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        ?

        It?積分和Str atonovich積分的比較

        2012-11-05 07:35:34
        浙江科技學(xué)院學(xué)報 2012年4期
        關(guān)鍵詞:理學(xué)院杭州浙江

        王 偉

        (浙江科技學(xué)院 理學(xué)院,杭州310023)

        1 Introduction

        In this paper,we consider the t wo kinds of stochastic integrals,the It?integral and the Stratonovich integral.Let(Ω,F(xiàn))be a measure space with t he pr obability measure P and Bt(ω)be a n-dimensional Bro wnian motion.Assu me t hat Ft=F(n)tis theσ-algebra generated by t he rando m variables{Bi(s)}1≤i≤n,0≤s≤t.We denote by V(S,T)the class of f unctions.

        such that

        1)(t,ω)→f(t,ω)is B×F-measurable,where B denotes t he Borelσ-al gebra on[0,∞);

        2)f(t,ω)is Ft-adapted;

        We adopt L2(P)to be a Hil bert space which is a co mplete inner pr oduct space wit h t he f ollo wing inner product.

        Definition 1 (It?integral) Suppose f∈V(0,T)and that t→f(t,ω)is continuous f or a.a.ω.Then t he It?integral is defined by

        Definition 2 (Str atonovich integral) Suppose f∈V(0,T)and t hat t→f(t,ω)is continuous f or a.a.ω.Then t he Str atonovich integral of f is defined by

        whenever t he limit exists in L2(P).

        2 It?for mula

        Theorem 1 (It?f or mula) Let Xtbe an It?process given by

        Asssu me g(t,x)∈C2([0,∞)×R)(i.e.g is t wice continuously diff erentiable on[0,∞)×R).Then

        is also an It?process,and we have

        where(d Xt)2=(d Xt)·(d Xt)is computed according to the r ules

        By the It?for mula(1),we get

        So,we get the value of this It?integral as the f ollowing

        3 Relationship bet ween the It?integral and the Stratonovich integral

        Theorem 2 Suppose f∈V(0,T)and that t→f(t,ω)is continuous f or a.a.ω.Then

        Proof Suppose f∈V(0,T)and that t→f(t,ω)is continuous for a.a.ω.Then,

        Now we can use t he Theorem 2 to co mpute so me Str atonovich integrals.

        We see the different values of the t wo kinds of integrals clearly through the Example 1 and the Example 2.

        4 Application in the stochastic differential equations

        Example 3 Solve the following stochastic equation,which is a well-known population growth model

        Sol ution The equation(3)can be written as

        By the It?for mula,we have

        By t he equation(3),we obtain(d Nt)2= (r Ntd t+αNtd Bt)2=α2N2t(d Bt)2=α2N2td t.So we get

        Then we can concl ude

        Exa mple 4 The Str atonovich inter pretation of stochastic equation(3)is

        Solve this stochastic equation.

        Solution By the Theorem 2,we have

        We call such a process Geometric Brownian motion.It is also an important model for stochastic prices in econo mics[1].

        5 Contrast bet ween the It?integral and the Str atonovich integral

        At t he end,let us ret ur n to t he population gro wt h model in t he Exa mple 3.We know that Ntis a solution of the stochastic equation(3),and

        For some suitable interpretation of the last integral in the equation(5),the It?interpretation of an integral is j ust one of t he several reasonable choices.However,t he Str atonovich integral is anot her choice,usually leading to a diff erent result.So t he question is:Which inter pretation of t he last integral in the equation (5)makes the equation the “exact”mathematical model for this equation?The Str atonovich interpretation in so me situations may be the most appropriate.Choose t-continuously differentiable pr ocesses B(n)tsuch that f or a.a.ω,

        unif or mly(in t)in bounded inter vals.For eachωlet N(n)t(ω)be t he sol ution of the corresponding(deter ministic)differential equation

        Then,f or a.a.ω,

        unif or mly(in t)in bounded intervals.

        It t ur ns out[2-3]t hat t his sol ution Ntcoincides wit h t he sol ution of t he equation (5)obtained by using t he Str atonovich integral

        This outco me implies that Ntis the sol ution of the following modified the It?equation,

        whereσ′denotes the derivative ofσ(t,x)w.r.t.x[4].

        Theref ore,fro m t his point of view it seems reasonable to use t he Str atonovich inter pretation of t he equation(6),and not t he It?inter pretation of t he equation(5)as t he model f or t he original white noise equation.However,t he specific f eat ure of t he It?model of“not l ooking into t he f ut ure”[5]seems to be a reason f or choosing the It?interpretation in many cases,for example in biology[6].Note that equation(5)and(7)coincide ifσ(t,x)does not depend on x[7].

        By t he Theorem 2,we can find t hat t here is no second or der ter m in t he Str atonovich analogue of the It?transf or mation for mula.It can be said that the Str atonovich integral has the advantage of leading to or dinary chain r ule for mulas under a transf or mation.This advantage makes the Str atonovich integral good to use f or exa mple in connection wit h stochastic diff erential equations on manif ol ds[8-9].However,the Stratonovich integrals are not martingales,but the It?integrals are.This gives the It?integral an important computational advantage,even though it does not behave so nicely under transfor mations.

        [1] Kallianpur G,Karandikar R L.Introduction to Option Pricing Theor y[M].Boston:Bir kh?user,2000.

        [2] Wong E,Zakaim.Riemann-Stieltjes approximations of stochastic integrals[J].Probability Theory and Related Fields,1969,12(2):87-97.

        [3] Sussmann H J.On the gap bet ween deter ministic and stochastic ordinary differential equations[J].The Annals of Probability,1978,6(1):19-41.

        [4] Stratonovich R L.A new representation for stochastic integrals and equations[J].SIA M Jour nal on Control,1966,4(2):362-371.

        [5] Ber nt?ksendal.Stochastic Differential Equations[M].6 ed.Beijing:World Publishing Cor poration,2006.

        [6] Turellim.Random environments and stochastic calculus[J].Theoretical Population Biology,1977,12(2):140-178.

        [7] Benth F E.Option Theory with Stochastic Analysis[M].Heidelberg:Springer-Verlag,2004.

        [8] El worthy K D.Stochastic Differential Equations on Manif olds[M].Cambridge:Cambridge University Press,1982.

        [9] Ikeda N,Watanabe S.Stochastic Differential Equations and Diff usion Processes[M].2 ed.Amster dam:North-Holland/Kodansha,1989.

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