Approximative Theorem of Incomplete Riemann-Stieltjes Sum Of

Approximative Theorem of Incomplete Riemann-Stieltjes Sum Of

Approximative Theorem of Incomplete Riemann-Stieltjes Sum of Stochastic Integral Jingwei Liu∗ ( School of Mathematics and System Sciences, Beihang University, Beijing,100083,P.R.China) March 14,2018 Abstract: The approximative theorems of incomplete Riemann-Stieltjes sums of Ito stochastic integral, mean square integral and Stratonovich stochas- tic integral with respect to Brownian motion are investigated. Some sufficient conditions of incomplete Riemann-Stieltjes sums approaching stochastic inte- gral are developed, which establish the alternative ways to converge stochas- tic integral. And, Two simulation examples of incomplete Riemann-Stieltjes sums about Ito stochastic integral and Stratonovich stochastic integral are given for demonstration. Keywords: Ito stochastic integral; Stratonovich stochastic integral; Mean square integral; Definite integral; Brownian motion ;Riemann-Stieltjes sum; Convergence in mean square; Convergence in probability 1 Introduction Ito stochastic integral and Stratonovich stochastic integral take important arXiv:1803.05182v2 [math.PR] 17 Mar 2018 roles in stochastic calculus and stochastic differential equation. Both of them can be expressed in the limit of Riemann-Stieltjes sum [1-4], though they can be modeled by martingale and semimartingale theory[5-13]. [14] pro- poses the deleting item Riemann-Stieltjes sum theorem of definite integral, which can also be called incomplete Riemann-Stieltjes sum. As definitions of martingale and semimartingale are still relative to integral including defi- nite integral, and the stochastic Riemann-Stieltjes sum limit provides conve- nient way for numeral solution, the most important, stochastic integral and ∗ Corresponding Author: Email: [email protected] (J.W Liu) 1 stochastic differential equation are mutual determined , is there alternative way of stochastic integral to describe the stochastic integral and stochas- tic differential equation convergence in mean square limit or in probability sense? In fact, [14] has revealed that definite integral is not uniquely deter- mined by differential equation according to deleting item Riemann-Stieltjes sum theorem. Furthermore, there is another stochastic integral, mean square integral [15-18], is involved in stochastic calculus. Motivated by above rea- sons, we concentrate on the limits of incomplete Riemann–Stieltjes sums of Ito stochastic integral, Stratonovich stochastic integral and mean square in- tegral. The rest of the paper is structured as follows: Section 2 briefly reviews definitions of Ito stochastic integral, mean square integral and Stratonovich stochastic integral. Section 3 constructs incomplete Riemann-Stieltjes sum approximating theorems of Ito stochastic integral, mean square integral and Stratonovich stochastic integral. Section 4 extends incomplete Riemann- Stieltjes sum approximating theorem of stochastic integral. Simulations of incomplete Riemann-Stieltjes sum approximating are demonstrated in Sec- tion 5 and discussion is given in Section 6. 2 Brief review of stochastic integral Let (Ω, , ( ) ,P ) be a probability space where ( ) is a complete F Ft t∈[0,T ] Ft t∈[0,T ] right continuous filtration generated by 1-dimensional standard Brownian motion (or Wiener process) (Bt)t∈[0.T ] and each t containing all P null sets of . A stochastic process Φ(ω, t) is a predictableF or non-anticipating− F0 process if Φ(ω, t) ( t) for each t, where ω Ω, which is also called adapted stochastic process.∈ And,F denote a stochastic∈ process X( , t) as X(t)= X , · t X(ω, t), Φt , Φ(ω, t). Denote l.i.m as limit in mean square. Definition 1 Suppose that Φ( , t) is a non-anticipating stochastic process with respect to the Brownian motion· B . Let = t , t , , t be the t △n { 0 1 ··· n} partition : 0 = t < t < < t = T of [0, T ], △ t = (t t ), 0 1 ··· n i i+1 − i n = max △ ti. The Itˆo stochastic integral of the stochastic process k△ k 0≤j≤n−1 Φ( , t) is denoted by · T n−1 ΦtdWt = l.i.m Φ(Bti , ti)(Bti+1 Bti ) (1) Z k△nk−→0 − 0 Xi=0 2 Definition 2 (Mean-square integration) Let X( , t) be a second-order stochastic process on Ω [0, T ], which is E[X2( , t)] < ·+ . For any partition × · ∞ :0 = t0 < t1 < < tn = T , n = max (ti+1 ti), and, for any ··· k△ k 0≤i≤n−1 − T ui [ti, ti+1]. The mean–square integral 0 X(t)dt is defined as the limit in mean∈ square R T n−1 X(t)dt = l.i.m X(ui)(ti+1 ti) (2) Z k△nk−→0 − 0 Xi=0 provided that the limit exists for arbitrary partition and selected points ui. X(t) is called mean-square integrable on [0, T ]. B + B If Φ(B , t ) is replaced by Φ( ti ti+1 , t ) in Itˆo stochastic integral, it ti i 2 i will be Stratonovich stochastic