find that this is bounded above by
CT ‖H(1,...,2d−1)(Z, 1)‖1,4
(|t−s|1/2+ |x−y|)−
j=d+1
(|t−s|1/2+ |x−y|)−1−dη+
that is, by
CT ‖H(1,...,2d−1)(Z, 1)‖1,4(|t− s|1/2 + |x− y|)−1/2−dη .
Iterating this procedure d times (during which we only encounter coefficients (γ−1Z )m,l for
(m, l) in blocs (3) and (4), cf. Theorem 6.3(a)), we get, for some integers m0, k0 > 0,
‖H(1,...,2d)(Z, 1)‖0,2 ≤ CT ‖H(1,...,d)(Z, 1)‖m0 ,k0(|t− s|1/2 + |x− y|)−d/2−d
Again, using the continuity of δ and Hölder’s inequality for the Malliavin norms, we obtain
‖H(1,...,d)(Z, 1)‖m,k ≤ C‖H(1,...,d−1)(Z, 1)‖m1 ,k1
‖(γ−1Z )d,j‖m2,k2 ‖D(Z
j)‖m3,k3
j=d+1
‖(γ−1Z )d,j‖m4,k4 ‖D(Z
j)‖m5,k5
for some integers mi, ki > 0, i = 1, ..., 5. This time, the entries of γ
Z that appear in this
expression come from the sets (1) and (2) of indices. We appeal again to Theorem 6.3(a)
and Propositions 4.1 and 6.2 to get
‖H(1,...,d)(Z, 1)‖m,k ≤ CT ‖H(1,...,d−1)(Z, 1)‖m1 ,k1(|t− s|1/2 + |x− y|)−dη.
Finally, iterating this procedure d times (during which we encounter coefficients (γ−1Z )m,l for
(m, l) in blocs (1) and (2) only, cf. Theorem 6.3(a)), and choosing η′ = 4d2η, we conclude
‖H(1,...,2d)(Z, 1)‖0,2 ≤ CT (|t− s|1/2 + |x− y|)−(d+η
′)/2,
which proves (6.3) and concludes the proof of Theorem 1.1(c).
Proof of (6.4). In order to prove (6.4), we proceed exactly along the same lines as in the
proof of (6.3) but we appeal to Theorem 6.3(b). This concludes the proof of Theorem
1.1(d).
6.4 Proof of Theorem 6.3
Let Z as in (6.1). Since the inverse of the matrix γZ is the inverse of its determinant
multiplied by its cofactor matrix, we examine these two factors separately.
Proposition 6.5. Fix T > 0 and let I and J be compact intervals as in Theorem 1.1.
Assuming P1, for any (s, y), (t, x) ∈ I × J , (s, y) 6= (t, x), p > 1,
E[|(AZ)m,l|p]1/p ≤
cp,T (|t− s|1/2 + |x− y|)d if (m, l) ∈ (1),
cp,T (|t− s|1/2 + |x− y|)d−
2 if (m, l) ∈ (2) or (3),
cp,T (|t− s|1/2 + |x− y|)d−1 if (m, l) ∈ (4),
where AZ denotes the cofactor matrix of γZ .
Proof. We consider the four different cases.
• If (m, l) ∈ (1), we claim that
|(AZ)m,l| ≤ C
‖D(u(s, y))‖2k
× ‖D(u(t, x)− u(s, y))‖2(d−1−k)
× ‖D(u(s, y))‖2(d−1−k)
× ‖D(u(t, x) − u(s, y))‖2(k+1)
(6.6)
Indeed, let A
Z = (a
m̄,l̄
)m̄,l̄=1,...,2d−1 be the (2d−1)× (2d−1)-matrix obtained by removing
from γZ its row m and column l. Then
(AZ)m,l = det ((AZ)
m,l) =
π permutation of (1,...,2d−1)
1,π(1)
· · · am,l
2d−1,π(2d−1).
Each term of this sum contains one entry from each row and column of A
Z . If there are
k entries taken from bloc (1) of γZ , these occupy k rows and columns of A
Z . Therefore,
d−1−k entries must come from the d−1 remaining rows of bloc (2), and the same number
from the columns of bloc (3). Finally, there remain k+1 entries to be taken from bloc (4).
Therefore,
|(AZ)m,l| ≤ C
(product of k entries from (1))
× (product of d− 1− k entries from (2))
× (product of d− 1− k entries from (3))× (product of k + 1 entries from (4))
Adding all the terms and using the particular form of these terms establishes (6.6).
Regrouping the various factors in (6.6), applying the Cauchy-Schwarz inequality and
using (4.1) and Proposition 6.2, we obtain
|(AZ)m,l|p
‖D(u(s, y))‖2(d−1)p
× ‖D(u(t, x) − u(s, y))‖2dp
≤ CT (|t− s|1/2 + |x− y|)dp.
• If (m, l) ∈ (2) or (m, l) ∈ (3), then using the same arguments as above, we obtain
|(AZ)m,l|
‖D(u(s, y))‖2(d−1−k)
× ‖D(u(s, y))‖k
× ‖D(u(t, x) − u(s, y))‖k
× ‖D(u(s, y))‖k+1
× ‖D(u(t, x)− u(s, y))‖k+1
× ‖D(u(t, x)− u(s, y))‖2(d−1−k)
‖D(u(s, y))‖2d−1
× ‖D(u(t, x) − u(s, y))‖2d−1
from which we conclude, using (4.1) and Proposition 6.2, that
|(AZ)m,l|p
≤ CT (|t− s|1/2 + |x− y|)(d−
• If (i, j) ∈ (4), we obtain
|(AZ)m,l| ≤ C
‖D(u(s, y))‖2(k+1)
× ‖D(u(s, y))‖2(d−1−k)
× ‖D(u(t, x) −D(u(s, y)))‖2(d−1−k)
× ‖D(u(t, x) −D(u(s, y)))‖2k
‖D(u(s, y))‖2d
× ‖D(u(t, x) − u(s, y))‖2d−2
from which we conclude that
|(AZ)m,l|p
≤ CT (|t− s|1/2 + |x− y|)(d−1)p.
This concludes the proof of the proposition.
Proposition 6.6. Fix η, T > 0. Assume P1 and P2. Let I and J be compact intervals as
in Theorem 1.1.
