• <tr id="yyy80"></tr>
  • <sup id="yyy80"></sup>
  • <tfoot id="yyy80"><noscript id="yyy80"></noscript></tfoot>
  • 99热精品在线国产_美女午夜性视频免费_国产精品国产高清国产av_av欧美777_自拍偷自拍亚洲精品老妇_亚洲熟女精品中文字幕_www日本黄色视频网_国产精品野战在线观看 ?

    Least Squares Estimator for Fractional Brownian Bridges of the Second Kind

    2021-06-30 00:09:06WANGYihan汪義漢
    應(yīng)用數(shù)學(xué) 2021年3期

    WANG Yihan(汪義漢)

    (School of Mathematics and Statistics,Anhui Normal University,Wuhu 241000,China)

    Abstract:In this paper,we study the least squares estimator for the drift of a fractional Brownian bridge of the second kind defined under the cases of parameter θ>0 and Hurst parameter H∈We obtain the consistency and the asymptotic distribution of this estimator using the Malliavin calculus.

    Key words:Fractional Brownian motion;Least squares estimator;Bridge

    1.Introduction

    LetT>0 be fixed.For allθ>0,the fractional bridge of the second kind{Xt}t∈[0,T)with initial value 0 is the solution of the stochastic differential equation(SDE)

    In the case when the processXhas Hlder continuous paths of Hurst indexwe can consider the following least squares estimator(LSE)proposed in[8],

    as estimator ofθ,where the integral with respect toXis a Young integral.Thus,thanks to(1.1),it is easily to obtain

    In the same setup as fractional Brownian process of the first kind,the parameter estimation forθhas been studied by using classical maximum likelihood method or least squares technique by Barczy and Pap[4],Tudor and Viens[13],Es-Sebaiy and Nourdin[6],HAN,SHEN and YAN[7],respectively.In this paper,we consider the least squares estimatorin the setup of the fractional bridge of the second kind.The key point is the Lemma 3.6 in Section 3.

    This paper is organized as follows.Section 2 deals with some preliminary results on stochastic integrals with respect to Malliavin derivative,Skorohod integral,Young integral.The consistency and the asymptotic distribution of the estimator are stated in Section 3.Almost all the proofs of the lemmas are provided in Section 4.

    2.Preliminaries

    We start by introducing the elements from stochastic analysis that we will need in the paper(see[1,11]).Letbe a fractional Brownian motion with the covariance function satisfying

    The crucial ingredient is the canonical Hilbert spaceH(is also said to be reproducing kernel Hilbert space)associated to the Gaussian process which is defined as the closure of the linear spaceEgenerated by the indicator functions 1[0,t],t∈[0,T]with respect to the scalar product

    Therefore,the elements of the Hilbert spaceHmay not be functions but distributions of negative order.Let|H|be the set of measurable functionsφon[0,T]such that

    It is not difficult to show that|H|is a Banach space with the norm‖φ‖|H|andEis dense in|H|.Forφ,?∈|H|,we have

    Notice that the above formula holds for any Gaussian process,i.e.,ifGis a centered Gaussian process with covarianceRinL1([0,T]2),then(see[10])

    ifφ,?are such thatdudv<∞.

    LetSbe the space of smooth and cylindrical random variables of the form

    wheren≥1,f:Rn→R is a-function such thatfand all its derivatives have the most polynomial growth,andφi∈H.For a random variableFof the form above we define its Malliavin derivative as theH-valued random variable

    By iteration,one can define themth derivativeDmF,which is an element ofL2(Ω;H?m),for everym≥2.

    For everyq≥1,letHqbe theqth Wiener chaos ofB,that is,the closed linear subspace ofL2(Ω)generated by the random variables{Hq(H(h)),h∈H,‖h‖H=1},whereHqis theqth Hermite polynomial.The mappingIq(h⊙q)=Hq(B(h))provides a linear isometry between the symmetric tensor productH⊙q(equipped with the modified normandHq.Specifically,for allf,g∈H⊙qandq≥1,one has

    On the other hand,it is well-known that any random variableZbelonging toL2(Ω)admits the following chaotic expansion:

    where the series converges inL2(Ω)and the kernelsfq,belonging toH⊙q,are uniquely determined byZ.For a detail account on Malliavin calculus we refer to[11].

    Letf,g:[0,T]R be Hlder continuous functions of orderμ∈(0,1)andν∈(0,1)respectively withμ+ν>1.Young[14]proved that the Riemman-Stiltjes(so called Young integral)exists.Moreover,ifμ=ν∈(1/2,1)andΦ:R2R is a function of classC1,the integralsandexist in the Young sense and the following formula holds:

    for 0≤t≤T.As a consequence,ifand(ut,t∈[0,T])is a process with the Holder paths of orderμ∈(1-H,1),the integralis well defined as a Young integral.Suppose moreover that for anyt∈[0,T],ut∈D1,2(|H|),and

    Then,by the same argument as in[1],we have

    In particular,whenφis a non-random Hlder continuous function of orderμ∈(1-H,1),we obtain

    Finally,we will use the following covariance of the increments of the noiseY(1)satisfying(see Proposition 3.5 in[9])

    where

    withCH=H2H-1(2H-1).Note that the kernelrHis symmetric.

