Numerical solution of second-order stochastic differential equations with Gaussian random parameters

Journal of Linear and Topological Algebra Vol. 2, No. 4, 23, 223-235 Numerical solution of second-order stochastic differential equations with Gaussian random parameters R. Farnoosh a, H. Rezazadeh a,, A. Sobhani a and D. Ebrahimibagha b a School of Mathematics, Iran University of Science and Technology, 6844, Tehran, Iran. b Department of Mathematics, Center Branch, Islamic Azad university, Tehran, Iran. Abstract. In this paper, we present the numerical solution of ordinary differential equations or SDEs, from each order especially second-order with time-varying and Gaussian random coefficients. We indicate a complete analysis for second-order equations in special case of scalar linear second-order equations damped harmonic oscillators with additive or multiplicative noises. Making stochastic differential equations system from this equation, it could be approximated or solved numerically by different numerical methods. In the case of linear stochastic differential equations system by Computing fundamental matrix of this system, it could be calculated based on the exact solution of this system. Finally, this stochastic equation is solved by numerically method like Euler-Maruyama and Milstein. Also its Asymptotic stability and statistical concepts like expectation and variance of solutions are discussed. c 23 IAUCTB. All rights reserved. Keywords: Stochastic differential equation, linear equations system, Gaussian random variables, damped harmonic oscillators with noise, multiplicative noise. 2 AMS Subject Classification: 65C3, 65U5, 6H, 65M25.. Introduction One of the most important and applicable concepts in various sciences is Newtons second law of motion which relates force and acceleration together. Therefore, second-order differential equations are most common in various scientific applications, As we can see in some articles, the famous and well-known differential equations of second-order such as OrnsteinUhlenbeck process and Random harmonic oscillator have been solved by different methods like as Monte Carlo and other numerical methods [, 6, 4]. The study Corresponding author. E-mail address: hr rezazadeh@mathdep.iust.ac.ir H. Rezazadeh. Print ISSN: 2252-2 c 23 IAUCTB. All rights reserved. Online ISSN: 2345-5934 http://jlta.iauctb.ac.ir

224 H. Rezazadeh et al. / J. Linear. Topological. Algebra. 24 23 223-235. of numerical methods of second-order ordinary differential equations is one of the most applicable branches in numerical analysis issues[9]. With attention to stochastic essence of almost all physical phenomena, the most interesting advances in recent years are the development and extension of previous methods to stochastic systems [2]. Numerical methods have to replace discrete-time dynamics in place of continuous-time with generating values at times t, t,..., t n. In this process, t and t n, are fixed points. Some criterions for specification a good numerical method are order of convergency, comparison with exact solution and produced error of a used method and determining its discrete-time dynamics which has an appropriate stationary density as close as possible to that of the corresponding continuous-time system that Wiener chaos expansion WCE, is one of these methods[4],[7]. The differential equation which describe second-order systems contains some parameters known as damping. The stationary density is completely independent of damping, but dynamical quantities, and the specially the final numerical algorithms, are strongly dependent on it. With some change of variables, the system becomes first order one. In this paper, we intend to extend our previous work about solving second-order linear stochastic differential equation [6], to solve second-order stochastic differential equation By first-order stochastic linear system equation which has been mentioned in various books like [], [], [3] and [5]. That is, we consider X t R n and X t L 2 n, T, as a stochastic process and unique solution of following S.D.E: dx t = At.X t Btdt Ct.X t DtdW X = X. t T such that W., is a m-dimensional Brownian motion. Afterwards, By construction fundamental or Hamiltonian matrix for this system, we solve it with numerical methods as EulerMaruyamaE.M., Milstein and Rung-Kutta method. finally, in the end of our conclusions, we investigate some statistical properties like expectation and variance by numerical simulation like predictor-corrector E.M.[8], and will have a comparison between exact solution and its solution and find least square error for this equations. This paper is organized as follow. In section 2, we consider the stochastic linear equation system of stochastic linear second-order equation which has been mentioned in various books like [3] and [] and latest articles[3] and [5]. Afterwards, by construction fundamental matrix for this system we solve it numerically by Rung-Kutta method. In this section we consider second order S.D.E. examples solve them by this mentioned method and stochastic numerical simulation like predictor-corrector EulerMaruyama and Milstein method [8]. Also, we take a discussion about expectation, variance of these equations solutions. In final section, the conclusion of this paper has been said again. 2. Making Stochastic Differential Equation System Let the general form of a second-order stochastic differential equation is defined such this equation: Ẍt = fx t, t ˆfX t, tξtẋt gx t, t ĝx t, tξtx t hx t, t, X t = X, Ẋ = X. 2