(a) There exists C depending on T and η such that for any (s, y), (t, x) ∈ I × J , (s, y) 6=
(t, x), p > 1,
det γZ
)−p]1/p ≤ C(|t− s|1/2 + |x− y|)−d(1+η). (6.7)
(b) There exists C only depending on T such that for any s = t ∈ I, x, y ∈ J , x 6= y,
p > 1,
det γZ
)−p]1/p ≤ C(|x− y|)−d.
Assuming this proposition, we will be able to conclude the proof of Theorem 6.3, after
establishing the following estimate on the derivative of the Malliavin matrix.
Proposition 6.7. Fix T > 0. Let I and J be compact intervals as in Theorem 1.1. As-
suming P1, for any (s, y), (t, x) ∈ I × J , (s, y) 6= (t, x), p > 1 and k ≥ 1,
‖Dk(γZ)m,l‖pH ⊗k
]1/p ≤
ck,p,T if (m, l) ∈ (1),
ck,p,T (|t− s|1/2 + |x− y|)1/2 if (m, l) ∈ (2) or (3),
ck,p,T (|t− s|1/2 + |x− y|) if (m, l) ∈ (4).
Proof. We consider the four different blocs.
• If (m, l) ∈ (4), proceeding as inDalang and Nualart [DN04, p.2131] and appealing
to Proposition 6.2 twice, we obtain
∥Dk(γZ)m,l
dv Dr,v(um(t, x)− um(s, y)) ·Dr,v(ul(t, x) − ul(s, y))
≤ (k + 1)p−1
dv DjDr,v(um(t, x)− um(s, y))
·Dk−jDr,v(ul(t, x)− ul(s, y))
≤ C̃T (k + 1)p−1
∥DjD(um(t, x)− um(s, y))
H ⊗(j+1)
])1/2
∥Dk−jD(ul(t, x)− ul(s, y))
H ⊗(k−j+1)
])1/2}
≤ CT (|t− s|1/2 + |x− y|)p.
• If (m, l) ∈ (2) or (m, l) ∈ (3), proceeding as above and appealing to (4.1) and Propo-
sition 6.2, we get
∥Dk(γZ)m,l
≤ C̃T (k + 1)p−1
∥DjD(um(t, x)− um(s, y))
H ⊗(j+1)
])1/2
∥Dk−jD(ul(s, y))
H ⊗(k−j+1)
])1/2}
≤ CT (|t− s|1/2 + |x− y|)p/2.
• If (m, l) ∈ (1), using (4.1), we obtain
∥Dk(γZ)m,l
≤ C̃T (k + 1)p−1
∥DjD(um(s, y))
H ⊗(j+1)
])1/2
∥Dk−jD(ul(s, y))
H ⊗(k−j+1)
])1/2}
≤ CT .
Proof of Theorem 6.3. When k = 0, the result follows directly using the fact that the
inverse of a matrix is the inverse of its determinant multiplied by its cofactor matrix and
the estimates of Propositions 6.5 and 6.6.
For k ≥ 1, we shall establish the following two properties.
(a) For any (s, y), (t, x) ∈ I × J , (s, y) 6= (t, x), s ≤ t, k ≥ 1 and p > 1,
E[‖Dk(γ−1Z )m,l‖
]1/p ≤
ck,p,η,T (|t− s|1/2 + |x− y|)−dη if (m, l) ∈ (1) ,
ck,p,η,T (|t− s|1/2 + |x− y|)−1/2−dη if (m, l) ∈ (2), (3),
ck,p,η,T (|t− s|1/2 + |x− y|)−1−dη if (m, l) ∈ (4).
(b) For any s = t ∈ I, x, y ∈ J , x 6= y, k ≥ 1 and p > 1,
E[‖Dk(γ−1Z )m,l‖
]1/p ≤
ck,p,T if (m, l) ∈ (1) ,
ck,p,T |x− y|−1/2 if (m, l) ∈ (2) or (3),
ck,p,T |x− y|−1 if (m, l) ∈ (4).
Since
‖(γ−1Z )m,l‖k,p =
E[|(γ−1Z )m,l|
E[‖Dj(γ−1Z )m,l‖
(a) and (b) prove the theorem.
We now prove (a) and (b). When k = 1, we will use (3.3) written as a matrix product:
D(γ−1Z ) = γ
Z D(γZ)γ
Z . (6.8)
Writing (6.8) in bloc product matrix notation with blocs (1), (2), (3) and (4), we get that
D((γ−1Z )
(1)) = (γ−1Z )
(1)D(γ
Z )(γ
(1) + (γ−1Z )
(1)D(γ
Z )(γ
+ (γ−1Z )
(2)D(γ
Z )(γ
(1) + (γ−1Z )
(2)D(γ
Z )(γ
D((γ−1Z )
(2)) = (γ−1Z )
(1)D(γ
Z )(γ
(2) + (γ−1Z )
(1)D(γ
Z )(γ
+ (γ−1Z )
(2)D(γ
Z )(γ
(2) + (γ−1Z )
(2)D(γ
Z )(γ
D((γ−1Z )
(3)) = (γ−1Z )
(3)D(γ
Z )(γ
(1) + (γ−1Z )
(3)D(γ
Z )(γ
+ (γ−1Z )
(4)D(γ
Z )(γ
(1) + (γ−1Z )
(4)D(γ
Z )(γ
D((γ−1Z )
(4)) = (γ−1Z )
(3)D(γ
Z )(γ
(2) + (γ−1Z )
(3)D(γ
Z )(γ
+ (γ−1Z )
(4)D(γ
Z )(γ
(2) + (γ−1Z )
(4)D(γ
Z )(γ
It now suffices to apply Hölder’s inequality to each block and use the estimates of the
case k = 0 and Proposition 6.7 to obtain the desired result for k = 1. For instance, for
(m, l) ∈ (1),
(γ−1Z )
(2)D(γ
Z )(γ
≤ sup
m1,l1
(γ−1Z )
m1,l1
]1/(2p)
m2,l2
m2,l2
]1/(4p)
× sup
m3,l3
(γ−1Z )
m3,l3
]1/(4p)
≤ c (|t− s|1/2 + |x− y|)−
−dη+1− 1
−dη = c (|t− s|1/2 + |x− y|)−2dη .