    3.Asymptotic Behavior of the Least Squares Estimator

    In this section we study the strong consistence and the asymptotic distribution of the estimator ofθtast→T.Consider the following processes related to{Xt}t∈[0,T):

    and

    then,fort∈[0,T)and<H<1,

    and

    Using the equations(3.1)and(3.2),the LSEdefined in(1.3)can be written as

    ⅠConsistency of the Estimator LSE

    The following theorem proves the(strong)consistency of the LSEFor simplicity,throughout this paper,letcstand for a positive constant depending only on the subscripts and its value may be different in different appearances,andstands for convergence in distribution(resp.probability,almost surely).

    Theorem 3.1Letbe given by(1.3).Then

    In particular,whenθ<H,Hast→T.

    In order to prove Theorem 3.1 we need the following two lemmas,and their proofs are shown in Section 4.

    Lemma 3.1Supposeθ∈(0,H),H∈and letξtbe given by(3.1).ThenξT:=limt→T ξtexists inL2.Furthermore,for all?∈(0,H-θ)there exists a modification of{ξt}t∈[0,T]with(H-θ-?)-Hlder continuous paths,still denotedξin the sequel.In particular,ξt→ξTalmost surely ast→T.

    Lemma 3.2Assumeθ∈(0,H),H∈and letξtbe defined in(3.1).Then,ast→T:

    1)if 0<θ<,then

    2)ifθ=,then

    Proof of Theorem 3.1By the formula(2.6),we obtain that,for anyt∈[0,T),

    which yields

    Whenusing Lemma 3.2 and Lemma 3.1,we derive that

    ast→T,respectively.Hence,we obtain thatast→T.

    Whenθ=the equality(3.3)becomes

    Analogously,by Lemma 3.2 and Lemma 3.1,we also deduce that

    ast→T,respectively.Hence,we obtainast→T.

    ast→T,respectively.Hence,(3.4)yields thatθ-ast→T,that is,

    Finally,whenθ≥H,by(2.4)and(2.9),and using the elementary inequality|ex-ey|α≤|x-y|αfor-1<α<0,x≥0,y≥0 andxy,we compute that

    Hence,having a look at(3.3)and becauseast→T,we deduce thatast→T,that is,which implies the desired conclusion.

    ⅡAsymptotic Distribution of the Estimator LSE

    Theorem 3.2Assume<H<1 be fixed and setσ2=2H(2H-1)β(1-H,2H-1)withβ(a,b)=LetN~N(0,1)be independent ofY(1),and letC(1)stand for the standard Cauchy distribution.

    1)Ifθ∈(0,1-H)then,ast→T,

    2)Ifθ=1-Hthen,ast→T,

    3)Ifθ∈(1-H,)then,ast→T,

    4)Ifθ=then,ast→T,

    We also apply the following several lammas to prove Theorem 3.2,whose proofs refer to Section 4.

    Lemma 3.3Letθ∈(1-H,H),H∈and letηtbe defined in(3.2).ThenηT:=limt→T ηtexists inL2.Furthermore,there existsγ>0 such that{ηt}t∈[0,T]admits a modification withγ-Hlder continuous paths,still denotedηin the sequel.In particular,ηt→ηTalmost surely ast→T.

    Lemma 3.4Letηtbe defined in(3.2).For anyt∈[0,T),we have

    Lemma 3.5Letθ∈(0,1-H],H∈Then,ast→T,

    Lemma 3.6Fix<H<1 and setσ1=σ2=2H(2H-1)β(1-H,2H-1).LetFbe anyσ{Y(1)}-measurable random variable satisfyingP(F<∞)=1,and letN~N(0,1)be independent ofY(1).

    1)Ifθ∈(0,1-H)then,ast→T,

    2)Ifθ=1-Hthen,ast→T,

    Proof of Theorem 3.21)Assume thatθ∈(0,1-H).By Lemma 3.4,we have

    with clear definitions forat,bt,ct,etandft.Let us begin to consider the termsat,btandct.First,Lemma 3.6 yields

    whereN~N(0,1)is independent ofY(1),whereas Lemmas 3.1,3.2 imply thatandast→T,respectively.On the other hand,by combining Lemma 3.2 with Lemma 3.5,we can get thatandast→T,respectively.Let us summarize all these convergence together,we obtain that

    2)Assume thatθ=1-H.By similar arguments as in the first point above,the counterpart of decomposition is the following:

    By using Lemma 3.6 again,we obtain that,ast→T,

    whereN~N(0,1)is independent ofY(1),whereas Lemmas 3.1,3.2 also imply thatandast→T,respectively.On the other hand,by combining Lemma 3.2 with Lemma 3.5,we deduce thata ndast→T,respectively.By plugging all these convergence together we get that,ast→T,

    3)Assume thatθ∈(1-H,).Using the decomposition

    we immediately obtain that the third point of Theorem 3.2 follows from an obvious consequence of Lemmas 3.3,3.2.

    4)Assume thatθ=By(3.4),

    4.Proofs of Several Lemmas

    In this section,we present here the proofs of several lemmas used in Section 3.