H. Rezazadeh et al. / J. Linear. Topological. Algebra. 24 23 223-235. 225 such that we define hx t, t = hx t, t ĥx t, tξt and X t is a -dimensional stochastic process defined on closed time interval [, T ] and the real functions fx t, t, gx t, t, hx t, t and also ˆfX t, t, ĝx t, t, ĥx t, t, are stochastic integrable functions. In special case, It shall be considered equation of the following form which has been discussed in[3]: Ẍt = fx t η ˆf 2 X t, tẋt ε ˆfX t, tξt, X t = X, Ẋ = X. 3 The damping parameter and The amplitude of the random forcing are denoted by η and ε respectively such that related to the temperature T and damping coefficient η by the fluctuation-dissipation relation[7], We have ε 2 = 2ηKT. ξt, is defined as White noise that has derivative relation with Wiener process W t = dw dt = ξt, Definition 2. Consider the m-dimensional vector W t of real stochastic processes W i t, i =, 2,..., m. It is named Wiener process or Brownian motion if; a W = a.s. almost sure with probability one, b W t W s is normal distribution i.e.w N, t s, for all s t c The random variables W t, W t 2 W t,..., W t n W t n, for times t t 2... t n, are independent increments. From this definition, about expectation and variance related to W i t, it could be concluded that E[W i t] =, E[W 2 i t] = t for i =, 2,..., m. 4 The expectation integral for these functions has these properties E[ξt, ξs] = δ s t, E[W s, W t] = mins, t. We can write 2, as a pair of first-order equations for X t and, the position and velocity stochastic processes: dx t = dt, d = fx t, t gx t, tx t hx t, t dt ˆf, t ĝx t, tx t ĥx t, t dw t. 5 With initial conditions X t = X, V = X. Now the second-order SDE2, could be written again as a first-order system by matrix notation. d = ΓX t, t dt Σ, t hx t, t dwt. 6 ĥx t, t

226 H. Rezazadeh et al. / J. Linear. Topological. Algebra. 24 23 223-235. Where we have ΓX t, t = gx t, t fx t, t, ΣX t, t = ĝx t, t ˆfX. t, t If ΓX t, t = Γt and ΣX t, t = Σt, we reach to a stochastic linear equation system: d = Γt ht dt Σt dwt. 7 ĥt If in special case, we have d = Γt dt dw t, 8 ĥt This special case is a better model of Brownian movement which is provided by the OrnsteinUhlenbeck equation. For solving this matrix form 8, it could be done by different numerical methods like Euler, Milstein or even Rung-kutta that in [6], it was done that is named linear second-order SDEs in narrow sense case. As another special cases, if we have d = Γt dt Σt dw t. 9 This stochastic differential equation system is well-known to Geometric Brownian Motion But as we will observe later, this system just in special case has an exact solution like Black-Scholes model in -dimentional case. Now we perform the same work in extended case on interval[, T ], to solve the system of equations27 explicitly and numerically. Suppose time interval has been separated to equal subintervals [t i, t i ], i =, 2,..., n and we define the following one step recursive equation even for general case6. Xi V i Xi V i = ΓX i, t i Xi V i ti ΣX i, t i hx i, t i Xi V i Wi. ĥx i, t i such that t i = t i t i = T n, and according to Wiener process definition: W i = W i W i N, t i. With the next theorem, we want to express the existence and uniqueness of second-order stochastic differential equations based on some conditions. Theorem 2.2 Suppose that At.X t Bt : R n [, T ] R n and Ct.X t Dt : R n [, T ] M m n are continuous and satisfy the following properties:

H. Rezazadeh et al. / J. Linear. Topological. Algebra. 24 23 223-235. 227 At. ˆX t t L. X t t ˆX t t, 2 Ct.X t t ˆX t t L. X t t ˆX t t. for all t T, X t, ˆX t R n 2 At.X t Bt L X t, Ct.X t Dt L X t. for all t T, X t R n, for some suitable L R. Let X R n is a random variable such that: E[X 2 ] < so, There exist a unique solution X t L 2 n, T of following S.D.E: dx t = At.X t Btdt Ct.X t DtdW X = X. t T where W., is a m-dimensional Brownian motion[6]. Remark Moreover, in special case if each one of matrixes gets its supreme value in a closed interval, that is; Sup Bt, Dt, At, Ct } < t T Therefore, AtX t Bt and CtX t Dt satisfy the hypotheses of uniqueness and Existence theorem [2] for Linear S.D.E. provided that E[X 2 ] <.Finally, this condition is necessary that for almost each W t, the random trajectories of linear S.D.E. when with attention to: dx t = At.X t Btdt Ct.X t DtdW, X = X ξ lim ξ Ct.X t Dt =, Converge uniformly on interval [, T ] to the trajectory of its corresponding ordinary differential equation. X t = At.X t Bt X = X.. 3 In general case, the following theorem that is named Dependence on parameters, indicates the concept of asymptotic stability for Linear stochastic systems. Theorem 2.3 Suppose for k =, 2, that A k tx t B k t and C k tx t D k t satisfy the hypotheses of existence and uniqueness theorem, with the same constant L which said as a real bond in theorem. Assume further that lim k E Xk X =

228 H. Rezazadeh et al. / J. Linear. Topological. Algebra. 24 23 223-235. and for each M >, such that X t M, lim max B k t Bt A k t At D k t Dt C k t Ct = k t T Finally suppose that X k solves: dx k t = A k tx t B k tdt D k tx t C k tdw X k = X k Then where X t is the unique solution of lim E max t T Xk t 2 =, dx t = At.X t Btdt Ct.X t DtdW, X = X. In addition, the analytic solution and least square error of O.D.E. Could be found in virtue of expectation and variance of S.D.E. solution such that in special Case, if A, B, Cand D contain continuous elements in [, T ], they get their finite maximum values in this interval. With attention to existence and uniqueness solution of Linear S.D.E., the equation has been brought in 8, has a closed and explicit solution which is found according to following theorem[2]: Theorem 2.4 dx t = At.X t Btdt Dt.dW 4 X = X = Ψt X Ψs Csds EsdW, 5 where Ψ is the Fundamental matrix of the following O.D.E. system: dψ dt = At.Ψ, Ψ = I. In other words, we have: and Ψ ij = δ ij = Ψ Ψ2 Ψ = 2 Ψ 22 Ψ 2 Ψ2 BtΨ AtΨ 2 BtΨ 2 AtΨ 22, i = j, i = j.

H. Rezazadeh et al. / J. Linear. Topological. Algebra. 24 23 223-235. 229 Consequently it turns out two second-order equations by different initial Conditions: Ψ = BtΨ At Ψ Ψ =, Ψ =, Ψ2 = BtΨ 2 At Ψ 2 Ψ 2 =, Ψ 2 = 6 so the explicit solution could be computed as follow X Ẋ Ψ Ψ 2 X = Ψ Ψ2 X ds Cs λγat λγt Hence, we should utilize this equality for equation solution: Ψ2 Ψ 2 detψ Ψ Ψ dw2 dw X = X t = Ψ X Ψ 2 X Ψ detψ Ψ 2Csds λγsdw 2 λγsdw 7 Ψ 2 detψ Ψ Csds λγsdw 2 λγsdw. the equation 6 are second-order Linear O.D.E. we could solve them by various methods like series solution respect to nonsingular point or Frobinios series respect to regular points.also, we could apply sinc method to solve directly this equation or convert it to a Linear system equation and solve it by 4th-order Rung kutta method. afterwards, we decide to Compute from equality 7 that it could be done by numerical methods like E.M. predictor-corrector E.M. and milstein method. Also in matrix form which is convenient for Matlab software, we could get the following recursive procedure. = Ψt X Ψs Csds EsdW s Ψ t i. i = X t i Ψs Csds EsdW s Ψ t i i = X t i Ψs Csds EsdW s Consequently, we could have: i = Xi = Ψt i Ψ t i X i i Ψs Csds EsdW s t i Xi = Ψt i Ψt i X i Ct i t i Et i W i 8 such that; δw i = W t i W t i = δt i ξ i ξ i N, The last approximation has been concluded from Independent Increment property of