For k ≥ 1, in order to calculate Dk+1(γ(·)Z ), we will need to compute Dk(γ
Z D(γZ)γ
For bloc numbers i1, i2, i3 ∈ {1, 2, 3, 4} and k ≥ 1, we have
(γ−1Z )
(i1)D(γ
Z )(γ
j1+j2+j3=k
ji∈{0,...,k}
j1 j2 j3
(γ−1Z )
(γ−1Z )
Note that by Proposition 6.7, the norms of the derivatives Dj2
Z ) of γ
Z are of the
same order for all j2. Hence, we appeal again to Hölder’s inequality and Proposition 6.7,
and use a recursive argument in order to obtain the desired bounds.
Proof of Proposition 6.6. The main idea for the proof of Proposition 6.6 is to use a pertur-
bation argument. Indeed, for (t, x) close to (s, y), the matrix γZ is close to
... 0
· · ·
... · · ·
... 0
The matrix γ̂ has d eigenvectors of the form (λ̂1,0), ..., (λ̂d,0), where λ̂1, ..., λ̂d ∈ Rd are
eigenvectors of γ
Z = γu(s,y), and 0 = (0, ..., 0) ∈ Rd, and d other eigenvectors of the form
(0, ei) where e1, ..., ed is a basis of Rd. These last eigenvectors of γ̂ are associated with the
eigenvalue 0, while the former are associated with eigenvalues of order 1, as can be seen in
the proof of Proposition 4.2.
We now write
det γZ =
(ξi)T γZξ
i, (6.9)
where ξ = {ξ1, ..., ξ2d} is an orthonormal basis of R2d consisting of eigenvectors of γZ . We
then expect that for (t, x) close to (s, y), there will be d eigenvectors close to the subspace
generated by the (λ̂i,0), which will contribute a factor of order 1 to the product in (6.9),
and d other eigenvectors, close to the subspace generated by the (0, ei), that will each
contribute a factor of order (|t− s|1/2 + |x− y|)−1−η to the product. Note that if we do not
distinguish between these two types of eigenvectors, but simply bound below the product
by the smallest eigenvalue to the power 2d, following the approach used in the proof of
Proposition 4.2, then we would obtain C(|t− s|1/2 + |x − y|)−2dp in the right-hand side of
(6.7), which would not be the correct order.
We now carry out this somewhat involved perturbation argument. Consider the spaces
E1 = {(λ,0) : λ ∈ Rd,0 ∈ Rd} and E2 = {(0, µ) : µ ∈ Rd,0 ∈ Rd}. Note that every ξi can
be written as
ξi = (λi, µi) = αi(λ̃
i,0) +
1− α2i (0, µ̃
i), (6.10)
where λi, µi ∈ Rd, (λ̃i,0) ∈ E1, (0, µ̃i) ∈ E2, with ‖λ̃i‖ = ‖µ̃i‖ = 1 and 0 ≤ αi ≤ 1. Note
in particular that ‖ξi‖2 = ‖λi‖2 + ‖µi‖2 = 1 (norms of elements of Rd or R2d are Euclidean
norms).
Lemma 6.8. Given a sufficiently small α0 > 0, with probability one, there exist at least d
of these vectors, say ξ1, ..., ξd, such that α1 ≥ α0, ..., αd ≥ α0.
Proof. Observe that as ξ is an orthogonal family and for i 6= j, the Euclidean inner product
of ξi and ξj is
ξi · ξj = αiαj (λ̃i · λ̃j) +
1− α2i
1− α2j (µ̃
i · µ̃j) = 0.
For α0 > 0, let D = {i ∈ {1, ..., 2d} : αi < α0}. Then, for i, j ∈ D, i 6= j, if α0 < 12 , then
|µ̃i · µ̃j | = αiαj√
1− α2i
1− α2j
|λ̃i · λ̃j| ≤ α
1− α20
‖λ̃i‖‖λ̃j‖ ≤ 1
Since the diagonal terms of the matrix (µ̃i · µ̃j)i,j∈D are all equal to 1, for α0 sufficiently
small, it follows that det((µ̃i · µ̃j)i,j∈D) 6= 0. Therefore, {µ̃i, i ∈ D} is a linearly independent
family, and, as (0, µ̃i) ∈ E2, for i = 1, ..., 2d, we conclude that a.s., card(D) ≤ dim(E2) = d.
We can therefore assume that {1, ..., d} ⊂ Dc and so α1 ≥ α0,...,αd ≥ α0.
By Lemma 6.8 and Cauchy-Schwarz inequality one can write
det γZ
)−p]1/p ≤
(ξi)T γZξ
)−2p])1/(2p)
ξ=(λ,µ)∈R2d :
‖λ‖2+‖µ‖2=1
ξTγZξ
−2dp
)1/(2p)
With this, Propositions 6.9 and 6.15 below conclude the proof of Proposition 6.6.
6.4.1 Small Eigenvalues
Let I and J two compact intervals as in Theorem 1.1.
Proposition 6.9. Fix η, T > 0. Assume P1 and P2.
(a) There exists C depending on η and T such that for all s, t ∈ I, 0 < t−s < 1, x, y ∈ J ,
x 6= y, and p > 1,
ξ=(λ,µ)∈R2d :
‖λ‖2+‖µ‖2=1
ξTγZξ
−2dp
≤ C(|t− s|1/2 + |x− y|)−2dp(1+η).
(b) There exists C depending only on T such that for all s = t ∈ I, x, y ∈ J , x 6= y, and
p > 1,
ξ=(λ,µ)∈R2d:
‖λ‖2+‖µ‖2=1
ξTγZξ
−2dp
≤ C(|x− y|)−2dp.
Proof. We begin by proving (a). Since γZ is a matrix of inner products, we can write
ξTγZξ =
r,v (u(s, y)) + µi(D
r,v (u(t, x)) −D(k)r,v (u(s, y)))
Therefore, for ǫ ∈ (0, t− s),
ξTγZξ ≥ J1 + J2,
where
J1 :=
(λi − µi) [Gs−r(y, v)σik(u(r, v)) + ai(k, r, v, s, y)] +W
J2 :=
dvW 2,
ai(k, r, v, s, y) is defined in (4.3) and
[µiGt−r(x, v)σik(u(r, v)) + µiai(k, r, v, t, x)] .
From here on, the proof is divided into two cases.
Case 1. In the first case, we assume that |x− y|2 ≤ t− s. Choose and fix an ǫ ∈ (0, t − s).
