Orthogonal Expansion of Multiple Ito Stochastic Integrals
Author(s):
Konstantin Alexandrovich Rybakov
Moscow Aviation Institute (National Research University)
rkoffice@mail.ru
Abstract:
Based on the properties of Hermite polynomials, which are orthogonal with
respect to the probability density of the normal distribution,
and Charlier polynomials, which are orthogonal
with respect to the Poisson distribution, a representation of multiple Ito
stochastic integrals by Wiener and Poisson processes
in the form of orthogonal series is proposed.
Keywords
- iterated Ito stochastic integral
- multiple Ito stochastic integral
- orthogonal expansion
- Poisson process
- Wiener process
References:
- Ito, K. Multiple Wiener integral. Journal of the Mathematical Society of Japan, 1951, vol. 3, no. 1, pp. 157-169
- Hida, T., Ikeda, N. Analysis on Hilbert space with reproducing kernel arising from multiple Wiener integral. Proc. 5th Berkeley Symp. on Math. Stat. and Prob. 1967, vol. II, part 1, pp. 117-143
- Hu, Y. -Z., Meyer, P. -A. Sur les integrales multiples de Stratonovitch. Seminaire de probabilites, 1988, vol. 22, pp. 72-81
- Budhiraja, A. S. Multiple stochastic integrals and Hilbert space valued traces with applications to asymptotic statistics and non-linear filtering. Ph. D. Diss., The University of North Carolina, Chapel Hill, 1994
- Delgado, R. Multiple Ogawa, Stratonovich and Skorohod anticipating integrals. Stochastic Analysis and Applications, 1998, vol. 16, no. 5, pp. 859-872
- Farre, M., Jolis, M., Utzet, F. Multiple Stratonovich integral and Hu-Meyer formula for Levy processes. The Annals of Probability, 2010, vol. 38, no. 6, pp. 2136-2169
- Milstein, G. N. Numerical Integration of Stochastic Differential Equations. Kluwer Academic Publ., 1995
- Kloeden, P. E., Platen, E. Numerical Solution of Stochastic Differential Equations. Springer-Verlag, 1995
- Averina, T. A. Statisticheskoe modelirovanie reshenii stokhasticheskikh differentsial’nykh uravnenii i sistem so sluchainoi strukturoi [Statistical Modeling of Solutions of Stochastic Differential Equations and Systems with a Random Structure]. Novosibirsk, Siberian Branch of the Russian Academy of Sciences Publ., 2019
- Kuznetsov, D. F. On numerical modeling of the multidimensional dynamic systems under random perturbations with the 1. 5 and 2. 0 orders of strong convergence. Automation and Remote Control, 2018, vol. 79, no. 7, pp. 1240-1254
- Kuznetsov, D. F. On numerical modeling of the multidimentional dynamic systems under random perturbations with the 2. 5 order of strong convergence. Automation and Remote Control, 2019, vol. 80, no. 5, pp. 867-881
- Kuznetsov, D. F. Strong approximation of iterated Ito and Stratonovich stochastic integrals based on generalized multiple Fourier series. Application to numerical solution of Ito SDEs and semilinear SPDEs. Differencialnie Uravnenia i Protsesy Upravlenia, 2020, no. 4, pp. A. 1-A. 606
- Rybakov, K. A. Applying spectral form of mathematical description for representation of iterated stochastic integrals. Differencialnie Uravnenia i Protsesy Upravlenia, 2019, no. 4, pp. 1-31. (In Russ. )
- Rybakov, K. A. Using spectral form of mathematical description to represent Stratonovich iterated stochastic integrals. Smart Innovation, Systems and Technologies, vol. 217, pp. 287-304. Singapore, Springer, 2021
- Rybakov, K. A. Application of Walsh series to represent Stratonovich iterated stochastic integrals. IOP Conference Series: Materials Science and Engineering, 2020, vol. 927, id 012080
- Balakrishnan, A. V. Applied Functional Analysis. Springer-Verlag, 1981
- Gikhman, I. I., Skorokhod, A. V. Introduction to the Theory of Random Processes. Dover Publ., 1997
- Bateman, H., Erdelyi, A. Higher Transcendental Functions, vol. 2. McGraw-Hill Book Company, 1953
- Dobrushin, R. L., Minlos, R. A. Polynomials in linear random functions. Russian Mathematical Surveys, 1977, vol. 32, no. 2, pp. 71-127
- Pugachev, V. S., Sinitsyn, I. N. Stochastic Systems: Theory and Applications. World Scientific, 2002