Stochastic system identification is of great interest in the areas of control and communication. In stochastic system identification, a model of a dynamic system is determined based on given inputs and received outputs from the system, where stochastic uncertainties are also involved. The scope of the report is to consider continuous-time models used within control and communication and to estimate the model parameters from sampled data with high accuracy in a computational efficient way. Continuous-time models of systems controlled in a networked environment, stochastic closed-loop systems, and wireless channels are considered. The parameters of a transfer function based model for the process in a networked control system are first estimated by a covariance function based approach, relying upon the second order statistical properties of the output signal. Some other approaches for estimating the parameters of continuous-time models for processes in networked environments are also considered. Further, the parameters of continuous-time autoregressive exogenous models are estimated from closed-loop filtered data, where the controllers in the closed-loop are of proportional and proportional integral type, and where the closed-loop also contains a time-delay. Moreover, a stochastic differential equation is derived for Jakes's wireless channel model, describing the dynamics of a scattered electric field with the moving receiver incorporating a Doppler shift. / <p>Article I was still in manuscript form at the time of the defense.</p>

Neste trabalho, analisamos uma equação diferencial estocástica (EDE) do tipo Landau- Ginzburg: d&phi; = A&phi;+d&eta; (t, x), onde A é uma função definida no espaço das variáveis aleatórias &phi; (x, t) com (x, t) &isin; R X Zd. Toda a dissertação segue de perto as ideias encontradas no artigo [FdVOPS01]. Utilizando a teoria de análise estocástica (mais precisamente, a fórmula de Feynman- Kac) associamos a EDE acima com uma equação de evolução. Desta forma nosso estudo é resumido ao problema de determinação do espectro do gerador de um semigrupo de evolução. Para realizar esta análise utilizamos técnicas desenvolvidas na teoria quântica de campos. A esquematização do presente texto se dá da seguinte forma: Na introdução formulamos o nosso problema detalhadamente, fornecendo os aspectos da análise estocástica e da teoria de campos envolvidas. Também enunciamos um teorema que resume as propriedades espectrais que pretendemos obter. Nos Capítulos 2 e 3 fornecemos o aparato conceitual necessário para o desenvolvimento do problema inicial. Ainda no Capítulo 3, fazemos uma revisão rápida sobre um problema bem conhecido da mecânica quântica (modelo &phi;4), afim de estabelecer familiaridade com esta teoria. No Capítulo 4, inicialmente, nos restringimos à determinação de propriedades espectrais para o nosso problema no volume finito, e depois realizamos um procedimento chamado expansão em cluster para passar ao estudo do problema no volume infinito. No Capítulo 5 definimos o operador de Bethe-Salpeter, para então, no Capítulo 6, determinar propriedades do núcleo deste operador. Por fim, estas informações são utilizadas no Capítulo 7 para obtermos a caracterização espectral desejada. / Following [FdVOPS01], we study a stochastic Landau-Ginzburg differential equation of the form d&phi; = A&phi; + d&eta; (t, x), where A is a function defined on the space of random variables &Phi; (x, t), with (x, t) &isin; R X Zd. Using the stochastic analysis theory (more precisely, the Feynman-Kac formula) we are able to associate this stochastic differential equation (EDE) with an evolution equation. In this way, our study is resumed to the problem of determine the spectrum of the generator of an evolution semigroup. To do this, we use techniques developed in the quantum field theory. This work is organized as follows. In the Introduction we formulate our problem in detail, providing the aspects of the stochastic analysis and field theory that are involved. We also enunciate a theorem that resumes the spectral properties that we want to achieve. Chapters 2 and 3 are meant to provide the conceptual tools that are needed to the development of the initial problem. Yet in Chapter 3, we do a quick review of a known problem in quantum field (the model &phi;4), intending estabilish familiarity with this theory. Chapter 4 is restricted initially to the determination of spectral properties of our problem in the finite volume [T, T] X &and; &sub; R X Zd, and then we perform the cluster expansion in order to formulate the problem in infinite volume [T, T] X Zd. In Chapter 5 we define the Bethe-Salpeter operator and, in Chapter 6, we determine some properties of the kernel of this operator. This informations are used in Chapter 7 to obtain the desired spectral characterization.