Then we may write
‖ξ‖=1
ξTγZξ ≥ min
‖ξ‖=1 ,‖µ‖≥ǫη/2
J2 , inf
‖ξ‖=1 ,‖µ‖≤ǫη/2
We are going to prove that
‖ξ‖=1 ,‖µ‖≥ǫη/2
J2 ≥ ǫ
+η − Y1,ǫ,
‖ξ‖=1 ,‖µ‖≤ǫη/2
J1 ≥ ǫ1/2 − Y2,ǫ,
(6.11)
where, for all q ≥ 1,
E [|Y1,ǫ|q] ≤ c1(q)ǫq and E [|Y2,ǫ|q] ≤ c2(q)ǫq(
+η). (6.12)
We assume these, for the time being, and finish the proof of the proposition in Case 1. Then
we will return to proving (6.11) and (6.12).
We can combine (6.11) and (6.12) with Proposition 3.5 to find that
‖ξ‖=1
ξTγZξ
)−2pd
≤ c(t− s)−2pd(
(t− s)1/2 + |x− y|
]−2pd(1+2η)
whence follows the proposition in the case that |x− y|2 ≤ t− s. Now we complete our proof
of Case 1 by deriving (6.11) and (6.12).
Let us begin with the term that involves J2. Inequality (4.4) implies that
‖ξ‖=1 ,‖µ‖≥ǫη/2
J2 ≥ Ŷ1,ǫ − Y1,ǫ,
where
Ŷ1,ǫ :=
‖µ‖≥ǫη/2
µiσik(u(r, v))
G2t−r(x, v),
Y1,ǫ := 2 sup
‖µ‖≥ǫη/2
µiai(k, r, v, t, x)
In agreement with hypothesis P2, and thanks to Lemma 7.2,
Ŷ1,ǫ ≥ c inf
‖µ‖≥ǫη/2
‖µ‖2ǫ1/2 ≥ cǫ
Next we apply Lemma 6.11 below [with s := t] to find that E[|Y1,ǫ|q] ≤ cǫq. This proves the
bounds in (6.11) and (6.12) that concern J2 and Y1,ǫ.
In order to derive the second bound in (6.11), we appeal to (4.4) once more to find that
‖ξ‖=1 ,‖µ‖≤ǫη/2
J1 ≥ Ŷ2,ǫ − Y2,ǫ,
where
Ŷ2,ǫ :=
‖µ‖≤ǫη/2
(λi − µi)σik(u(r, v))
G2s−r(y, v),
Y2,ǫ := 2 (W1 +W2 +W3) ,
where
W1 := sup
‖µ‖≤ǫη/2
µiGt−r(x, v)σik(u(r, v))
W2 := sup
‖ξ‖=1
(λi − µi)ai(k, r, v, s, y)
W3 := sup
‖µ‖≤ǫη/2
µiai(k, r, v, t, x)
Hypothesis P2 and Lemma 7.2 together yield
Ŷ2,ǫ ≥ cǫ1/2. (6.13)
Next, we apply the Cauchy–Schwarz inequality to find that
E [|W1|q] ≤ sup
‖µ‖≤ǫη/2
‖µ‖2q × E
(σik(u(r, v)))
G2t−r(x, v)
≤ cǫqη
dv G2t−r(x, v)
thanks to hypothesis P1. In light of this, Lemma 7.4 implies that E [|W1|q] ≤ cǫq(
In order to bound the q-th moment of |W2|, we use the Cauchy–Schwarz inequality
together with hypothesis P1, and write
E [|W2|q] ≤ sup
‖µ‖≤ǫη/2
‖λ− µ‖2q × E
a2i (k, r, v, s, y)
a2i (k, r, v, s, y)
We apply Lemma 6.11 below [with s := t] to find that E [|W2|q] ≤ cǫq.
Similarly, we find using Lemma 6.11 that
E [|W3|q] ≤ sup
‖µ‖≤ǫη/2
‖µ‖2q × E
a2i (k, r, v, t, x)
≤ cǫqη (t− s+ ǫ)q/2ǫq/2
≤ cǫq(
The preceding bounds for W1, W2, and W3 prove, in conjunction, that E[|Y2,ǫ|q] ≤
c2(q)ǫ
+η). This and (6.13) together prove the bounds in (6.11) and (6.12) that concern
J1 and Y2,ǫ, whence follows the result in Case 1.
Case 2. Now we work on the second case where |x − y|2 ≥ t − s ≥ 0. Let ǫ > 0 be such
that (1+α)ǫ1/2 < 1
|x− y|, where α > 0 is large but fixed; its specific value will be decided
on later. Then
ξTγZξ ≥ I1 + I2 + I3,
where
I1 :=
dv (S1 + S2)
I2 :=
dv (S1 + S2)
I3 :=
(t−ǫ)∨s
dvS 22 ,
S1 :=
(λi − µi) [Gs−r(y, v)σi,k(u(r, v)) + ai(k, r, v, s, y)] ,
S2 :=
µi [Gt−r(x, v)σik(u(r, v)) + ai(k, r, v, t, x)] .
From here on, Case 2 is divided into two further sub-cases.
Sub-Case A. Suppose, in addition, that ǫ ≥ t− s. In this case, we are going to prove that
‖ξ‖=1
ξTγZξ ≥ cǫ1/2 − Z1,ǫ, (6.14)
where for all q ≥ 1,
E [|Z1,ǫ|q] ≤ c(q)ǫ3q/4. (6.15)
Apply (4.4) to find that
Ã1 −B(1)1 −B
where
Ã1 :=
[(λi − µi)Gs−r(y, v) + µiGt−r(x, v)] σik(u(r, v))
1 := 4‖λ− µ‖2
a2i (k, r, v, s, y), (6.16)
1 := 4‖µ‖
a2i (k, r, v, t, x). (6.17)
Using the inequality
(a− b)2 ≥ 2
a2 − 2ab, (6.18)
we see that
Ã1 ≥
A1 −B(3)1 ,
where
A1 :=
(λi − µi)Gs−r(y, v)σik(u(r, v))
1 := 2
(λi − µi)Gs−r(y, v)σik(u(r, v))
µiGt−r(x, v)σik(u(r, v))
We can combine terms to find that
A1 −B(1)1 −B
We proceed in like manner for I2, but obtain slightly sharper estimates as follows. Owing
to (6.18),
A2 −B(1)2 −B
where
A2 :=
µiGt−r(x, v)σik(u(r, v))
2 := 2
µiGt−r(x, v)σik(u(r, v))
µiai(k, r, v, t, x)
2 := 2
µiGt−r(x, v)σik(u(r, v))
(λi − µi)ai(k, r, v, s, y)
2 := 2
µiGt−r(x, v)σik(u(r, v))
(λi − µi)Gs−r(y, v)σik(u(r, v))
Finally, we appeal to (4.4) to find that
A3 −B3,
where
A3 :=
(t−ǫ)∨s
µiGt−r(x, v)σik(u(r, v))
B3 := 2
(t−ǫ)∨s
µiai(k, r, v, t, x)
. (6.19)
By hypothesis P2,
A1 +A2 +A3 ≥ ρ2
‖λ− µ‖2
dv G2s−r(y, v)
+ ‖µ‖2
dv G2t−r(x, v)
+ ‖µ‖2
dv G2t−r(x, v)
Note that we have used the defining assumption of Sub-Case A, namely, that ǫ ≥ t − s.