23 H. Rezazadeh et al. / J. Linear. Topological. Algebra. 24 23 223-235. wiener process for any t, s [, T ]; W t W s = W t s N, t s. of course, W t could be computed by an infinite series of Haar functions with standard Gaussian random variables [6], or even have been expressed according to fundamental conditions in definition of Wiener process. Example Consider the stochastic differential equation :t [a, b], < a < b < X = βtξ 2 t X X =, X =. αtξ t t X γtξ 3 t 2 t ; that ξ 3 = λξ λξ 2, λ, and ξ i = dw i dt i =, 2, 3 are white noise. αt is a continuous function on interval[a, b]. At first, we convert this equation to a linear system, X = X 2, X 2 = αtξ t t X βtξ2 t X 2 t 2 t γtξ 3. dx t = At.X t Btdt F t.x t E t.dw F 2 t.x t E 2 t.dw 2 such that,at = t, F t = αt, F 2 t = t t t βt, t E t = γtλ, E2 t = γtλ and Bt = t 2 2 corresponding fundamental matrix for system is Ψ Ψ2 = Ψ 2 Ψ22 t t Ψ = AtΨ, Ψ = I Ψ Ψ 2 i j and Ψ Ψ 2 Ψ ij = δ ij = and so we have: 22 i = j Ψ = t t Ψ Ψ t, Ψ =, Ψ = Ψ2 =, t t Ψ2 Ψ t 2, Ψ 2 =, Ψ 2 =. These are special case of heun equation that multiplying in tt, they could be solved by robinious Series around t = since t = is a regular singular point for them, and their solutions is: Ψ 2 = t, Ψ = t lnt. Therefore, the matrix Ψ is indicated as follow: Ψ = t lnt t, Ψ lnt = t t lnt t lnt According to, we can conclude in narrow sense case: X X 2 X = Ψ X 2 Ψ s. s 2 ds dw s αsλ

H. Rezazadeh et al. / J. Linear. Topological. Algebra. 24 23 223-235. 23 3.5 3 mean of paths exact solution 5 individual paths 2.5 X 2.5.5.5.2.4.6.8 t Figure. The exact solution with mean of M = numerical solution by E.M. Method with N = 2 8 subinterval of [, ] and also 5 individual numerical solution has been shown.in this case the least square error is.235. dw 2 λαs that the solution of initial S.D.E. is X = X = X t Ψ Ψ 2 X Ψ s s 2 ds s dw dw 2 αsλ λαs with attention to this issue that expectation of itô Integral is zero so we have E[X t ] = X t lnt X t Ψ Ψ 2 Ψ s. ds with initial conditions and asymptotic stability of S.D.E., if αt, then implicit solution of it s correspond O.D.E. Ẍ = t Ẋ t t X t 2 t X =, Ẋ = s 2 s is the same expectation of S.D.E.: ss 2 t E[X t ] = t t lnt. s 2 ds t 2 s s 2 s ln sds = t t2. We have showed the maximum absolute errors in numerical solution of the example in the table for different N, where LEEM N h are least squares errors for E.M. method