The purpose of this thesis is to compare the effectiveness of several interest rate models in fitting the true value of interest rates. Up until 1990, the universally accepted models were the equilibrium models, namely the Rendleman-Bartter model, the Vasicek model, and the Cox-Ingersoll-Ross (CIR) model. While these models were probably considered relatively accurate around the time of their discovery, they do not provide a good fit to the initial term structure of interest rates, making them substandard for use by traders in pricing interest rate options. The fourth model we consider is the Hull-White one-factor model, which does provide this fit. After calibrating, simulating, and comparing these four models, we find that the Hull-White model gives the best fit to our data sets. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2014. / FAU Electronic Theses and Dissertations Collection

Neste trabalho, analisamos uma equação diferencial estocástica (EDE) do tipo Landau- Ginzburg: d&phi; = A&phi;+d&eta; (t, x), onde A é uma função definida no espaço das variáveis aleatórias &phi; (x, t) com (x, t) &isin; R X Zd. Toda a dissertação segue de perto as ideias encontradas no artigo [FdVOPS01]. Utilizando a teoria de análise estocástica (mais precisamente, a fórmula de Feynman- Kac) associamos a EDE acima com uma equação de evolução. Desta forma nosso estudo é resumido ao problema de determinação do espectro do gerador de um semigrupo de evolução. Para realizar esta análise utilizamos técnicas desenvolvidas na teoria quântica de campos. A esquematização do presente texto se dá da seguinte forma: Na introdução formulamos o nosso problema detalhadamente, fornecendo os aspectos da análise estocástica e da teoria de campos envolvidas. Também enunciamos um teorema que resume as propriedades espectrais que pretendemos obter. Nos Capítulos 2 e 3 fornecemos o aparato conceitual necessário para o desenvolvimento do problema inicial. Ainda no Capítulo 3, fazemos uma revisão rápida sobre um problema bem conhecido da mecânica quântica (modelo &phi;4), afim de estabelecer familiaridade com esta teoria. No Capítulo 4, inicialmente, nos restringimos à determinação de propriedades espectrais para o nosso problema no volume finito, e depois realizamos um procedimento chamado expansão em cluster para passar ao estudo do problema no volume infinito. No Capítulo 5 definimos o operador de Bethe-Salpeter, para então, no Capítulo 6, determinar propriedades do núcleo deste operador. Por fim, estas informações são utilizadas no Capítulo 7 para obtermos a caracterização espectral desejada. / Following [FdVOPS01], we study a stochastic Landau-Ginzburg differential equation of the form d&phi; = A&phi; + d&eta; (t, x), where A is a function defined on the space of random variables &Phi; (x, t), with (x, t) &isin; R X Zd. Using the stochastic analysis theory (more precisely, the Feynman-Kac formula) we are able to associate this stochastic differential equation (EDE) with an evolution equation. In this way, our study is resumed to the problem of determine the spectrum of the generator of an evolution semigroup. To do this, we use techniques developed in the quantum field theory. This work is organized as follows. In the Introduction we formulate our problem in detail, providing the aspects of the stochastic analysis and field theory that are involved. We also enunciate a theorem that resumes the spectral properties that we want to achieve. Chapters 2 and 3 are meant to provide the conceptual tools that are needed to the development of the initial problem. Yet in Chapter 3, we do a quick review of a known problem in quantum field (the model &phi;4), intending estabilish familiarity with this theory. Chapter 4 is restricted initially to the determination of spectral properties of our problem in the finite volume [T, T] X &and; &sub; R X Zd, and then we perform the cluster expansion in order to formulate the problem in infinite volume [T, T] X Zd. In Chapter 5 we define the Bethe-Salpeter operator and, in Chapter 6, we determine some properties of the kernel of this operator. This informations are used in Chapter 7 to obtain the desired spectral characterization.