integral. Definition 3 Suppose that Φ( , t) is a non-anticipating stochastic process · with respect to the Brownian motion Bt. Let n = t0, t1, , tn be the partition : 0 = t < t < < t = T of△ [0, T ],{ △ t ···= (t } t ), 0 1 ··· n i i+1 − i n = max △ ti. The Stratonovich integral of the stochastic process Φ(k△, t)k is denoted by · T n−1 Bti + Bti+1 Φt dWt = l.i.m Φ( , ti)(Bti+1 Bti ) (3) Z ◦ k△nk−→0 2 − 0 Xi=0 In fact, Stratonovich stochastic integral has an alternative formula as fol- lows [3,4], it can be achieved by derivation while Φ(x, t) is 1-order differential with x [8]. T n−1 Φti+1 + Φti Φt dWt = l.i.m (Bti+1 Bti ) (4) Z ◦ k△nk−→0 2 − 0 Xi=0 ∂Φ(x, t) If function Φ(x, t) continuous in t, having the continuous derivative . ∂x There is a relationship among Itˆo stochastic integral, mean square integral and Stratonovich stochastic integral as follows. T T 1 T ∂Φ(B(t), t) Φ(B(t), t) dB(t)= Φ(B(t), t)dB(t)+ dt (5) Z0 ◦ Z0 2 Z0 ∂B(t) Denote J = 0, 1, 2, , n 1 . For a fixed natural number K N +(0 < { ··· − } ∈ K < n), denote JK = i1, , iK J, and J JK = 0, 1, 2, , n 1 i , , i . { ··· } ⊂ \ { ··· − }\ { 1 ··· K } 3 We will investigate the limit of incomplete Riemann-Stieltjes sums in mean square convergence, Φ(B , t )(B B ) (6) ti i ti+1 − ti JX\JK X(u )(t t ) (7) i i+1 − i JX\JK and, B(t )+ B(t ) Φ( j j+1 , t )[B(t ) B(t )] (8) 2 j j+1 − j JX\JK In [1], Ito Stochastic integral is originally defined on B(ω, t) with no mov- ing discontinuity, such that E[B(ω, t) B(ω,s)] = 0 , E[B(ω, t) B(ω,s)]2 = t s . Our investigation on stochastic− integral with incomplete− Riemann- Stieltjes| − | sum will show the asymptotical limit behavior of discontinuity subin- tervals on continuous path of Brownian motion, which is, for any partition of [s, t], E[B B ] = 0, and E[B B ]2 t s , as n + ti+1 − ti ti+1 − ti −→ | − | −→ ∞ JX\JK JX\JK . Our motivation is also different from the δ fine belated partial division of − [s, t] in [19]. Here, [s, t] = [0, T ]. Two convergence properties, convergence in mean square (l.i.m or =m.s ⇒ ) and convergence in probability ( P ), and two kinds of inequalities are −→ involved in the discussion [20,21]. Lemma 1. Suppose that X =m.s X, Y =m.s Y . For a, b R, then n ⇒ n ⇒ ∀ ∈ aX + bY =m.s aX + bY. (9) n n ⇒ Lemma 2. Suppose that X P X, Y P Y . For a, b R, then n −→ n −→ ∀ ∈ aX + bY P aX + bY. (10) n n −→ Lemma 3. (H¨older Inequality) Suppose that ξ, η are two random variables on (Ω, ,P ), for any real numbers p, q R satisfying 1 < p,q < 1 1 F ∈ + , and + = 1, ∞ p q E ξη (E1/p ξ p)(E1/q η q). (11) | | ≤ | | | | 4 If E ξ p < + and E η q < + , then “=” holds only when ξ = 0 a.s. or η =| 0| a.s. ,or∞ there exists| | constant∞ C so that ξ p = C η q a.s. | | | | Lemma 4. (Minkowski Inequality) Suppose that ξ, η are two random variables on (Ω, ,P ), 1) For any realF number p 1 , ≥ E1/p ξ + η p E1/p ξ p + E1/p η p. (12) | | ≤ | | | | If E( ξ p + η p) < + , then “=” holds only when p> 1, ξ = 0 a.s. or η =0 a.s. ,or| | there| | exists∞ constant C > 0 so that ξ = Cη a.s.;when p = 1, ξη 0 ≥ a.s. 2) For 0 <p< 1, E ξ + η p E ξ p + E η p). (13) | | ≤ | | | | If E( ξ p + η p) < + , then “=” holds if and only if ξη = 0 a.s. | | | | ∞ 3 Incomplete Riemann-Stieltjes Sum approx- imating of Stochastic integral Since Brownian motion is of unbounded variation, the convergence of Riemann- Stieltjes sum of Ito stochastic integral is not in usual Riemann-Stieltjes sense, but something like Lebesgue sense. It is first defined on a class of simple func- tions, then extended to a large class of functions with isometry [1,6]. The following notation is from [13]. Denote ∞ L = Φ:Φ= f (ω)I (t)+ f (ω)I (t), 0= t < t < < t . 0 { 0 0 k (tk,tk+1] 0 1 ··· n ↑∞ Xk=1 F F sup0≤k<∞ fk < , f0 0, fk k { } ∞ ∈ ∈ } (14) Lp = Φ:Φ=(Φt)t≥0, Φt(ω) Φ(ω, t) is measurable and (Ft) adapted { E T ≡ p process, and for T, 0 Φt dt < ,p 1. ∀ R | | ∞} ≥ (15) Lp,T = Φ:Φ=(Φt)t≥0, Φt(ω) Φ(ω, t) is measurable and (Ft) adapted { E T p ≡ process, 0 Φt dt < ,p 1.

View Full Text

Details

  • File Type
    pdf
  • Upload Time
    -
  • Content Languages
    English
  • Upload User
    Anonymous/Not logged-in
  • File Pages
    26 Page
  • File Size
    -

Download

Channel Download Status
Express Download Enable

Copyright

We respect the copyrights and intellectual property rights of all users. All uploaded documents are either original works of the uploader or authorized works of the rightful owners.

  • Not to be reproduced or distributed without explicit permission.
  • Not used for commercial purposes outside of approved use cases.
  • Not used to infringe on the rights of the original creators.
  • If you believe any content infringes your copyright, please contact us immediately.

Support

For help with questions, suggestions, or problems, please contact us