Guided proposals for simulating multi-dimensional diffusion bridges


Full text



Guided proposals for simulating

multi-dimensional diffusion bridges

M O R I T Z S C H AU E R1, F R A N K VA N D E R M E U L E N2and H A R RY VA N Z A N T E N3

1Mathematical Institute, Leiden University, P.O. Box 9512, 2300 RA Leiden, The Netherlands.

2Delft Institute of Applied Mathematics (DIAM), Delft University of Technology, Mekelweg 4, 2628 CD Delft, The Netherlands.

3Korteweg-de Vries Institute for Mathematics, University of Amsterdam, P.O. Box 94248, 1090 GE Amster-dam, The Netherlands.

A Monte Carlo method for simulating a multi-dimensional diffusion process conditioned on hitting a fixed point at a fixed future time is developed. Proposals for such diffusion bridges are obtained by superimposing an additional guiding term to the drift of the process under consideration. The guiding term is derived via approximation of the target process by a simpler diffusion processes with known transition densities. Acceptance of a proposal can be determined by computing the likelihood ratio between the proposal and the target bridge, which is derived in closed form. We show under general conditions that the likelihood ratio is well defined and show that a class of proposals with guiding term obtained from linear approximations fall under these conditions.

Keywords:change of measure; data augmentation; linear processes; multidimensional diffusion bridge

1. Introduction

1.1. Diffusion bridges

SupposeXis ad-dimensional diffusion with time dependent driftb:R+×Rd→Rdand disper-sion coefficientσ:R+×Rd→Rd×d governed by the stochastic differential equation (SDE)

dXt=b(t, Xt)dt+σ (t, Xt)dWt, X0=u, (1.1) whereW is a standardd-dimensional Brownian motion. When the processXis conditioned to hit a pointv∈Rdat timeT >0, the resulting processXon[0, T]is called thediffusion bridge fromu to v. In this paper, we consider the problem of simulating realizations of this bridge process. Since we are conditioning on an event of probability zero and in general no closed form expression for the transition densities of the original processX or the bridgeX exist, this is known to be a difficult problem.

This problem arises for instance, when making statistical inference for diffusion models from discrete-time, low-frequency data. In that setting, the fact that the transition densities are unavail-able implies that the likelihood of the data is not accessible. A successful approach initiated by


Roberts and Stramer [21] is to circumvent this problem by viewing the continuous segments be-tween the observed data points as missing data. Computational algorithms can then be designed that augment the discrete-time data by (repeatedly) simulating the diffusion bridges between the observed data points. This statistical application of simulation algorithms for diffusion bridges was our initial motivation for this work. The present paper however focusses on the simulation problem as such and can have other applications as well.

The simulation of diffusion bridges has received much attention over the past decade, see for instance the papers Elerianet al.[11], Eraker [12], Roberts and Stramer [21], Durham and Gallant [10], Stuart et al.[22], Beskos and Roberts [5], Beskos et al.[3], Beskos et al. [4], Fearnhead [13], Papaspiliopoulos and Roberts [20], Linet al.[17], Bladt and Sørensen [6], Bayer and Schoenmakers [2] to mention just a few. Many of these papers employ accept-reject-type methods. The common idea is that while sampling directly from the lawPof the bridge process X is typically impossible, sampling from an equivalent law P◦of some proposal process X◦ might in fact be feasible. If this proposal is accepted with an appropriately chosen probability, depending on the Radon–Nikodym derivative(dP/dP◦)(X), then either exact or approximate draws from the target distributionP can be generated. Importance sampling and Metropolis– Hastings algorithms are the prime examples of methods of this type.

To be able to carry out these procedures in practice, simulating paths from the proposal process has to be relatively easy and, up to a normalizing constant, an expression for the derivative (dP/dP◦)(X)has to be available that is easy to evaluate. The speed of the procedures greatly depends on the acceptance probability, which in turn depends on (dP/dP◦)(X). This can be influenced by working with a cleverly chosen proposal processX◦. A naive choice might result in a proposal process that, although its law is equivalent to that of the target bridgeX, has sample paths that are with considerable probability rather different from those ofX. This then results in small ratios(dP/dP◦)(X)with large probability, which in turn leads to small acceptance probabilities and hence to a slow procedure. It is therefore desirable to have proposals that are “close” to the target in an appropriate sense. In this paper, we construct such proposals for the multi-dimensional setting.

1.2. Guided proposals

We will consider so-called guided proposals, according to the terminology suggested in Pa-paspiliopoulos and Roberts [20]. This means that our proposals are realizations of a processX◦ that solves an SDE of the form (1.1) as well, but with a drift term that is adapted in order to force the processX◦to hit the pointvat timeT.

An early paper suggesting guided proposals is Clark [8] (a paper that seems to have received little attention in the statistics community). Clark [8] considers the cased =1 andσ constant and advocates using proposals from the SDE dXt◦=b(Xt)dt+



using proposalsXsatisfying the SDE

dXt =

bt, Xt +vX

t Tt

dt+σt, Xt dWt. ()

This considerably complicates proving that the laws ofX◦and the target bridgeXare absolutely continuous. Further, Delyon and Hu [9] consider the alternative proposalsXsatisfying the SDE

dXt =vX

t Tt dt+σ

t, Xt dWt, ()

where the original drift ofXis disregarded. This is a popular choice in practice especially with a discretization scheme known as the Modified Brownian Bridge. Both proposals have their individual drawbacks, see Section1.3.

Another important difference is that they consider the multi-dimensional case. With more degrees of freedom a proposal process that is not appropriately chosen has a much higher chance of not being similar to the target process, leading to very low acceptance probabilities and hence slow simulation procedures. In higher dimensions, the careful construction of the proposals is even more important for obtaining practically feasible procedures than in dimension one.

Our approach is inspired by the ideas in Clark [8] and Delyon and Hu [9]. However, we pro-pose to adjust the drift in a different way, allowing more flexibility in constructing an appropriate guiding term. This is particularly aimed at finding procedures with higher acceptance probabil-ities in the multi-dimensional case. To explain the approach in more detail we recall that, under weak assumptions the target diffusion bridgeXis characterized as the solution to the SDE

dXt =bt, Xtdt+σt, XtdWt, X0=u, t∈ [0, T ), () where

b(t, x)=b(t, x)+a(t, x)∇xlogp(t, x;T , v) () anda(t, x)=σ (t, x)σ(t, x). In the bridge SDE the terma(t, x)∇xlogp(t, x;T , v)is added to the original drift to directXtowardsvfrom the current positionXt =xin just the right manner. Since equation () contains the unknown transition densities of the original processXit cannot be employed directly for simulation. We propose to replace this unknown density by one coming from an auxiliary diffusion process with known transition densities. So the proposal process is going to be the solutionX◦of the SDE

dX◦t =bt, Xtdt+σt, XtdWt, X0◦=u, (◦) where


of the proposal processX◦and secondly, we have additional freedom since we can choose the processX.˜

The paper contains two main theoretical results. In the first, we give conditions under which the processX◦is indeed a valid proposal process in the sense that its distributionP◦(viewed as Borel measure onC([0, T],Rd)) is equivalent to the lawPof the target processX and we derive an expression for the Radon–Nikodym derivative of the form

dP dP◦




Gs, Xs◦ds


where the functional G does not depend on unknown or inaccessible objects. In the second theorem, we show that the assumptions of the general result are fulfilled if in (◦◦) we choose the transition densityp˜of a processX˜ from a large class oflinear processes. This is a suitable class, since linear processes have tractable transition densities.