232 H. Rezazadeh et al. / J. Linear. Topological. Algebra. 24 23 223-235. Table. Generated errors via Numerical methods E.M. andmilstein N E N EM h a E N M h b 2 7 5.2 2 3.24 2 2 8 2.36 2 2.9 2 2 9 2.3 2.22 2 2 2.2 2 4.24 3 a The value of least squares error generated by E.M. method. b The value of least squares error generated by Milstein method and LEM N h are least squares errors for Milstein method. Afterwards, we intend to express an explicitly solution of second-order linear SDEs systems. At first, let express the basic theorem related to general vector linear equation[2]. Theorem 2.5 Suppose X t R n is a stochastic process and consider the following S.D.E in L 2, T : dx t = At.X t Btdt Ct.X t DtdW, X = X. t T where W., is a m-dimensional Brownian motion, the square matrixes A, C M n n of time independent variable, and vector time dependent functions B, D R n. Therefore, the exact solution of this system is X t = Ψ t X Ψ s dy s, such that stochastic process Y t, and the fundamental matrix Ψ t M n n holds in following SDEs: dy t = Bt CtDtdt DtdW t, dψ t = AtΨ t dt CtΨ t dw t, Ψ = I. As we can see easily, with integration by part formula, dy could be represented based on a Lebesgue integral: dy t = Bt CtDtdt DtdW t = Bt CtDtdt DtW t W t D tdt = DtW t Bt CtDt W t D tdt. 9 2 Also,in virtue of zero value of itô integral expectation, E[X t ] = Mt, holds in following ODE: Ṁt = AtMt Bt, M = X. Unfortunately, the explicit solution for fundamental matrix of stochastic equations system is represented under some special conditions on related coefficients which don t hold in general case.

H. Rezazadeh et al. / J. Linear. Topological. Algebra. 24 23 223-235. 233 Theorem 2.6 Under previous theorem conditions, if At and Ct, are commutative matrixes, then there is an explicit stochastic solution for fundamental matrix is: Ψt = exp As C2 s ds 2 CsdW s. 2 Proof: On account of commutative matrixes, it s enough to do itô formula for Ψt = expy such that dy t = At C2 t 2 dt CtdW t. dψt = dexpy = expy dy 2 expy dy 2 22 = expy At C2 t 2 2 C2 t CtdW t = expy Atdt CtdW t and therefore the proof of theorem is completed.. Thus we propose to find and explicit solution for fundamental matrix equation which has been produced from linear equations system27, by appropriate change of variable. Before any thing, we pay attention to next theorem Theorem 2.7 The solution of28, such that Ct is a invertible matrix, could be represented as a function of following SDE: 23 24 dz t = P tdt QtdW t, P t = QtC t At 2 Ct2. 25 that Qt, is considered as a rotation matrix and Ψt = UZ t = U = exp Z t CtQ t. ât ˆbt Proof: Considering dz t = P tdt QtdW t, Qt = and applying ˆbt ât itô formula under change of variable method for UZ t = Ψt, on fundamental matrix equation28, we get: U P t 2 U Q 2 t = UAt U Qt = UCt Since Qt is a commutative and invertible matrix, therefore;u = UCtQ t and consequently U = exp Z t CtQ t. Afterwards, with substituting this solution in first equation of 26, we get 26 in other words, we conclude UCtQ tp t 2 UCtQ t 2 Q 2 t = UAt. P t = CtQ t At 2 Q tct 2 Q 2 t

234 H. Rezazadeh et al. / J. Linear. Topological. Algebra. 24 23 223-235. = QtC t At 2 Ct2. thus, the proof is completed.. Example 2 Consider the following stochastic model dx = 3 4 t2 X 2 dt tx 3/2 dw, X =. It could be checked that for this equation the necessary condition holds. according to 26u bt = tu 3/2. since u is just a function of Y, so it should be bt = t, u = 4 Y 2 and also at bt = or at =. thus dy = tdw and Y = sdw s Y. Finally X = uy = 4 sdw s Y 2 In the end, we represent the explicit solution of 27, based on the theorems 2.7 and2.5. Theorem 2.8 Suppose X t, T R 2 is a stochastic process and consider the following S.D.E in L 2, T. d = Γt ht dt Σt ĥt dwt, X V = x 27. x Whence, solution of this system is X t = X Ψt V Ψ s dy s, such that stochastic process Y t = Y Y 2, and the fundamental matrix Ψt M n n holds in following SDEs: dyt = ht Σt ĥt dt ĥt dwt, dψ t = ΓtΨ t dt ΣtΨ t dw t, Ψ = I. and also the solution of last equation is Ψt = UZ t = U = exp Z t CtQ t, that Z t, is the solution of following SDE, and Qt, is considered as a rotation matrix: 28 dz t = P tdt QtdW t, P t = QtΣ t Γt 2 Σt2. 29 That is, the fundamental matrix is Ψt = exp P sds QsdW s CtQ t 3. Conclusion As it was indicated in this paper, we performed a survey on stochastic ordinary differential equations from second-order with time-varying and Gaussian random coefficients. We indicated a complete analysis for stochastic second-order equations in special case of scalar linear second-order equations damped harmonic oscillators with additive or multiplicative noises. Afterwards, with making a system of stochastic differential equations from this mentioned equation, In the case of linear stochastic differential equations