Next, we group the last two integrals and apply Lemma 7.2 to find that
A1 +A2 +A3 ≥ c
‖λ− µ‖2ǫ1/2 + ‖µ‖2
dv G2t−r(x, v)
‖λ− µ‖2 + ‖µ‖2
≥ cǫ1/2.
(6.20)
We are aiming for (6.14), and propose to bound the absolute moments of B
1 , B
i = 1, 2, 3 and B3, separately. According to Lemma 6.11 below with s = t,
‖ξ‖=1
|B3|q
≤ c(q)ǫq. (6.21)
Next we bound the absolute moments of B
1 , i = 1, 2, 3. Using hypothesis P1 and
Lemma 6.11, with t = s, we find that for all q ≥ 1,
‖ξ‖=1
≤ cǫq. (6.22)
In the same way, we see that
‖ξ‖=1
≤ c(t− s+ ǫ)q/2ǫq/2. (6.23)
We are in the sub-case A where t− s ≤ ǫ. Therefrom, we obtain the following:
‖ξ‖=1
≤ cǫq. (6.24)
Finally, we turn to bounding the absolute moments of B
1 . Hypothesis P1 assures us
dv Gs−r(y, v)Gt−r(x, v)
dv Gs−r(y, v)Gt−r(x, v)
dr Gt+s−2r(x, y),
thanks to the semi-group property Walsh [W86, (3.6)] (see (6.44) below). This and Lemma
7.1 together prove that
t− s+ 2u
− |x− y|
2(t− s+ 2u)
t− s+ 2u
2(t− s+ 2u)
since |x−y| ≥ 2(1+α)ǫ1/2 ≥ αǫ1/2. Now we can change variables [z := 2(t−s+2u)/(α2ǫ)],
and use the bounds 0 ≤ t− s ≤ ǫ to find that
∣ ≤ cǫ1/2Ψ(α), where Ψ(α) := α
∫ 6/α2
z−1/2e−1/z dz. (6.25)
Following exactly in the same way, we see that
∣ ≤ cǫ1/2Ψ(α). (6.26)
We can combine (6.22), (6.24) as follows:
‖ξ‖=1
≤ c(q)ǫq. (6.27)
On the other hand, we will see in Lemmas 6.13 and 6.14 below that
‖ξ‖=1
≤ c(q)ǫ3q/4. (6.28)
Now, by (6.20), (6.21), (6.25), (6.26), (6.27) and (6.28),
‖ξ‖=1
ξTγZξ
A1 +A2 +A3
−B(3)1 −B
2 +B3
≥ c1ǫ1/2 − c2Ψ(α)ǫ1/2 − Z1,ǫ,
where Z1,ǫ := B
1 + B
1 + B
2 + B
2 + B3 satisfies E[|Z1,ǫ|q] ≤ c1(q)ǫ3q/4. Because
limν→∞Ψ(ν) = 0, we can choose and fix α so large that c2Ψ(α) ≤ c1/4 for the c1 and c2 of
the preceding displayed equation. This yields,
‖ξ‖=1
ξTγZξ ≥ cǫ1/2 − Z1,ǫ, (6.29)
as in (6.14) and (6.15).
Sub-Case B. In this final (sub-) case we suppose that ǫ ≤ t− s ≤ |x − y|2. Choose and fix
0 < ǫ < t− s. During the course of our proof of Case 1, we established the following:
‖ξ‖=1
ξTγZξ ≥ min
+η − Y1,ǫ , cǫ1/2 − Y2,ǫ
where
E [|Y1,ǫ|q] ≤ c(q)ǫq and E [|Y2,ǫ|q] ≤ c(q)ǫq(
See (6.11) and (6.12). Consider this in conjunction with (6.14) to find that for all 0 < ǫ <
(1 + α)−2|x− y|2,
‖ξ‖=1
ξTγZξ ≥ min
+η − Y1,ǫ , cǫ1/2 − Y2,ǫ − Z1,ǫ1{t−s<ǫ}
Because of this and (6.15), Proposition 3.5 implies that
‖ξ‖=1
ξTγZξ
)−2pd
≤ c|x− y|2(−2dp)(
≤ c(|t− s|1/2 + |x− y|)−2dp(1+2η).
This concludes the proof of Proposition 6.9(a).
If t = s, then Sub–Case B does not arise, and so we get directly from (6.29) and
Proposition 3.5 that
‖ξ‖=1
ξTγZξ
)−2pd
≤ c|x− y|−2dp.
This proves (b) and concludes the proof of Proposition 6.9.
Remark 6.10. If σ and b are constant, then ai = 0, so η can be taken to be 0. This gives
the correct upper bound in the Gaussian case, which shows that the method of proof of
Proposition 6.9 is rather tight.
We finally prove three results that we have used in the proof of Proposition 6.9.
Lemma 6.11. Assume P1. For all T > 0 and q ≥ 1, there exists a constant c = c(q, T ) ∈
(0,∞) such that for every 0 < ǫ ≤ s ≤ t ≤ T and x ∈ [0, 1],
a2i (k, r, v, t, x)
≤ c(t− s+ ǫ)q/2ǫq/2.
Proof. Define
a2i (k, r, v, t, x).
Use (4.3) to write
E [|A|q] ≤ c (E [|A1|q] + E [|A2|q]) ,
where
A1 :=
i,j,k=1
Gt−θ(x, η)D
r,v (σij(u(θ, η))) W
j(dθ, dη)
A2 :=
i,k=1
dη Gt−θ(x, η)D
r,v (bi(u(θ, η)))
We bound the q-th moment of A1 and A2 separately.