1.3. Comparison of proposals

Numerical experiments presented van der Meulen and Schauer [23] show that our approach can indeed substantially increase acceptance rates in a Metropolis–Hastings sampler, especially in the multi-dimensional setting. Already in a simple one-dimensional example however we can illustrate the advantage of our method.

Consider the solutionXof the SDE,


2dWt, X0=u withb(x)=β1−β2sin(8x).

The corresponding bridgeXis obtained by conditioningXto hit the pointv∈Rat timeT >0. We takeu=0, v=π2 and consider either the caseβ1=β2=2 orβ1=2, β2=0. We want to compare the three mentioned proposals (), () and (◦) in these two settings. The driftbsatisfies the assumptions for applying the Exact Algorithm of Beskos and Roberts [5], but numerical experiments revealed the rejection probability is close to 1 in this particular example. Besides, our main interest lies in comparing proposals that are suited for simulating general diffusion bridges in the multivariate case as well. A simple choice for the guided proposal (◦) is obtained by taking


Xto be a scaled Brownian motion with constant driftϑ. This givesb(s, x)=b(x)+vTxsϑ as the drift of the corresponding guided proposal. Here we can choose ϑ freely. In fact, far more flexibility can be obtained by choosingX˜ a linear process as in Theorem2. In particular, we could take ϑ to depend on t, resulting in an infinite dimensional class of proposals. For illustration purposes, in this example, we show that just taking a scaled Brownian motion with constant driftϑforX˜ is already very powerful.


Figure 1. Samples from the true distribution of the bridge compared to different proposals for the ex-ample b(x)=2−2 sin(8x). Top row: True bridge, proposals with drift b(t, x)=b(x)+ vTxt and

b(t, x)=vTxt. Bottom row:b(s, x)=b(x)+vTxtϑfor different values ofϑ. The top-middle figure and bottom-left figure coincide.

Now ifβ2=2, both () and () fail to capture the true dynamics of (). Roughly speaking, for () the proposals fail to capture the multimodality of the marginal distributions of the true bridge, while proposals with () arrive at values close tovtoo early due to the mismatch between pulling term and drift. On the other hand, the proposals (◦) can be quite close to the target bridge for good choices ofϑ, see Figure1. Two effects are in place: incorporating the true drift into the proposal results in the correct local behaviour of the proposal bridge (multimodality in this particular example). Further, an appropriate choice ofϑreduces the mismatch between the drift part and guiding part of the proposal. The additional freedom in (◦) by choice ofϑ will be especially useful, if one can find good values forϑ in a systematic way. We now explain how this can be accomplished.

LetP◦ϑ denote the law of X◦. One option to choose ϑ in a systematic way is to take the information projectionP◦ϑopt defined by

ϑopt=arg min ϑ


P Pϑ


Figure 2. Left: trace plot ofϑusing the stochastic gradient descent algorithm. Right:ϑDKL(P P◦ϑ), estimated with 100,000 simulated bridges.

Here, the Kullback–Leibler divergence is given by


P Pϑ



dP dP◦ϑ


This is a measure how much information is lost, whenP◦ϑ is used to approximateP. This ex-pression is not of much direct use, as it depends on the unknown measureP. However, given a sampleX◦fromP◦ϑ

0 using a reference parameterϑ0, the gradient ofDKL(P


ϑ)can be ap-proximated by







This in turn can be used in an iterative stochastic gradient descent algorithm (details are given in theAppendix). The valueϑ=1.36 used in Figure1was obtained in this way. From the trace plot of the gradient descent algorithm displayed in Figure2it appears the algorithm settles near the optimal value shown in the right-hand figure.

1.4. Contribution of this paper


Furthermore, the dispersion coefficientσ does not need to be invertible. In a companion paper (van der Meulen and Schauer [23]), we show how guided proposals can be used for Bayesian estimation of discretely observed diffusions.

1.5. Organization

The main results of the paper are presented in Section2. Proofs are given in Sections3–6.

1.6. General notations and conventions

1.6.1. Vector- and matrix norms

The transpose of a matrixAis denoted byA. The determinant and trace of a square matrixA are denoted by|A|and tr(A), respectively. For vectors, we will always use the Euclidean norm, which we denote by x . For ad ×d matrix A, we denote its Frobenius norm by A F = (di=1jd= 1A2ij)1/2. The spectral norm, the operator norm induced by the Euclidean norm will de denoted by A , so

A =sup Ax , x∈Rdwith x =1 .

Both norms are submultiplicative, AxA F x and AxA x . The identity matrix will be denoted by Id.

1.6.2. Derivatives

Forf:RmRn, we denote by Df them×n-matrix with element(i, j )given by Dijf (x)= (∂fj/∂xi)(x). Ifn=1, then Df is the column vector containing all partial derivatives off, that is∇xf from the first section. In this setting, we write theith element of Df by Dif (x)= (∂f /∂xi)(x) and denote D2f =D(Df ) so that D2ijf (x)=2f (x)/(∂xi∂xj). If x ∈Rn and A∈Rn×ndoes not depend onx, then D(Ax)=A. Further, forf:Rn→Rnwe have

Df (x)Af (x)=Df (x)A+Af (x). Derivatives with respect to time are always denoted as∂/∂t.

1.6.3. Inequalities

We writexyto denote that there is a universal (deterministic) constantC >0 such thatxCy.

2. Main results

2.1. Setup


Lipschitz in both arguments, satisfy a linear growth condition in their second argument and that σ is uniformly bounded. These conditions imply in particular that the SDE has a unique strong solution (e.g., Karatzas and Shreve [16]). The auxiliary processX˜ whose transition densities are used in the proposal process is defined as the solution of an SDE like (1.1) as well, but with drift


binstead ofband dispersionσ˜ instead ofσ. The functionsb˜andσ˜ are assumed to satisfy the same Lipschitz, linear growth and boundedness conditions asb andσ. We writea=σ σand

˜ a= ˜σσ˜.