本論文主要研究污染源追蹤和重構的反問題以及倒向隨機微分方程的數值求解。 / 論文的第一部份考慮污染源追蹤及重構的反問題。它的目的是重構反應對流擴散系統中的未知污染源的位置以及強度。污染源的追蹤和重構在工程、化學、生物以及環境等領域有廣泛的應用。我們將同時重構靜態單點污染源的位置以及強度。在本論文中，我們提出了一個基於對偶概率的算法，它將污染源追蹤重構的反問題轉化為Volterra積分反問題。對於污染源的位置和污染物釋放強度的可重構性，文中也進行了理論上的分析和討論。數值結果表明此方法是高效穩定的。隨後，我們將對偶概率方法推廣應用與追蹤和重構動態單點污染源隨時間的軌跡以及強度。數值結果顯示，我們的方法要比多數現有的方法為有效，計算成本也大大降低。 / 論文的第二部份討論倒向隨機微分方程的數值求解。倒向隨機微分方程在隨機控制、生物、化學反應，尤其是數理金融上有重要的應用。論文中所提出的數值方法，主要是基於倒向隨機微分方程的置換解的概念。置換解的適定性分析不涉及鞅表示論，從而更靈活，更容易推廣。利用置換解的理論，文中所涉及的誤差分析都不需要用到鞅表示論。對於一般的倒向隨機微分方程，我們提出了一種簡單的倒向算法，并證明了它是半階收斂的。但是，在算法的實際應用中只可能選取有限個基函數，從而帶入了截斷誤差。截斷誤差在簡單倒向算法中會隨時間累加，導致誤差是半階增長的。為了克服這個缺點，我們提出了一種新的算法。這種算法無需進行皮卡迭代，並且在理論上我們證明了，使用這個新的算法，截斷誤差是可控的，它不會隨時間增加。隨後，我們對馬爾科夫情況的倒向隨機微分方程提出了幾個高階的數值算法，並且給出了嚴格的誤差分析。我們的數值實驗結果表明，文中所提出的方法精度高，穩定性強，且計算成本小。 / In this thesis, we shall propose some numerical methods for solving two important classes of application problems, namely the inverse heat source problems and the backward stochastic differential equations. / The inverse heat source problems are to recover the source terms in a convection-diffusion- reaction system. These inverse problems have wide applications in many areas, such as engineering, chemistry, biology, pollutant tracking, and so on. We shall first investigate the simultaneous reconstruction of the location and strength of a static singular source. An adjoint probabilistic algorithm is proposed, which turns the inverse heat source problem into an inverse Volterra integral problem. The identifiability of the location and strength of a singular source is also discussed, and numerical results are presented to show the robustness and effectiveness of the method. Then we extend the adjoint probabilistic method to reconstruct the source trace and release history of a singular moving point source. Numerical examples show that the adjoint probabilistic method is more efficient and less expensive than most existing efficient numerical methods. / The second part of the thesis is devoted to numerical solutions of some nonlinear backward stochastic differential equations (BSDEs). BSDEs are widely used in various fields like stochastic control, biology, chemistry reaction, especially mathematical finance. Our numerical methods are based on a new framework about the transposition solution to BSDEs. The proof of the well-posedness of the transposition solution does not involve Martingale representation, neither does our error analysis for the numerical schemes proposed in this thesis. For general BSDEs, we first propose a simple backward scheme, which is proved to have an accuracy of half order. However, in the real application of the scheme, it is only possible to choose a finite subset of basis functions, which will generate truncation error. The truncation error accumulates backward in time, leading to the increment of the numerical error up to a half order. To overcome this drawback, we propose a new numerical scheme without Picard iterations and prove that the truncation error is bounded independent of time partitions. Afterwards, we propose some higher order schemes for Markovian BSDEs with rigorous error analysis. Finally, numerical simulations are presented to demonstrate that the proposed methods are accurate, stable and less expensive than most existing ones. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Wang, Shiping. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2013. / Includes bibliographical references (leaves 126-133). / Abstract also in Chinese. / Abstract --- p.i / Acknowledgement --- p.v / Chapter 1 --- Introduction to inverse heat source problems and BSDEs --- p.1 / Chapter 1.1 --- Inverse heat source problems --- p.2 / Chapter 1.2 --- Backward stochastic differential equations --- p.7 / Chapter 1.3 --- Outline of the thesis --- p.11 / Chapter Part I: --- Numerical Method for Inverse Heat Source Problem --- p.13 / Chapter 2 --- Inverse heat source: static point source --- p.14 / Chapter 2.1 --- Reformulation of the forward problem --- p.15 / Chapter 2.2 --- Inverse source problem and its identifiability --- p.21 / Chapter 2.2.1 --- Identifiability of partial time in one dimensional cases --- p.21 / Chapter 2.2.2 --- Identifiability of two dimensional cases --- p.25 / Chapter 2.3 --- Algorithm to solve the inverse problem --- p.26 / Chapter 2.4 --- Numerical experiments --- p.29 / Chapter 3 --- Inverse heat source: moving point source --- p.41 / Chapter 3.1 --- Reformulation of the problem --- p.42 / Chapter 3.2 --- Algorithm and numerical examples --- p.43 / Chapter 3.2.1 --- Algorithm to recover source trace and strength --- p.44 / Chapter 3.2.2 --- Numerical examples --- p.45 / Chapter Part II: --- Numerical Methods to Backward Stochastic Differential Equations --- p.55 / Chapter 4 --- Preliminaries --- p.56 / Chapter 4.1 --- Notations and definitions --- p.56 / Chapter 4.2 --- Useful lemmas and theorems --- p.60 / Chapter 4.3 --- Existing schemes for forward SDEs --- p.66 / Chapter 5 --- Numerical algorithms to BSDEs and error estimates --- p.68 / Chapter 5.1 --- A simple backward algorithm for BSDEs and its error estimate --- p.69 / Chapter 5.1.1 --- A simple backward algorithm --- p.69 / Chapter 5.1.2 --- Error estimate for simple backward scheme --- p.71 / Chapter 5.2 --- A new explicit backward algorithm for BSDEs and its error estimates --- p.85 / Chapter 5.2.1 --- A new explicit backward algorithm --- p.85 / Chapter 5.2.2 --- Error estimate for explicit backward scheme --- p.86 / Chapter 6 --- Higher order schemes of Markovian cases and error estimates --- p.91 / Chapter 6.1 --- Error estimate of 1-order scheme for Markovian BSDEs --- p.92 / Chapter 6.2 --- 2-order scheme for Markovian BSDEs and its error estimate --- p.100 / Chapter 6.2.1 --- 2-order scheme for Markovian BSDEs --- p.100 / Chapter 6.2.2 --- Error estimate of 2-order scheme --- p.102 / Chapter 7 --- Simulation results for BSDEs --- p.106 / Chapter 7.1 --- Basis functions --- p.107 / Chapter 7.2 --- Numerical simulations --- p.108 / Chapter 7.2.1 --- Application on option pricing --- p.108 / Chapter 7.2.2 --- Numerical examples on Markovian BSDEs --- p.114 / Chapter 8 --- Conclusions and future work --- p.123 / Chapter 8.1 --- Conclusions --- p.123 / Chapter 8.2 --- Future work --- p.124 / Bibliography --- p.126