As regards A1, we apply the Burkholder inequality for Hilbert-space-valued martingales
(Lemma 7.6) to find that
E [|A1|q] ≤ c
i,j,k=1
, (6.30)
where
Θ := 1{θ>r}Gt−θ(x, η)
D(k)r,v (σij(u(θ, η)))
≤ c1{θ>r}Gt−θ(x, η)
D(k)r,v (ul(θ, η))
thanks to hypothesis P1. Thus,
E [|A1|q] ≤ c
dη G2t−θ(x, η)
∫ s∧θ
D(k)r,v (ul(θ, η))
We apply Hölder’s inequality with respect to the measure G2t−θ(x, η) dθ dη to find that
E [|A1|q] ≤ c
dη G2t−θ(x, η)
dη G2t−θ(x, η)
∫ s∧θ
(6.31)
where Υ :=
r,v (ul(θ, η)). Lemma 7.3 assures us that
dη G2t−θ(x, η)
≤ c(t− s+ ǫ)(q−1)/2. (6.32)
On the other hand, Lemma 7.5 implies that
∫ s∧θ
≤ cǫq/2,
where c ∈ (0,∞) does not depend on (θ, η, s, t, ǫ, x). Consequently,
dη G2t−θ(x, η)
∫ s∧θ
≤ cǫq/2
dη G2t−θ(x, η)
≤ cǫq/2(t− s+ ǫ)1/2.
(6.33)
Equations (6.31), (6.32), and (6.33) together imply that
E [|A1|q] ≤ c(t− s+ ǫ)q/2ǫq/2. (6.34)
This is the desired bound for the q-th moment of A1. Next we derive a similar bound for
A2. This will finish the proof. By the Cauchy–Schwarz inequality
E [|A2|q] ≤ c(t− s+ ǫ)q
i,k=1
where Φ := Gt−θ(x, η)|D(k)r,v (bi(u(θ, η))) |. From here on, the q-th moment of A2 is estimated
as that of A1 was; cf. (6.30), and this yields E[|A2|q] ≤ c(t− s+ ǫ)3q/2ǫq/2. This completes
the proof.
Remark 6.12. It is possible to prove that E[|A1|] is at least a constant times (t−s+ǫ)1/2ǫ1/2.
In this sense, the preceding result is not improvable.
Lemma 6.13. Assume P1. Fix T > 0 and q ≥ 1. Then there exists c = c(q, T ) such that
for all x ∈ [0, 1], 0 ≤ s ≤ t ≤ T , and ǫ ∈ (0, 1),
µ∈Rd: ‖µ‖≤1
≤ cǫ3q/4.
Proof. Define
2 (k, i) :=
dv Gt−r(x, v) |ai(k, r, v, t, x)| .
Then, by the Cauchy–Schwarz inequality,
2 (k, i)
≤ J1J2, (6.35)
where
J1 :=
dv G2t−r(x, v)
J2 := E
dv a2i (k, r, v, t, x)
On one hand, according to Lemma 7.4,
J1 ≤ c
(t− s+ ǫ)q/4
. (6.36)
On the other hand, Lemma 6.11 assures us that
J2 ≤ c′(t− s+ ǫ)q/4ǫq/4. (6.37)
By combining (6.35), (6.36), and (6.37), we find that E[|B̂(1)2 (k, i)|q ] ≤ cǫ3q/4 for a constant
c ∈ (0,∞) that does not depend on ǫ. By hypothesis P1,
µ∈Rd: ‖µ‖≤1
2 (k, i)
≤ cǫ3q/4, (6.38)
as asserted.
Lemma 6.14. Assume P1. Fix T > 0 and q ≥ 1. Then there exists c = c(q, T ) such that
for any x ∈ [0, 1], 0 ≤ s ≤ t ≤ T , and ǫ ∈ (0, 1),
ξ=(λ,µ)∈R2d : ‖ξ‖=1
≤ cǫ3q/4.
Proof. Define
2 (k, i) :=
dv Gt−r(x, v) |ai(k, r, v, s, y)| .
Then, by the Cauchy–Schwarz inequality,
2 (k, i)
≤ J1J2,
where
J1 :=
dv G2t−r(x, v)
J2 := E
dv a2i (k, r, v, s, y)
According to (6.36),
J1 ≤ c
(t− s+ ǫ)q/4
≤ c′ǫq/4.
On the other hand, Lemma 6.11, with t = s, assures us that J2 ≤ c′′ǫq/2. It follows that the
q-th absolute moment of B̂
2 (k, i) is at most cǫ
3q/4. An appeal to the triangle inequality
finishes the proof; see (6.38) where a similar argument was worked out in detail.
6.4.2 Large Eigenvalues
Proposition 6.15. Assume P1 and P2. Fix T > 0 and p > 1. Then there exists C =
C(p, T ) such that for all 0 ≤ s < t ≤ T with t− s < 1
, x, y ∈ (0, 1), x 6= y,
(ξi)TγZξ
where ξ1, ..., ξd are the vectors from Lemma 6.8.
Proof. Let ξ1, . . . , ξd, written as in (6.10), be such that α1 ≥ α0, . . . , αd ≥ α0 for some
α0 > 0. In order to simplify the exposition, we assume that 0 < α = α1 = · · · = αd ≤ 1,
since the general case follows along the same lines. Let 0 < ǫ < s ≤ t. As in the proof of
Proposition 6.9, we note first that
i=1(ξ
i)TγZξ
i is bounded below by
αλ̃iGs−r(y, v)
+ µ̃i
1− α2 (Gt−r(x, v) −Gs−r(y, v))
σik(u(r, v))
+ αλ̃iai(k, r, v, s, y)
+ µ̃i
1− α2 (ai(k, r, v, t, x) − ai(k, r, v, s, y))
s∨(t−ǫ)
1− α2 Gt−r(x, v)σik(u(r, v))
+ µ̃i
1− α2 ai(k, r, v, t, x)
(6.39)
We intend to use Proposition 3.5 with ε0 > 0 fixed, so we seek lower bounds for this
expression for 0 < ε < ε0.