The processes X and X˜ are assumed to have smooth transition densities with respect to Lebesgue measure. More precisely, denoting the law of the process X started in x at time s byP(s,x), we assume that for 0≤s < tandy∈Rd

P(s,x)(Xtdy)=p(s, x;t, y)dy

and similarly for the processX, whose transition densities are denoted by˜ p˜instead ofp. The infinitesimal generators ofXandX˜ are denoted byLandL, respectively, so that˜

(Lf )(s, x)= d


bi(s, x)Dif (s, x)+ 1 2



aij(s, x)D2ijf (s, x), (2.1)

forfC1,2(R×Rd,R), and similarly forL˜(withb˜anda). Under regularity conditions, which˜ we assume to be fulfilled, we have that the transition densities ofX˜ satisfy Kolmogorov’s back-ward equation:

∂sp(s, x˜ ;t, y)+(L˜p)(s, x˜ ;t, y)=0

(hereL˜acts ons, x). (See, for instance, Karatzas and Shreve [16], page 368, for sufficient regu-larity conditions.)

We fix a time horizonT >0 and a pointv∈Rd such that for allsT andxRd it holds thatp(s, x;T , v) >0 andp(s, x˜ ;T , v) >0. The target bridge processX=(X

t :t∈ [0, T])is defined by conditioning the original processXto hit the pointvat timeT. The proposal process X◦=(Xt◦:t∈ [0, T])is defined as the solution of (◦)–(◦◦). In the results ahead, we will impose conditions on the transition densitiesp˜ofX˜ that imply that this SDE has a unique solution. All processes are assumed to be defined on the canonical path space and(Ft)is the corresponding canonical filtration.

For easy reference, the following table briefly describes the various processes around. X original, unconditioned diffusion process

X corresponding bridge, conditioned to hitvat timeT, defined through () X◦ proposal process defined through (◦)


X auxiliary process whose transition densitiesp˜appear in the definition ofX◦.


2.2. Main results

The end-timeT and the end-pointv of the conditioned diffusion will be fixed throughout. To emphasize the dependence of the transition density on the first two arguments and to shorten notation, we will often write

p(s, x)=p(s, x;T , v).

Motivated by the guiding term in the drift ofX(see ()), we further introduce the notations R(s, x)=logp(s, x), r(s, x)=DR(s, x), H (s, x)= −D2R(s, x).

Here D acts onx. Similarly the functionsR,˜ r˜andH˜ are defined by starting with the transition densitiesp˜in the place ofp.

The following proposition deals with the laws of the processesX,X◦andX on the interval [0, t]fort < T (strict inequality is essential). Equivalence of these laws is clear from Girsanov’s theorem. The proposition gives expressions for the corresponding Radon–Nikodym derivatives, which are derived using Kolmogorov’s backward equation. The proof of this result can be found in Section3.

Proposition 1. Assume for allx, y∈Rdandt∈ [0, T ) r(t, x)˜ 1+ xv

Tt , r(t, y)˜ − ˜r(t, x) yx

Tt . (2.2)

Define the processψby

ψ (t )=exp



Gs, Xs◦ds

, t < T , (2.3)


G(s, x)=b(s, x)− ˜b(s, x)r(s, x)˜

−1 2tr

a(s, x)− ˜a(s, x)H (s, x)˜ − ˜r(s, x)r(s, x)˜ .

Then fort∈ [0, T )the lawsPt,P◦t andPt are equivalent and we have dPt


X◦= p(0, u˜ ;T , v) ˜

p(t, Xt◦;T , v)ψ (t ),

(2.4) dPt


X◦=p(0, u˜ ;T , v) p(0, u;T , v)

p(t, Xt;T , v) ˜

p(t, Xt;T , v)ψ (t ).


of measures on the whole interval[0, T]and that dPT


X◦=p(0, u˜ ;T , v)

p(0, u;T , v)ψ (T ). (2.5) Asψ (T )does not depend onp, samples from X◦ can then be used as proposals forX in a Metropolis–Hastings sampler, for instance. Numerical evaluation ofψ (T )is somewhat simpli-fied by the fact that no stochastic integral appears in its expression. To establish (2.5), we need to put appropriate conditions on the processesXandX˜ that allow us to control the behaviour of the bridge processesX∗andX◦near timeT.

Assumption 1. For the auxiliary processX,˜ we assume the following:

(i) For all bounded,continuous functionsf:[0, T] ×Rd→Rthe transition densitiesp˜of

˜ Xsatisfy

lim tT

f (t, x)p(t, x˜ ;T , v)dx=f (T , v). (2.6)

(ii) For allx, y∈Rdandt∈ [0, T ),the functionsr˜andH˜ satisfy

r(t, x)˜ 1+ xv (Tt )−1, r(t, x)˜ − ˜r(t, y) yx (Tt )−1,

H (t, x)˜ (Tt )−1+ xv (Tt )−1.

(iii) There exist constants ,˜ C >˜ 0such that for0< s < T, ˜

p(s, x;T , v)≤ ˜C(Ts)d/2exp

− ˜ vx 2 Ts

uniformly inx.

Roughly speaking, Assumption1requires that the processX, which we choose ourselves, is˜ sufficiently nicely behaved near timeT.

Assumption 2. ForM >1andu≥0definegM(u)=max(1/M,1−Mu).There exist constants , C >0,M >1and a functionμt(s, x):{s, t: 0stT}×Rd→Rdwith μt(s, x)x < M(ts) x and μt(s, x) 2≥gM(ts) x 2,so that for alls < tT andx, y∈Rd,

p(s, x;t, y)C(ts)d/2exp

yμt(s, x) 2 ts



Assumption 3. There exist anε(0,1/6)and an a.s.finite random variableMsuch that for all

t∈ [0, T],it a.s.holds that

XtvM(Tt )1/2−ε.

This third assumption requires that the proposal processX◦does not only converge tov as tT, as it obviously should, but that it does so at an appropriate speed. A requirement of this kind cannot be essentially avoided, since in general two bridges can only be equivalent if they are pulled to the endpoint with the same force. Theorem2below asserts that this assumption holds in caseX˜ is a linear process, provided its diffusion coefficient coincides with that of the process Xat the final timeT.

We can now state the main results of the paper.

Theorem 1. Suppose that Assumptions1,2and3hold and thata(T , v)˜ =a(T , v).Then the laws of the bridgesXandXare equivalent on[0, T]and(2.5)holds,withψas in Proposition1.

We complement this general theorem with a result that asserts, as already mentioned, that Assumptions1and3hold for a class of processesX˜ given by linear SDEs.

Theorem 2. AssumeX˜ is a linear process with dynamics governed by the stochastic differential equation

dX˜t= ˜B(t )X˜tdt+ ˜β(t)dt+ ˜σ (t )dWt, (2.7)

for non-random matrix and vector functionsB,˜ β˜andσ˜.

(i) IfB˜ andβ˜ are continuously differentiable on[0, T],σ˜ is Lipschitz on [0, T]and there exists anη >0such that for alls∈ [0, T]and ally∈Rd,

ya(s)y˜ ≥η y 2,

thenX˜ satisfies Assumption1.

