Stochastic Analysis for Vector-valued Generalized Grey Brownian Motion

  • This dissertation presents a generalization of the generalized grey Brownian motion with componentwise independence, called a vector-valued generalized grey Brownian motion (vggBm), and builds a framework of mathematical analysis around this process with the aim of solving stochastic differential equations with respect to this process. Similar to that of the one-dimensional case, the construction of vggBm starts with selecting the appropriate nuclear triple, and construct the corresponding probability measure on the co-nuclear space. Since independence of components are essential in constructing vggBm, a natural way to achieve this is to use the nuclear triple of product spaces: \[ \mathcal{S}_d(\mathbb{R}) \subset L^2_d(\mathbb{R}) \subset \mathcal{S}_d'(\mathbb{R}), \] where \( L^2_d(\mathbb{R}) \) is the real separable Hilbert space of \( \mathbb{R}^d \)-valued square integrable functions on \( \mathbb{R} \) with respect to the Lebesgue measure, \( \mathcal{S}_d(\mathbb{R}) \) is the external direct sum of \(d\) copies of the nuclear space \(\mathcal{S}(\mathbb{R})\) of Schwartz test functions, and \(\mathcal{S}_d'(\mathbb{R})\) is the dual space of \(\mathcal{S}_d(\mathbb{R})\). The probability measure used is the the \(d\)-fold product measure of the Mittag-Leffler measure, denoted by \(\mu_{\beta}^{\otimes d}\), whose characteristic function is given by \[ \int_{\mathcal{S}_d'(\mathbb{R})} e^{i\langle\omega,\varphi\rangle}\,\text{d}\mu_{\beta}^{\otimes d}(\omega) = \prod_{k=1}^{d}E_\beta\left(-\frac{1}{2}\langle\varphi_k,\varphi_k\rangle\right),\qquad \varphi\in \mathcal{S}_d(\mathbb{R}), \] where \( \beta\in(0,1] \), and \( E_\beta \) is the Mittag-Leffler function. Vector-valued generalized grey Brownian motion, denoted by \( B^{\beta,\alpha}_{d}:=(B^{\beta,\alpha}_{d,t})_{t\geq 0}\), is then defined as a process taking values in \( L^2(\mu_{\beta}^{\otimes d};\mathbb{R}^d) \) given by \[ B^{\beta,\alpha}_{d,t}(\omega) := (\langle\omega_1,M^{\alpha/2}_{-}1\!\!1_{[0,t)}\rangle,\dots,\langle\omega_d,M^{\alpha/2}_{-}1\!\!1_{[0,t)}\rangle),\quad \omega\in\mathcal{S}_d'(\mathbb{R}), \] where \( M^{\alpha/2} \) is an appropriate fractional operator indexed by \( \alpha\in(0,2) \) and \( 1\!\!1_{[0,t)} \) is the indicator function on the interval \( [0,t) \). This process is, in general, not the aforementioned \(d\)-dimensional analogues of ggBm for \(d\geq 2\), since componentwise independence of the latter process holds only in the Gaussian case. The study of analysis around vggBm starts with accessibility to Appell systems, so that characterizations and tools for the analysis of the corresponding distribution spaces are established. Then, explicit examples of the use of these characterizations and tools are given: the construction of Donsker's delta function, the existence of local times and self-intersection local times of vggBm, the existence of the derivative of vggBm in the sense of distributions, and the existence of solutions to linear stochastic differential equations with respect to vggBm.

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Metadaten
Author:Karlo Orge
URN:urn:nbn:de:hbz:386-kluedo-71399
DOI:https://doi.org/10.26204/KLUEDO/7139
Advisor:Martin Grothaus
Document Type:Doctoral Thesis
Language of publication:English
Date of Publication (online):2023/02/02
Year of first Publication:2023
Publishing Institution:Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau
Granting Institution:Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau
Acceptance Date of the Thesis:2023/01/27
Date of the Publication (Server):2023/02/03
Page Number:69
Faculties / Organisational entities:Kaiserslautern - Fachbereich Mathematik
DDC-Cassification:5 Naturwissenschaften und Mathematik / 510 Mathematik
MSC-Classification (mathematics):26-XX REAL FUNCTIONS [See also 54C30] / 26Axx Functions of one variable / 26A33 Fractional derivatives and integrals
33-XX SPECIAL FUNCTIONS (33-XX DEALS WITH THE PROPERTIES OF FUNCTIONS AS FUNCTIONS) (For orthogonal functions, see 42Cxx; for aspects of combinatorics see 05Axx; for number-theoretic aspects see 11-XX; for representation theory see 22Exx) / 33Exx Other special functions / 33E12 Mittag-Leffler functions and generalizations
46-XX FUNCTIONAL ANALYSIS (For manifolds modeled on topological linear spaces, see 57Nxx, 58Bxx) / 46Fxx Distributions, generalized functions, distribution spaces [See also 46T30] / 46F12 Integral transforms in distribution spaces [See also 42-XX, 44-XX]
46-XX FUNCTIONAL ANALYSIS (For manifolds modeled on topological linear spaces, see 57Nxx, 58Bxx) / 46Fxx Distributions, generalized functions, distribution spaces [See also 46T30] / 46F25 Distributions on infinite-dimensional spaces [See also 58C35]
60-XX PROBABILITY THEORY AND STOCHASTIC PROCESSES (For additional applications, see 11Kxx, 62-XX, 90-XX, 91-XX, 92-XX, 93-XX, 94-XX) / 60Gxx Stochastic processes / 60G22 Fractional processes, including fractional Brownian motion
60-XX PROBABILITY THEORY AND STOCHASTIC PROCESSES (For additional applications, see 11Kxx, 62-XX, 90-XX, 91-XX, 92-XX, 93-XX, 94-XX) / 60Hxx Stochastic analysis [See also 58J65] / 60H10 Stochastic ordinary differential equations [See also 34F05]
Licence (German):Creative Commons 4.0 - Namensnennung (CC BY 4.0)