Este texto apresenta alguns dos elementos básicos envolvidos em um estudo introdutório das equações diferencias estocásticas. Tais equações modelam problemas a tempo contínuo em que as grandezas de interesse estão sujeitas a certos tipos de perturbações aleatórias. Em nosso estudo, a aleatoriedade nessas equações será representada por um termo que envolve o processo estocástico conhecido como Movimento Browniano. Para um tratamento matematicamente rigoroso dessas equações, faremos uso da Integral Estocástica de Itô. A construção dessa integral é um dos principais objetivos do texto. Depois de desenvolver os conceitos necessários, apresentaremos alguns exemplos e provaremos existência e unicidade de solução para equações diferenciais estocásticas satisfazendo certas hipóteses. / This text presents some of the basic elements involved in an introductory study of stochastic differential equations. Such equations describe certain kinds of random perturbations on continuous time models. In our study, the randomness in these equations will be represented by a term involving the stochastic process known as Brownian Motion. For a mathematically rigorous treatment of these equations, we use the Itô Stochastic Integral. The construction of this integral is one of the main goals of the text. After developing the necessary concepts, we present some examples and prove existence and uniqueness of solution of stochastic differential equations satisfying some hypothesis.

In the presented work we study the existence of periodic solution to infinite dimensional stochastic equation with periodic coefficients driven by Cylindrical Wiener process. Used theory of infinite dimensional stochastic equations in Hilbert spaces and Markov processes is summarized in the first two chapters. In the third and last chapter we present the result itself. Necessary technical background mostly from operator theory is encapsulated in the Appendix. The proof of existence of periodic solution of corresponding equation is a combination of arguments by Khasminskii, which ensure under suitable conditions the existence of periodic Markov process, and the results of Da Prato, G¸atatrek and Zabczyk for the existence of invariant measure for homogeneous stochastic equation in Hilbert spaces. At the end we derive sufficient condition for the existence of periodic solution in the language of coefficients using the work of Ichikawa and illustrate the results by the example of Stochastic PDE. The work is written in English.

We present a new pathwise approximation method for stochastic differential equations driven by Brownian motion which does not require simulation of the stochastic integrals. The method is developed to give Wasserstein bounds O(h3/2) and O(h2) which are better than the Euler and Milstein strong error rates O(√h) and O(h) respectively, where h is the step-size. It assumes nondegeneracy of the diffusion matrix. We have used the Taylor expansion but generate an approximation to the expansion as a whole rather than generating individual terms. We replace the iterated stochastic integrals in the method by random variables with the same moments conditional on the linear term. We use a version of perturbation method and a technique from optimal transport theory to find a coupling which gives a good approximation in Lp sense. This new method is a Runge-Kutta method or so-called derivative-free method. We have implemented this new method in MATLAB. The performance of the method has been studied for degenerate matrices. We have given the details of proof for order h3/2 and the outline of the proof for order h2.

Chan, Tsz Him. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2010. / Includes bibliographical references (p. 94-96). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iii / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Monte Carlo method in Finance --- p.6 / Chapter 2.1 --- Principle of MC and pricing theory --- p.6 / Chapter 2.2 --- An illustrative example --- p.9 / Chapter 3 --- Discretization method --- p.15 / Chapter 3.1 --- The Euler scheme and Milstein scheme --- p.16 / Chapter 3.2 --- Convergence of Mean Square Error --- p.19 / Chapter 4 --- Quasi Monte Carlo method --- p.22 / Chapter 4.1 --- Basic idea of QMC --- p.23 / Chapter 4.2 --- Application of QMC in Finance --- p.29 / Chapter 4.3 --- Another illustrative example --- p.34 / Chapter 5 --- Our Methodology --- p.42 / Chapter 5.1 --- Measure decomposition --- p.43 / Chapter 5.2 --- QMC in SDE simulation --- p.51 / Chapter 5.3 --- Towards a workable algorithm --- p.58 / Chapter 6 --- Numerical Result --- p.69 / Chapter 6.1 --- Case I Generalized Wiener Process --- p.69 / Chapter 6.2 --- Case II Geometric Brownian Motion --- p.76 / Chapter 6.3 --- Case III Ornstein-Uhlenbeck Process --- p.83 / Chapter 7 --- Conclusion --- p.91 / Bibliography --- p.96

Backward stochastic differential equations appear in many areas of research including mathematical finance, nonlinear partial differential equations, financial economics and stochastic control. The first existence and uniqueness result for nonlinear backward stochastic differential equations was given by Pardoux and Peng (Adapted solution of a backward stochastic differential equation. System and Control Letters, 1990). They looked for an adapted pair of processes {x(t) / y(t)} / t is in [0 / 1]} with values in Rd and Rd&times / k respectively, which solves an equation of the form: x(t) + int_t^1 f(s,x(s),y(s))ds + int_t^1 [g(s,x(s)) + y(s)]dWs = X. This dissertation studies this paper in detail and provides all the steps of the proofs that appear in this seminal paper. In addition, we review (Cvitanic and Karatzas, Hedging contingent claims with constrained portfolios. The annals of applied probability, 1993). In this paper, Cvitanic and Karatzas studied the following problem: the hedging of contingent claims with portfolios constrained to take values in a given closed, convex set K. Processes intimately linked to BSDEs naturally appear in the formulation of the constrained hedging problem. The analysis of Cvitanic and Karatzas is based on a dual control problem. One of the contributions of this thesis is an algorithm that numerically solves this control problem in the case of constant volatility. The algorithm is based on discretization of time. The convergence proof is also provided.