(ii) Suppose moreover thata(T )˜ =a(T , v),that there exists anε >0such that for all s∈ [0, T],x∈RdandyRd

ya(s, x)yε y 2, (2.8)

and thatbis of the formb(s, x)=B(s, x)x+β(s, x),whereBis a bounded matrix-valued func-tion andβis a bounded vector-valued function.Then there exists an a.s.finite random variable

Msuch that,a.s.,


(Tt )log log

1 Tt +e


Remark 1. Extending absolute continuity ofXandX◦on[0, T −ε](ε >0) to absolute con-tinuity on[0, T]is a subtle issue. This can already be seen from a very simple example in the one-dimensional case. Supposed=d=1,v=0,b≡0 andσ (t, x)≡1. That is,Xis the law of a Brownian bridge from 0 at time 0 to 0 at timeT satisfying the stochastic differential equation

dXt = − X t


Suppose we takeX˜t= ˜σdWt, so thatX◦satisfies the stochastic differential equation

dX◦t = − 1 ˜ σ2



It is a trivial fact thatX◦andXare absolutely continuous on[0, T]ifσ˜ =1 (this also follows from Theorem2). It is natural to wonder whether this condition is also necessary. The answer to this question is yes, as we now argue. Lemma 6.5 in Hida and Hitsuda [15] gives a general result on absolute continuity of Gaussian measures. From this result, it follows that X◦ andX are absolutely continuous on[0, T]if and only if for the symmetrized Kullback–Leibler divergences


logdP t dP◦t



t dPt


it holds that supt∈[0,T )dt <∞. We consider the second term. Denotingα=1/σ˜2, Girsanov’s theorem gives


t dPt




(1α) X


TsdWs+ 1 2





2 ds.

By Itô’s formula Xt

Tt =(1α) t

0 Xs (Ts)2ds+

t 0 −

1 TsdWs. This is a linear equation with solution


Tt = −(Tt )

−1+α t





Xt Tt


=(Tt )−2+2α t



Fort < T,0tE[( XsTs)

2]ds <∞, soE[t 0


TsdWs] =0. Therefore, E


t dPt

X◦=1 2−1)

2 t


(Ts)−2+2α s


(Tτ )−2αdτds.


Remark 2. For implementation purposes integrals in likelihood ratios and solutions to stochastic differential equations need to be approximated on a finite grid. This is a subtle numerical issue as the drift of our proposal bridge has a singularity near its endpoint. In a forthcoming work van der Meulen and Schauer [23] we show how this problem can be dealt with. The main idea in there is the introduction of a time-change and space-scaling of the proposal process that allows for numerically accurate discretisation and evaluation of the likelihood.

3. Proof of Proposition


We first note that by equation (2.2),r˜is Lipschitz in its second argument on[0, t]and satisfies a linear growth condition. Hence, a unique strong solution of the SDE forX◦exists.

By Girsanov’s theorem (see, e.g., Liptser and Shiryaev [18]) the laws of the processesXand X◦on[0, t]are equivalent and the corresponding Radon–Nikodym derivative is given by

dPt dP◦t




βsdWs−1 2



βs 2ds


where W is a Brownian motion under P◦t and βs =β(s, Xs) solves σ (s, Xs)β(s, Xs)= b(s, Xs)b(s, Xs). (Here we lightened notation by writingβs instead ofβ(s, Xs). In the re-mainder of the proof we follow the same convention and apply it to other processes as well.) Observe that by definition ofr˜andb◦we haveβs= −σsrs˜ and βs 2= ˜rsasrs˜, hence

dPt dP◦t



0 ˜

rsσsdWs− 1 2


0 ˜ rsasr˜sds


Denote the infinitesimal operator ofX◦ by L◦. By definition ofX◦ and R, we have˜ LR˜ = LR˜+ ˜rar˜. By Itô’s formula, it follows that


Rt− ˜R0= t



ds+ t

0 ˜

rsasr˜sds+ t

0 ˜


Combined with what we found above, we getdPt

dP◦t(X)=e− (R˜t− ˜R0)e


0Gsds, where(∂

∂sR˜+LR)˜ + 1

2r˜ar. By Lemma˜ 1, ahead the first term between brackets on the right-hand side of this display equalsLR˜− ˜LR˜−21r˜a˜r. Substituting this in the expression for˜ Ggives

G=(b− ˜b)r˜−1 2tr

(a− ˜a)H˜+1 2r˜

(a− ˜a)r,˜

which is as given in the statement of the theorem. Since−(R˜t− ˜R0)=logp(0, u)/˜ p(t, X˜ t), we arrive at the first assertion of the proposition.

To prove the second assertion, let 0=t0< t1< t2<· · ·< tN< t < T and definex0=u. Ifgis a bounded function onR(N+1), then standard calculations showE[g(Xt

1, . . . , X

tN, X



E[g(Xt1, . . . , XtN, Xt) 1

p(0,u)], using the abbreviationp(t, x)=p(t, x;T , v). Since the grid and gare arbitrary, this proves that fort < T,

dPt dPt

(X)=p(t, Xt;T , v)

p(0, u;T , v). (3.1)

Combined with the first statement of the proposition, this yields the second one.

Lemma 1. R˜ satisfies the equation

∂sR˜+ ˜LR˜= − 1 2r˜


Proof. First, note that

D2ijR(s, x)˜ =

D2ijp(s, x)˜ ˜

p(s, x)

DiR(s, x)˜ DjR(s, x)˜ . (3.2)

Next, Kolmogorov’s backward equation is given by

∂sp(s, x)˜ +(L˜p)(s, x)˜ =0. Dividing both sides byp(s, x)˜ and using (2.1), we obtain

∂sR(s, x)˜ = − d

i=1 ˜

bi(s, x)DiR(s, x)˜ −1 2


i,j=1 ˜

aij(s, x)D 2 ijp(s, x)˜

˜ p(s, x) .

Now substitute (3.2) for the second term on the right-hand side and reorder terms to get the


4. Proof of Theorem


Auxiliary lemmas used in the proof are gathered in Section4.1ahead. As before, we use the notationp(s, x)=p(s, x;T , v)and similar forp. Moreover, we define˜ p¯=p(p(˜00,u),u). The main part of the proof consists in proving thatpψ (T )¯ is indeed a Radon–Nikodym derivative, that is, that it has expectation 1. Forε(0,1/6)as in Assumption3,m∈Nand a stochastic process Z=(Zt, t∈ [0, T]), define

σm(Z)=T ∧ inf t∈[0,T]

|Ztv| ≥m(Tt )1/2−ε .