Case 1. t− s ≤ ǫ. Then, by (4.4), the expression in (6.39) is bounded below by
(f1(s, t, ǫ, α, λ̃, µ̃, x, y) + f2(s, t, ǫ, α, λ̃, µ̃, x, y))− 2Iǫ,
where, from hypothesis P2,
f1 ≥ cρ2
αλ̃Gs−r(y, v) +
1− α2 µ̃(Gt−r(x, v) −Gs−r(y, v))
, (6.40)
f2 ≥ cρ2
s∨(t−ǫ)
1− α2 Gt−r(x, v)
, (6.41)
and Iǫ = I1,ǫ + I2,ǫ + I3,ǫ, where
I1,ǫ :=
αλ̃i − µ̃i
1− α2
ai(k, r, v, s, y)
I2,ǫ :=
1− α2 ai(k, r, v, t, x)
I3,ǫ :=
1− α2 ai(k, r, v, t, x)
There are obvious similarities between the terms I1,ǫ and B
1 in (6.16). Thus, we apply
the same method that was used to bound E[|B(1)1 |q] to deduce that E[|I1,ǫ|q] ≤ c(q)ǫq. Since
I2,ǫ is similar to B
1 from (6.17) and t− s ≤ ǫ, we see using (6.24) that E[|I2,ǫ|q] ≤ c(q)ǫq.
Finally, using the similarity between I3,ǫ and B3 in (6.19), we see that E[|I3,ǫ|q] ≤ c(q)ǫq.
We claim that there exists α0 > 0, ǫ0 > 0 and c0 > 0 such that
f1 + f2 ≥ c0
ǫ for all α ∈ [α0, 1], ǫ ∈ (0, ǫ0], s, t ∈ [1, 2], x, y ∈ [0, 1]. (6.42)
This will imply in particular that for ǫ ≥ t− s,
i ≥ c0ǫ1/2 − 2Iǫ,
where E[|Iǫ|q] ≤ c(q)ǫq.
In order to prove (6.42), first define
pt(x, y) := (4πt)
−1/2e−(x−y)
2/(4t).
In addition, let g1(s, t, ǫ, α, λ̃, µ̃, x, y) and g2(s, t, ǫ, α, λ̃, µ̃, x, y) be defined by the same ex-
pressions as the right-hand sides of (6.40) and (6.41), but with Gs−r(x, v) replaced by
ps−r(x− v), and
replaced by
Observe that g1 ≥ 0, g2 ≥ 0, and if g1 = 0, then for all v ∈ Rd,
∥αps−r(y − v)λ̃+
1− α2 (pt−r(x− v)− ps−r(y − v))µ̃
∥ = 0. (6.43)
If, in addition, λ̃ = µ̃, then we get that for all v ∈ Rd,
1− α2
ps−r(y − v) +
1− α2pt−r(x− v) = 0.
We take Fourier transforms to deduce from this that for all ξ ∈ Rd,
1− α2
eiξy = −
1− α2eiξxe(s−t)ξ2 .
If x = y, then it follows that s = t and α −
1− α2 = −
1− α2. Hence, if α 6= 0, x = y
and λ̃ = µ̃, then g1 > 0. We shall make use of this observation shortly.
Because ‖λ̃‖ = ‖µ̃‖ = 1, f1 is bounded below by
α2G2s−r(y, v) +
1− α2
(Gt−r(x, v) −Gs−r(y, v))2
1− α2Gs−r(y, v)(Gt−r(x, v)−Gs−r(y, v))(λ̃ · µ̃)
= cρ2
1− α2
G2s−r(y, v)) +
1− α2
G2t−r(x, v)
1− α2
1− α2Gs−r(y, v)Gt−r(x, v)
1− α2Gs−r(y, v)(Gt−r(x, v)−Gs−r(y, v))(λ̃ · µ̃− 1)
Recall the semigroup property
dv Gs−r(y, v)Gt−r(x, v) = Gs+t−2r(x, y) (6.44)
(see Walsh [W86, (3.6)]). We set h := t− s and change variables [r̄ := s− r] to obtain the
following bound:
f1 ≥ cρ2
1− α2
G2r(y, y) +
1− α2
G2h+2r(x, x)
1− α2
1− α2Gh+2r(x, y)
1− α2(Gh+2r(x, y)−G2r(y, y))
λ̃ · µ̃− 1
Recall ([W86, p.318]), that
Gt(x, y) = pt(x, y) +Ht(x, y),
where Ht(x, y) is a continuous function that is uniformly bounded over (t, x, y) ∈ (0,∞)×
(0, 1) × (0, 1). Therefore, f1 ≥ cρ2g̃1 − cǫ, where
g̃1 := g̃1(h, ǫ, α, λ̃, µ̃, x, y)
1− α2
p2r(y, y) +
1− α2
p2h+2r(x, x)
1− α2
1− α2ph+2r(x, y)
1− α2 (ph+2r(x, y)− p2r(y, y))
λ̃ · µ̃− 1
We can recognize that
ph+2r(x, y)− p2r(y, y) =
exp(−(x− y)2/(4(h + 2r)))
4π(h + 2r)
4π(2r)
Also, λ̃ · µ̃− 1 ≤ 0. Thus,
g̃1 ≥ ĝ1,
where
ĝ1 := ĝ1(h, ǫ, α, x, y)
1− α2
p2r(y, y) +
1− α2
p2h+2r(x, x)
1− α2
1− α2ph+2r(x, y)
Therefore,
ĝ1 =
1− α2)2 1√
1− α2
8π(h + r)
+ 2(α −
1− α2)
1− α2ph+2r(x, y)
On the other hand, by (6.44) above,
∫ ǫ∧(t−s)
1− α2
G2r(y, y)
≥ g̃2 :=
∫ ǫ∧h
1− α2
p2r(y, y)− Cǫ
1− α2
ǫ ∧ h− Cǫ.
Finally, we conclude that
f1 + f2 ≥ ĝ1 + g̃2 − 2Cǫ
1− α2
1− α2√
h+ ǫ−
1− α2
1− α2
dr ph+2r(x, y)
1− α2√
ǫ ∧ h− 2Cǫ.
Now we consider two different cases.
Case (i). Suppose α−
1− α2 ≥ 0, that is, α ≥ 2−1/2. Then
ǫ−1/2 (ĝ1 + g̃2) ≥ φ1
− 2Cǫ1/2,
where
φ1(α , z) :=
1− α2
1− α2
1 + z +
1− α2
1 ∧ z
Clearly,
α≥2−1/2
φ1(α, z) ≥ inf
α>2−1/2
1− α2
1− α2
> φ0 > 0.
Thus,
α≥2−1/2, h≥0, 0<ǫ≤ǫ0
ǫ−1/2 (ĝ1 + g̃2) > 0.