    Proof of Lemma 3.1The idea mainly comes from Lemma 2.2 in[5]and Lemma 4 in[6].We give the main process as following for the completeness.In order to apply the Kolmogorov continuity criterium,we need to evaluate the mean square of the increment

    with 0≤s≤t<T.This is a Gaussian random variable and we will use the formula(2.4)in order to compute itsL2norm.The covariance of the processY(1)can be obtained from the formula(2.9).

    Recall that the elementary inequality|ex-ey|α≤|x-y|αholds for-1<α<0,x≥0,y≥0 and.For all 0≤s≤t<T,using(2.3)and(2.9),we have

    for some positive constantC(H,θ)depending onHandθ.By applying the Cauchy criterion,we deduce thatξT:=limt→T ξtexists inL2.Furthermore,because the processξis centered and Gaussian,for any positive integerm,we have

    Hence,formsufficiently large satisfying 2m(H-θ)-1>0,ξtareγ-Hder continuous for every=Therefore,the result follows directly from an application of the Kolmogorov continuity theorem.

    Proof of Lemma 3.21)By Lemma 3.1,using the-Hlder continuity ofξt,we have

    3)By Lemma 3.1,the processξis continuous on[0,T],hence integrable.Furthermore,becauseu→(T-u)2θ-2is integrable atu=T.The convergence in point 3 holds with a finite limit.

    Proof of Lemma 3.3Letβ1,β2∈(1-H,H)be fixed.We first show that there existsε=ε(β1,β2,H)>0 andc=c(β1,β2,H)>0 such that,for all 0≤s≤t<T,

    In fact,by means of the change of variables,we have

    ≤c(t-s)εfor someε∈(0,1∧(2H-β1)-β2).

    Therefore,the inequality(4.1)holds.

    Now,using(2.7),we shall separateηtinto two terms.Fort<T,

    Set

    The identity(4.2)becomes

    Therefore,by(2.5),we have

    Sinceψt-ψs∈H⊙2,we obtain that

    Notice the fact thatψt-ψsis symmetric,we easily get thatis upper bounded by a sum of integrals of the type

    withβ1,β2,β3,β4∈{θ,1-θ}.Therefore,by(4.1),there existsε>0 small enough andc>0 such that,for alls,t∈[0,T],

    On the other hand,for all 0≤s≤t<T,we obtain again that

    Thus,

    Second,ifθ=2H-1 then

    Thus,

    Third,ifθ<2H-1 then

    Thus,

    To summarize three cases above,there existsc>0 such that,for alls,t∈[0,T],

    Substituting(4.4)and(4.5)into(4.3)yields that there existsε>0 small enough andc>0 such that,for alls,t∈[0,T],

    By using the Cauchy criterion again,we obtain thatηT:=limt→T ηtexists inL2.Furthermore,becauseηt-ηs-E[ηt]+E[ηs]belongs to the second Wiener chaos ofY(1),the result follows directly from an application of the Kolmogorov continuity theorem.

    Proof of Lemma 3.4Lett∈[0,T)be fixed.By(2.6),v)-θdY(1)vbecomes

    Furthermore,it follows from(2.7)that

    Hence,the result follows.

    Proof of Lemma 3.5PutNotice thatφt∈|H|⊙2andwe obtain that

    Sinceθ≤1-H,we obtain that

    Furthermore,since 2H+2θ-3<-1 andθ∈(0,1-H],we obtain that

    Hence,ifθ<1-H,then

    Assume now thatθ=1-H,then

    Thus,by combining all the previous cases,we obtain that lim supt→Tis finite,which completes the proof.

    Proof of Lemma 3.61)We will use the approach from the proof of Lemma 7 in[6].It is enough to prove that for anym≥1 and anys1,...,sm∈[0,T),we shall prove that,ast→T,

    where,for|T-t|sufficiently small,

    with

    Thus,

    On the other hand,by(2.9)we have,for anyv<t<T,

    2)By(2.9),for anyt∈[0∨(T-1),T)and for|T-t|sufficiently small,we have

    On the other hand,fixv∈[0,T),by(2.9),for allt∈[0∨(T-1),T),we have

    whereσ2=2H(2H-1)β(1-H,2H-1).Therefore,the same reasoning as in point 1 allows to go from(4.7)to(3.6).The proof of the lemma is concluded.

    AcknowledgmentsWe thank Professor Shen Guangjun for his guidance,including valuable suggestions and remarks and for his fund support.

    淄博市| 资阳市| 饶河县| 航空| 鲁甸县| 邹城市| 锦屏县| 扎兰屯市| 简阳市| 咸宁市| 澄江县| 万全县| 江都市| 邵阳市| 汉中市| 仪征市| 宁都县| 舞钢市| 广东省| 新沂市| 元氏县| 通许县| 托克托县| 徐闻县| 洛扎县| 乌兰县| 调兵山市| 沾益县| 绥芬河市| 太康县| 天柱县| 广宁县| 阆中市| 政和县| 涟水县| 法库县| 华池县| 博爱县| 新巴尔虎右旗| 长垣县| 房产|