By Proposition1, for anyt < T and bounded,Ft-measurablef, we have


fXp(t, X˜ t) p(t, Xt)

=EfXpψ (t )¯ . (4.1)

By Corollary1in Section4.1, for eachm∈N, sup0≤tTψ (t )is uniformly bounded on the event {T =σm}. Hence, by dominated convergence,

Epψ (T )1T¯ =σm

=lim tTE


pψ (t )1tσm

≤lim tTE


pψ (t )=lim tTE

˜ p(t, Xt) p(t, Xt)


Here the final two equalities follow from equation (4.1) and Lemma3, respectively. Taking the limitm→ ∞we obtainE[ ¯pψ (T )] ≤1, by monotone convergence. For the reverse inequality note that by similar arguments as just used we obtain

Epψ (T )¯ ≥Epψ (T )1T¯ =σm

=lim tTE


pψ (t )1tσm

=lim tTE

˜ p(t, Xt) p(t, Xt)1tσm


By Lemma5, the right-hand side of the preceding display tends to 1 asm→ ∞. We conclude thatp¯E[ψ (T )] =1.

To complete the proof, we note that by equation (4.1) and Lemma3we havep¯E[ψ (t )] →1 as tT. In view of the preceding and Scheffé’s lemma this implies thatψ (t )ψ (T )inL1-sense astT. Hence fors < T and a bounded,Fs-measurable functionalg,

EgXpψ (T )¯ =lim tTE

gXp(t, X˜

t) p(t, Xt)

¯ pp(t, X

t) ˜

p(t, Xt)ψ (t )


Proposition1implies that fort > s, the expectation on the right equals


gXp(t, X˜ t) p(t, Xt)


By Lemma3, this converges toEg(X)as tT and we find that Eg(X)pψ (T )¯ =Eg(X). Sinces < tandgare arbitrary, this completes the proof.

4.1. Auxiliary results used in the proof of Theorem


Lemma 2. Suppose Assumptions1(iii)and2apply.For

ft(s, x)=

p(s, x;t, z)p(t, z˜ ;T , v)dz, 0≤s < t < T , x∈Rd, (4.2)

there exist positive constantscandλsuch that

ft(s, x)c(Ts)d/2exp

λ vx 2



Proof. LetC,C, ˜ and ˜ be the constant appearing in Assumptions1(iii) and2. Define ¯ = min( , )/2. Denote by˜ ϕ(z;μ, ) the N (μ, )-density, evaluated atz. Then there exists a


C >0 such that ft(s, x)≤ ¯C

ϕz;μt(s, x),¯−1(ts)Idd

ϕvz;0,¯−1(Tt )Idddz = ¯Cϕv;μt(s, x), ¯−1(Ts)Idd


Using the second assumed bound onμt(s, x)and the fact thatgM(ts)≥1/Mwe get vμt(s, x)2≥M−1 vx 2+1−gM(ts) v 2−2vμt(s, x)gM(ts)x.

By Cauchy–Schwarz, the triangle inequality and the first assumed inequality we find vμt(s, x)gM(ts)xv x



We conclude that

vμt(s, x) 2


1 M

vx 2 Ts +


Ts v



M(ts) Ts +

1−gM(ts) Ts

v x .

By definition ofgM, the multiplicative terms appearing in front of v 2and v x are both bounded. As there exist constantsD1>0 andD2∈Rsuch that the third term on the right-hand side can be lower bounded byD1 vx 2+D2the result follows.

The following lemma is similar to Lemma 7 in Delyon and Hu [9].

Lemma 3. Suppose Assumptions1(i),1(iii)and2apply.If0< t1< t2<· · ·< tN< t < T and gCb(RN d),then

lim tTE


1, . . . , X


p(t, X˜ t) p(t, Xt)


1, . . . , X



Lemma 4. Assume

1. b(s, x),b(s, x),˜ a(s, x)anda(s, x)˜ are locally Lipschitz insand globally Lipschitz inx; 2. a(T , v)˜ =a(T , v).

Then for allx and for alls∈ [0, T ),

b(s, x)− ˜b(s, x)1+ xv (4.3)



If in additionr˜andH˜ satisfy the bounds

r(s, x)˜ 1+ xv (Ts)−1,

H (s, x)˜ F (Ts)−1+ xv (Ts)−1,


G(s, x)1+(Ts)+ xv + xv Ts +

xv 2 Ts +

xv 3 (Ts)2.

Proof. Since|tr(AB)| ≤ A F B F and AB F ≤ A F B F for compatible matricesAand B, we have

G(s, x)b(s, x)− ˜b(s, x)r(s, x)˜

(4.5) +a(s, x)− ˜a(s, x)FH (s, x)˜ F+r(s, x)˜ 2.

Bounding b(s, x)− ˜b(s, x) proceeds by using the assumed Lipschitz properties forbandb.˜ We have

b(s, x)− ˜b(s, x)b(s, x)b(s, v)+b(s, v)− ˜b(s, v)+b(s, v)˜ − ˜b(s, x)Lb xv +b(s, v)− ˜b(s, v)+Lb˜ vx ,

whereLbandLb˜denote Lipschitz constants. Sinceb(·, v)andb(˜ ·, v)are continuous on[0, T], we have b(s, v)− ˜b(s, v) 1. This inequality together with preceding display gives (4.3).

Bounding a(s, x)− ˜a(s, x) F proceeds by using the assumed Lipschitz properties foraand ˜

atogether witha(T , v)˜ =a(T , v). We have a(s, x)− ˜a(s, x)

Fa(s, x)a(T , x)F +a(T , x)a(T , v)F+a(T , v)− ˜a(T , v)F +a(T , v)˜ − ˜a(s, v)

F+a(s, v)˜ − ˜a(s, x)F

(Ts)+ xv .

The final result follows upon plugging in the derived estimates for b(s, x)− ˜b(s, x) and a(s, x)− ˜a(s, x) F into equation (4.5) and subsequently using the bounds onr˜ andH˜ from

the assumptions of the lemma.

Corollary 1. Under the conditions of Lemma4,for allε(0,1/6)there is a positive constant

K(not depending onm)such that for allt∈ [0, T ) ψ (t )1tσm◦ ≤exp


Proof. On the event{tσm◦}, we have


Together with the result of Lemma4, this implies that there is a constantC >0 (that does not depend onm) such that for alls∈ [0, t]

Gs, Xs◦≤C1+m(Ts)1/2−ε+m(Ts)−1/2−ε+m2(Ts)−2ε+m3(Ts)−1/2−3εCm31+(Ts)1/2−ε+(Ts)−1/2−3ε.


ψ (t )1tσm◦ ≤exp

Cm3 T




for some constantK.