Case (ii). Now we consider the case where α −
1− α2 < 0, that is, α < 2−1/2. In this
case,
ǫ−1/2 (ĝ1 + g̃2) ≥ ψ1
− 2Cǫ1/2,
where
ψ1(α , z) :=
1− α2
1− α2
1 + z +
1− α2 − α
1− α2
2 + z +
1− α2
1 ∧ z
Note that ψ1(α, z) > 0 if α 6= 0. This corresponds to the observation made in the lines
following (6.43). Moreover, for α ≥ α0 > 0, limz↓0 ψ1(α, z) ≥ (2π)−1/2α20, and
ψ1(α , z) ≥ inf
1− α2)2 +
1− α2
Therefore,
α∈[α0,2−1/2], z≥0
ψ1(α , z) > 0.
This concludes the proof of the claim (6.42).
Case 2. t− s > ǫ. In accord with (6.39), we are interested in
1≥α≥α0
i := min(E1,ǫ, E2,ǫ),
where
E1,ǫ := inf
α0≤α≤
E2,ǫ := inf√
1−ǫη≤α≤1
Clearly,
E1,ǫ ≥
f2 − 2I3,ǫ.
Since α ≤
1− ǫη is equivalent to
1− α2 ≥ ǫη/2, we use hypothesis P2 to deduce that
f2 ≥ cρ2ǫη
dv G2t−r(x, v) ≥ cρ2ǫ
Therefore,
E1,ǫ ≥ cρ2ǫ
+η − I3,ǫ,
and we have seen that I3,ǫ has the desirable property E [|I3,ǫ|q] ≤ c(q)ǫq.
In order to estimate E2,ǫ, we observe using (6.39) that
E2,ǫ ≥
f̃1 − J̃1,ǫ − J̃2,ǫ − J̃3,ǫ − J̃4,ǫ,
where
f̃1 ≥ α2
λ̃iσik(u(r, v))
G2s−r(y, v),
J̃1,ǫ = 2
1− α2
µ̃iσik(u(r, v))
G2t−r(x, v),
J̃2,ǫ = 2
1− α2
µ̃iσik(u(r, v))
G2s−r(y, v),
J̃3,ǫ = 2
αλ̃i − µ̃i
1− α2
ai(k, r, v, s, y)
J̃4,ǫ = 2
1− α2
µ̃iai(k, r, v, t, x)
Because α2 ≥ 1−ǫη and ǫ ≤ t−s ≤ 1
, hypothesis P2 and Lemma 7.2 imply that f̃1 ≥ cǫ1/2.
On the other hand, since 1−α2 ≤ ǫη, we can use hypothesis P1 and Lemma 7.4 to see that
J̃1,ǫ
≤ c(q)ǫqηǫq/2 = c(q)ǫ(
+η)q,
and similarly, using Lemma 7.3, E[|J̃2,ǫ|q] ≤ c(q)ǫ(
+η)q. The term J̃3,ǫ is equal to 2I1,ε, so
E[|J̃3,ǫ|q] ≤ cεq, and J̃4,ε is similar to B(2)1 from (6.17), so we find using (6.23) that
J̃4,ǫ
≤ cǫqη(t− s+ ǫ)q/2ǫq/2 ≤ cǫ(
+η)q.
We conclude that when t − s > ǫ, then E2,ǫ ≥ cǫ1/2 − J̃ǫ, where E[|J̃ǫ|q] ≤ c(q)ǫ(
+η)q.
Therefore, when t− s > ǫ,
1≥α≥α0
i ≥ min
+η − I3,ǫ , cǫ
2 − J̃ǫ
Putting together the results of Case 1 and Case 2, we see that for 0 < ǫ ≤ 1
‖ξ‖=1, 1≥α≥α0
i ≥ min
+η − I3,ǫ, cǫ
2 − 2Iǫ1{ǫ≥t−s} − J̃ǫ1{ǫ<t−s}
We take into account the bounds on moments of I3,ǫ, Iǫ and J̃ǫ, and then use Proposition
3.5 to conclude the proof of Proposition 6.15.
7 Appendix
On several occasions, we have appealed to the following technical estimates on the Green
kernel of the heat equation.
Lemma 7.1. [BP98, (A.1)] There exists C > 0 such that for any 0 < s < t and x, y ∈ [0, 1],
x 6= y,
Gt−s(x, y) ≤ C
2π(t− s)
−|x− y|
2(t− s)
Lemma 7.2. [BP98, (A.3)] There exists C > 0 such that for any t ≥ ǫ > 0 and x ∈ [0, 1],
G2t−s(x, y)dyds ≥ C
Lemma 7.3. [BP98, (A.5)] There exists C > 0 such that for any ǫ > 0, q < 3
, t ≥ ǫ and
x ∈ [0, 1],
t−s(x, y)dyds ≤ Cǫ3/2−q.
Lemma 7.4. There exists C > 0 such that for all 0 < a < b and x ∈ [0, 1],
G2s(x, y) dyds ≤ C
b− a√
Proof. Using Lemma 7.1 and the change of variables z =
, we see that
G2s(x, y) dyds ≤ C
ds = 2C̃(
which concludes the proof.
The next result is a straightforward extension to d ≥ 1 of Morien [M98, Lemma 4.2]
for d = 1.
Lemma 7.5. Assume P1. For all q ≥ 1, T > 0 there exists C > 0 such that for all
T ≥ t ≥ s ≥ ǫ > 0 and 0 ≤ y ≤ 1,
D(k)r,v (ui(t, y))
 ≤ Cǫq/2.
The next result is Burkholder’s inequality for Hilbert-space-valued martingales.
Lemma 7.6. [BP98, eq.(4.18)] Let Hs,t be a predictable L
2(([0, t]× [0, 1])m)-valued process,
m ≥ 1. Then, for any p > 1, there exists C > 0 such that
([0,t]×[0,1])m
Hs,y(α)W (dy, ds)
([0,t]×[0,1])m
H2s,y(α)dα
Acknowledgement. The authors thank V. Bally for several stimulating discussions.
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	Introduction and main results
	Proof of Theorems 1.2, 1.6 and their corollaries (assuming Theorem 1.1)
	Elements of Malliavin calculus
	Existence, smoothness and uniform boundedness of the one-point density
	The Gaussian-type lower bound on the one-point density
	The Gaussian-type upper bound on the two-point density
	Bounds on the increments of the Malliavin derivatives
	Study of the Malliavin matrix
	Proof of Theorem 1.1(c) and (d)
	Proof of Theorem 6.3
	Small Eigenvalues
	Large Eigenvalues
	Appendix