H. Rezazadeh et al. / J. Linear. Topological. Algebra. 24 23 223-235. 235 system and by computing fundamental matrix of this system, it calculated based on the exact solution of this system. We approximated its solution based on a method with an analytical approach different from other numerical methods. Finally, this stochastic equation was solved by conventional numerically method like E.M. or Milstein. Also its asymptotic stability and statistical concepts like expectation and variance of solutions was discussed. Acknowledgements The authors would like to thank the referees for their valuable nice comments. Mean while, the authors thanks Dr. Mohammad hossein Beheshti from Islamic Azad University, Varamin branch for his suitable viewpoints. References [] M. P. Allen and D. J. Tildesley, Computer Simulation of Liquids, Oxford University Press, Oxford, UK, 997. [2] L. Arnold, Stochastic Differential Equations: Theory and Applications, Wiley, 974. [3] K. Burrage, I. Lenane, and G. Lythe, Numerical methods for second-order stochastic differential equations, SIAM J. SCI. Compute., Vol. 29, No., pp. 245264, 27. [4] R. Cairoli, J. Walsh, Stochastic integrals in the plane, in Acta Math., 34, pp. 83., 975. [5] Dongbin Xiu, D Daniel M. Tartakovsky, Numerical solution for differential equation in random domain, SIAM J. Sci. Compute. Vol. 28, No. 3, pp. 67-85 26. [6] Lawrence C. Evans.:An Introduction to Stochastic Differential Equations Version.2 24. [7] C. W. Gardiner, Handbook of Stochastic Methods for Physics, Chemistry, and the Natural Sciences, 3rd ed., Springer-Verlag, Berlin, 24. [8] D. J. Higham, An algorithmic introduction to numerical simulation of stochastic differ- ential equations, SIAM Review 43, 525-546, 2. [9] E. Hairer, S. P. Norsett, and G. Wanner, Solving Ordinary Differential Equations I: Nonstiff Problems, 2nd ed., Springer-Verlag, Berlin, 993. [] N. V. Krylov, Introduction to the Theory of Diffusion Processes, American Math Society, 995. [] J. Lamperti, A simple construction of certain diffusion processes, J. Math. Kyoto, 6-7, 964. [2] G. N. Milstein and M. V. Tretyakov, Quasi-symplectic methods for Langevin-type equations, IMA J. Numer. Anal., 23, pp. 593626, 23. [3] H. McKean, Stochastic Integrals, Academic Press, 969. [4] C. A. Marsh and J. M. Yeomans, Dissipative particle dynamics: The equilibrium for finite time steps, Europhys. Lett., 37, pp. 556, 997. [5] B. K. Oksendal, Stochastic Differential Equations: An Introduction with Applications, 4th ed., Springer, 995. [6] H. R. Rezazadeha, M. Magasedib, B. Shojaeec.Numerical Solution of Heun Equation Via Linear Stochastic Differential Equation, Journal of Linear and Topological Algebra Vol., No. 2, 79-89, 22. [7] Wuan Luo. Wiener Chaos Expansion and Numerical Solutions of Stochastic Partial Differential Equations. California Institute of Technology Pasadena, California,26.