Lemma 5. Suppose Assumptions1(i), (iii)and2apply.Then

lim m→∞limtTE

˜ p(t, Xt) p(t, Xt)1tσm

=1. Proof. First, E ˜ p(t, Xt) p(t, Xt)1tσm


˜ p(t, Xt) p(t, Xt)


˜ p(t, Xt) p(t, Xt)1t >σm


Hence, by Lemma3, it suffices to prove that the second term tends to 0. Fort < T

p(0, u)E

˜ p(t, Xt) p(t, Xt)1t >σm

=Ep(t, X˜ t)1t >σm

=EEp(t, Xt˜ )1t >σm|Fσm

=E1t >σmEp(t, Xt˜ )|Fσm =E

1t >σm

p(σm, Xσm;t, z)p(t, z)˜ dz

=E1t >σmft(σm, Xσm)


whereft is defined in equation (4.2). Here we used (3.1) and the strong Markov property. By Lemma2,

Eft(σm, Xσm)



λ vXσm 2



Since vXσm =m(Tσm)

1/2−ε, the right-hand side can be bounded by a constant times E[(Tσm)d/2exp(−λm2(Tσm)−2ε)]. Note that this expression does not depend ont. The proof is concluded by taking the limitm→ ∞. Trivially,Tσm∈ [0, T], so that the preceding display can be bounded by

C sup τ∈[0,)


d 4λm2


4ε .


5. Proof of Theorem



It is well known (see, for instance, Liptser and Shiryaev [18]) that the linear process X˜ is a Gaussian process that can be described in terms of the fundamentald×d matrix(t), which satisfies

(t)=Id+ t

0 ˜

B(τ )(τ )dτ.

We define(t, s)=(t )(s)−1,

μt(s, x)=(t, s)x+ t


(t, τ )β(τ )˜ dτ (5.1)


Kt(s)= t


(t, τ )a(τ )(t, τ )dτ. (5.2)

To simplify notation, we use the convention that whenever the subscriptt is missing, it has the value of the end timeT. So we writeμ(s, x)=μT(s, x) andK(s)=KT(s). The Gaussian transition densities of the processX˜ can be explicitly expressed in terms of the objects just defined. In particular, we have


R(s, x)= −d

2log(2π )− 1

2logK(s)− 1 2

vμ(s, x)K(s)−1vμ(s, x). (5.3)

This will allow us to derive explicit expressions for all the functions involved in Assumption1. For future purposes, we state a number of properties of(t, s), which are well known in literature on linear differential equations (proofs can be found for example, in Sections 2.1.1 up till 2.1.3 in Chicone [7]).

(t, s)(s, τ )=(t, τ ),(t, s)−1=(s, t)and∂s(t, s)= −(t, s)B(s).

• There is a constantC≥0 such that for alls, t∈ [0, T], (t, s)C(this is a consequence of Gronwall’s lemma).

• |(t, s)| =exp(sttr(B(u))˜ du)(Liouville’s formula).

• IfB(t )˜ ≡ ˜Bdoes not depend ont,(t, s)=exp(B(t˜ −s))=∞k=0k1!B˜k(ts)k.

By Theorem 1.3 in Chicone [7], we have that the mappings (t, s, x)μt(s, x)and(t, s)t(s)are continuously differentiable.

The following lemma provides the explicit expressions for the functionsr˜andH˜.

Lemma 6. Fors∈ [0, T )andx∈Rd ˜




H (s, x)= ˜H (s)= −Dr(s, x)˜ =(T , s)K(s)−1(T , s)

(5.4) =



(s, τ )a(τ )(s, τ )˜ dτ 1


Moreover,we have the relationr(s, x)˜ = ˜H (s)(v(s)x)where

v(s)=(s, T )vT


(s, τ )β(τ )˜ dτ. (5.5)

Proof. We use the conventions and rules on differentiations outlined in Section1.6. SinceK(s) is symmetric


r(s, x)= −Dvμ(s, x)K(s)−1vμ(s, x)

=(T , s)K(s)−1vμ(s, x),

where we used Dμ(s, x)=(s). By equation (5.1),

vμ(s, x)=v(T , s)xT


(T , τ )β(τ )˜ dτ. (5.6)

The expression forH˜ now follows from ˜

H (s)= −D(T , s)K(s)−1vμ(s, x)

=D(T , s)K(s)−1(T , s)x=(T , s)K(s)−1(T , s), where the second equality follows from equation (5.6).

The final statement follows upon noting that ˜

r(s, x)=(T , s)K(s)−1(T , s)(s, T )vμ(s, x)

= ˜H (s)(s, T )vμ(s, x)= ˜H (s)v(s)x.

The last equality follows by multiplying equation (5.6) from the left with(s, T ). In the following three subsections we use the explicit computations of the preceding lemma to verify Assumption1, in order to complete the proof statement (i) of Theorem2.

5.1. Assumption


Lemma 7. Iff:[0, T] ×Rd→Ris bounded and continuous then

lim tT


Proof. The log of the transition density of a linear process is given in equation (5.3). Usingvas defined in (5.5) and the expression forμas given in (5.1), we get

μ(t, x)=(T , t )x+(t, T )vv(t )=(T , t)xv(t )+v.

This gives

A(t, x):=vμ(t, x)K(t )−1vμ(t, x)=(T , t)xv(t )K(t )−1(T , t)xv(t ).

It follows that we can write

f (t, x)p(t, x˜ ;T , v)dz=

f (t, x)

|K(t )|(2π )

d/2 exp


2A(t, x)


Upon substitutingz=(T , t)(xv(t ))this equals

ft, (t, T )z+v(t )(2π )d/2√ 1 |K(t )|exp



K(t )−1z(t, T )dz.

We can rewrite this expression asE[Wt]where Wt=(t, T )f

t, (t, T )Zt+v(t )

andZt denotes a random vector withN (0, K(t))-distribution. AstT,Zt converges weakly to a Dirac mass at zero. As(t, T )converges to the identity matrix andv(t )v, we get that (t, T )Zt+v(t )converges weakly tov. By the continuous mapping theorem and continuity off,Wt converges weakly tof (T , v). Since the limit is degenerate, this statement holds for convergence in probability as well. By boundedness off, we getE[Wt] →f (T , v).

5.2. Assumption


Lemma 8. There exists a positive constantCsuch that for alls∈ [0, T )andx, y∈Rd

(Ts)H (s)˜ ≤C, (5.7)

r(s, x)˜C

1+ vx Ts

, (5.8)

r(s, y)˜ − ˜r(s, x)C yx

Ts , (5.9)

vx TsC

1+r(s, x)˜ . (5.10)

Proof. In the proof, we use the relations proved in Lemma6. From this lemma, it follows that ˜

H (s)−1= T



Since (s, τ ) is uniformly bounded and τ → ˜a(τ ) is continuous, it easily follows that yH (s)˜ −1y≤ ˜c(Ts) y 2 for ally∈Rd. By uniform ellipticity ofa, there exists a constant˜ c1>0 such that for ally∈Rd

y(s, τ )a(τ )(s, τ )˜ yc1y(s, τ )(s, τ )y.

Second, there exists a constantc2>0 such thaty(s, τ )(s, τ )yc2 y 2uniformly ins, τ∈ [0, T]. To see this, suppose this second claim is false. Then for eachn∈Nthere aresn, τn∈ [0, T],yn∈Rd\ {0}such that (sn, τn)yn 2≤ 1n yn 2, or lettingzn=yn/ yn ,

(sn, τn)zn 2

≤1 n.

By compactness of the set[0, T]2× {z∈Rd, z =1}and by continuity of, there exists a convergent subsequencesni, τni, znis, τ, z∗, such that, (s, τ)z

2=0 withz=0. This contradicts Liouville’s formula.

Integrating overτ∈ [s, T]gives

yH (s)˜ −1yc(Ts) y 2, (5.11) wherec=c1c2. Hence, we have proved that

c y 2≤y(Ts)H (s)˜ −1y≤ ˜c y 2.

SinceH˜ is symmetric, this says that the eigenvalues of the matrix((Ts)H (s))˜ −1are contained in the interval[c,c˜]. This implies that the eigenvalues of(Ts)H (s)˜ are in[1/c,˜ 1/c]. Since the operator norm of a positive definite matrix is bounded by its largest eigenvalue, it follows that (Ts) ˜H (s) ≤1/c.

To prove the second inequality, note that ˜

r(s, x)= ˜H (s)v(s)x= ˜H (s)v(s)v(T )+vx

=(Ts)H (s)˜

v(T )v(s) Ts +

vx Ts



v(T )v(s)=(T , T )(s, T )v+ T


(s, τ )β(τ )˜ dτ. Ass(s, T )is continuously differentiable, we have

v(T )v(s)C1(Ts)+ T


(s, τ )β(τ )˜ dτ≤C2(Ts).


r(s, x)˜(Ts)H (s)˜

C2+ vx Ts


which yields (5.8). Also,

r(s, x)˜ − ˜r(s, y)=H (s)(y˜ −x) yx Ts .

For obtaining the fourth inequality of the lemma, ˜

H (s)(vx)= ˜r(s, x)+ ˜H (s)v(T )v(s).

Upon multiplying both sides by((Ts)H (s))˜ −1this gives vx

Ts(Ts)H (s)˜ 1

r(s, x)+ v(T )v(s) Ts .

Substitution of the derived bounds onH (s)˜ −1andv(T )v(s)completes the proof.

5.3. Assumption


Lemma 9. There exist positive constantsCand such that for alls∈ [0, T ) ˜

p(s, x;T , v)C(Ts)d/2exp

vx 2 Ts

. (5.12)

Proof. Using the relations from Lemma6 together with equation (5.3), some straightforward calculations yield


R(s, x)= −d

2log(2π )− 1

2logK(s)− 1 2r(s, x)˜

H (s)˜ −1r(s, x).˜

By (5.11), there exists a positive constantc1>0 such that ˜

r(s, x)H (s)˜ −1r(s, x)˜ ≥c1(Ts)r(s, x)˜ 2.

By equation (5.10) the right-hand side is lower bounded by




Tsc2 √

Ts,0 2

for some positive constantc2. Now ifa≥0 andb∈ [0, c2], then there exist c3, c4>0 such that(max(ab,0))2≥c3a2−c4(this is best seen by drawing a picture). Applying this with a= vx /Tsandb=c2



r(s, x)H (s)˜ −1r(s, x)˜ ≥c1


vx 2 Tsc4



SinceH (s)˜ −1=(s, T )K(s)(s, T )T we have|K(s)| =|(T ,s)|2

| ˜H (s)| . Multiplying both sides by (Ts)dgives

(Ts)dK(s)= |(T , s)| 2

|(Ts)H (s)˜ |.

Since the eigenvalues of (Ts)H (s)˜ are bounded by 1/c uniformly over s∈ [0, T] (see Lemma8) and the determinant of a symmetric matrix equals the product of its eigenvalues, we get

(Ts)dK(s)(T , s)2cd=cdexp

2 T


trB(u)˜ du

by Liouville’s formula. Now it follows that the right-hand side of the preceding display is

bounded away from zero uniformly overs∈ [0, T].

6. Proof of Theorem



Auxiliary results used in the proof are gathered in Section6.1ahead.

By (5.10) in Lemma8, we have xv (Tt )(1+ ˜r(t, x) ). Therefore, we focus on bounding ˜r(t, x) . Definewto be the positive definite square root ofa(T , v). Then it follows from our assumptions that w <∞and w−1 <∞, hence we can equivalently derive a bound forZ(s, x)˜ =wr(s, x). We do this in two steps. First, we obtain a preliminary bound by writing˜ an SDE forZ˜ and bounding the terms in the equation. Next, we strengthen the bound using a Gronwall-type inequality.

By Lemma11,Z˜ satisfies the stochastic differential equation dZ˜s, Xs= −wH (s)σ˜ s, XsdWs+ϒ

s, Xsds+s, XsZ˜s, Xsds, (6.1) where

s, Xs◦=wH (s)˜ a(s)˜ −as, Xs◦− ˜B(s)w−1, (6.2) ϒs, Xs◦=wH (s)˜ b˜s, Xsbs, Xs. (6.3) DefineJ (s)˜ =wH (s)w. For˜ we have the decomposition=1+2+3, with


s, Xs= 1 Ts

Id−w−1as, Xsw−1,


s, Xs=


J (s)− 1 Ts

Id−w−1as, Xsw−1, (6.4)

3(s)=w ˜

H (s)a(s)˜ − ˜a(T )− ˜B(s)w−1.

To see this, we calculate1(s, Xs)+2(s, Xs)= ˜J (s)(Idw−1a(s, Xs)w−1)and s, Xs1

s, Xs◦−2


Upon substitutingJ (s)˜ =wH (s)a(T , v)w˜ −1=wH (s)˜ a(T )w˜ −1 into this display we end up with exactly3(s).

Forϒwe have a decompositionϒ=ϒ1Z˜+ϒ2with ϒ1

s, Xs◦=wH (s)˜ Bs, Xs◦− ˜B(s)H˜−1(s)w−1,


s, Xs◦=wH (s)˜ β(s)˜ −β(s)Bs, Xs◦− ˜B(s)v(s).

Here,v(s)is as defined in (5.5). To prove the decomposition, first note thatϒ,ϒ1andϒ2share the factorwH (s). Therefore, it suffices to prove that˜


b(s, x)b(s, x)B(s, x)− ˜B(s)H˜−1(s)w−1Z(s, x)˜

(6.5) = ˜β(s)β(s, x)B(s, x)− ˜B(s)v(s).

By Lemma6,Z(s, x)˜ =wr(s, x)˜ =wH (s)(v(s)˜ −x). Upon substituting this into the left-hand side of the preceding display, we obtain


B(s)B(s, x)x+ ˜β(s)β(s, x)B(s, x)− ˜B(s)v(s)x,

which is easily seen to be equal to the right-hand side of (6.5). Thus, (6.1) can be written as dZ˜s, Xs◦= −wH (s)σ˜ s, Xs◦dWs


s, Xs◦+2

s, Xs◦+3(s)+ϒ1

s, XsZ˜s, Xs◦ds (6.6) +ϒ2

s, Xsds.

Next, we derive bounds on1,2,3,ϒ1andϒ2.

• By Lemma12, it follows that there is aε0∈(0,1/2)such that


s, Xsy≤1−ε0 Ts y


for alls∈ [0, T )andy∈Rd.

• By Lemma13, ˜J (s)−Id/(T−s) is bounded fors∈ [0, T]. Asσis bounded, this implies 2can be bounded by deterministic constantC1>0.

• For3, we employ the Lipschitz property ofa˜ to deduce that there is a deterministic con-stantC2>0 such that

3(s)(Ts)H (s)˜

a(s)˜ T− ˜a(T )s +B(s)˜ ≤C2.

• Since(s, x)B(s, x)is assumed to be bounded, there exists a deterministic constantC3> 0 such that



• Similarly, using thatsv(s)is bounded on[0, T], we have the existence of a deterministic constantC4such that


= w (Ts)H (s)˜ β(s)˜ +βs, Xs◦+Bs, Xs◦− ˜B(s)v(s)C4.

Now we set A(s, x)=1(s, x)+2(s, x)+3(s)+ϒ1(s, x) and let(s) be the principal fundamental matrix at 0 for the corresponding random homogeneous linear system

d(s)=As, Xs(s)ds, (0)=Id. (6.7)

SincesA(s, Xs)is continuous for each realizationX◦,(s)exists uniquely (Chicone [7], Theorem 2.4). Using the just derived bounds, for ally∈Rd

yAs, Xsy


Ts +C1+C2+C3

y 2.

By Lemma14, this implies existence of a positive constantCsuch that (t )(s)−1≤C

Ts Tt


, 0≤st < T .

By Lemma15, fors < T we can representZ˜ as ˜

Zs, Xs=(s)Z(0, u)˜ +(s) s


(h)−1ϒ2(h)dh−Ms, (6.8)


Ms=(s) s


(h)−1wH (h)σ˜ h, XhdWh. (6.9)

Bounding ˜Z(s, X) can be done by bounding the norm of each term on the right-hand side of equation (6.8).

The norm of the first term can be bounded by ˜Z(0, u) (s) (Ts)ε0−1. The norm of the second one can be bounded by



Th Ts

1ε0 1

T2(h)(Th)dh(Ts) ε0−1.

For the third term, it follows from Lemma16, applied withU (s, h)=wH (h)σ (h, X˜ ◦h), that there is an a.s. finite random variableMsuch that for alls < T MsM(Ts)ε0−1. Therefore, there exists a random variableMsuch that


We finish the proof by showing that the bound obtained can be improved upon. We go back to equation (6.1) and consider the various terms. By inequality (4.3) and the inequalities of Lemma8we can bound

ϒ (s, x)H (s)˜ 1+ xv (Ts)−1+ vx

Ts 1+(Ts)

−1+Z(s, x)˜ .

Similarly, using inequality (4.4)

(s, x)1+ vx

Ts 1+Z(s, x)˜ . The quadratic variation L of the martingale part Lt =


0wH (s)σ (s, X˜ s)dWs is given by Lt=0twH (s)a(s, X˜ ◦s)H (s)w˜ ds. Hence, by the boundedness of ˜H (s)(Ts) we have

Lt t

0 1

(Ts)2ds= 1 Tt

1 T

1 Tt.

By the Dambis–Dubins–Schwarz time-change theorem and the law of the iterated logarithm of Brownian motion, it follows that there exists an a.s. finite random variableN such that LtNf (t )for allt < T, where

f (t )=


Tt log log

1 Tt +e


Taking the norm on the left- and right-hand side of equation (6.1), applying the derived bounds and using that 0t(Ts)−1ds √1/(T −t ) we get with ρ(s)= ˜Z(s, Xs) that ρ(t )Nf (t )+C0t(ρ(s)+ρ2(s))ds,t < T for some positive constantC. The bound (6.10) derived above implies thatρ is integrable on[0, T]. The proof of assertion (ii) of Theorem2is now completed by applying Lemma17.

6.1. Auxiliary results used in the proof of Theorem


Lemma 10. DefineV (s)=w−1H (s)˜ −1w−1 and V(s)=∂s∂V (s).It holds that sV(s)is Lipschitz on[0, T]andV(s)→ −IdassT.

Proof. By equation (5.4)

(T , s)H (s)˜ −1(T , s)= T


(T , τ )a(τ )(T , τ )˜ dτ.

Taking the derivative with respect toson both sides and reordering terms gives

∂sH (s)˜


and henceV(s)=w−1(−˜a(s)+ ˜B(s)H (s)˜ −1+ ˜H (s)−1B(s)˜ )w−1. Since (s, τ )C for all s, τ ∈ [0, T], it follows that sV(s) is Lipschitz on [0, T]. Furthermore, V(s)

w−1a(T )w−1= −Id, assT.

Lemma 11. We have

dr˜s, Xs◦= − ˜H (s)σs, XsdWs

+ ˜H (s)b˜s, Xsbs, Xsds

+H (s)˜ a(s)˜ −as, Xs◦− ˜Br˜s, Xsds,


Proof. In the proof, we will omit dependence onsandXs◦in the notation. By Itô’s formula d˜r=

∂sr˜ds− ˜HdX

. (6.11)

For handling the second term, we plug-in the expression for X◦ from its defining stochastic differential equation. This gives


HdX◦= ˜H bds+ ˜H ar˜ds+ ˜H σdW. (6.12) For the first term, we compute the derivative ofr(s, x)˜ with respect tos. For this, we note that by Lemma1∂s∂R˜= − ˜LR˜−12r˜a˜r˜, withL˜R˜= ˜br˜−12tr(a˜H ). Next, we take˜ Don both sides of this equation. Since we assumeR(s, x)˜ is differentiable in(s, x)we haveD((∂/∂s)R)˜ =(∂/∂s)r.˜ Further, D(L˜R)˜ = ˜Br˜− ˜Hb˜ andD(12r˜a˜r)˜ = − ˜Ha˜r˜. Therefore, ∂s∂r˜= − ˜Br˜+ ˜Hb˜+ ˜Ha˜r.˜ Plugging this expression together with (6.12) into equation (6.11) gives the result.

Lemma 12. There exists anε0∈(0,1/2)such that for0≤s < T,x, y∈Rd

y1(s, x)y

1−ε0 Ts

y 2,

with1as defined in(6.4).

Proof. Lety∈Rd. By (2.8) there isε >0 such that

y1(s, x)y=y

1 Ts

Id−w−1a(s, x)w−1y

1 Ts

yyεya(T )˜ −1y.

Since a(T )˜ =a(T , v)is positive definite, its inverse is positive definite as well. Hence, there exists aε>0 such thatya(T )˜ −1yε y 2. This givesy1(s, x)y≤1−εε

Ts y

2. Letε 0=εε.



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