These lecture notes are intended as a short introduction to diffusion processes on a domain with a reflecting boundary for graduate students, researchers in stochastic analysis and interested readers. Specific results on stochastic differential equations with reflecting boundaries such as existence and uniqueness, continuity and Markov properties, relation to partial differential equations and submartingale problems are given. An extensive list of references to current literature is included. This book has its origins in a mini-course the author gave at the University of Potsdam and at the Technical University of Berlin in Winter 2013.

This thesis studies the role of higher moments, that is moments behind mean and variance, in continuous-time, or diffusion, processes, which are commonly used to model so-called high-frequency data. Thereby, the first part is devoted to the derivation of closed-form expression of general (un)conditional (co)moment formulas of the famous CIR process’s solution. A byproduct of this derivation will be a novel way of proving that the process’s transition density is a noncentral chi-square distribution and that its steady-state law is a Gamma distribution. In the second part, we use these moment formulas to derive a near-exact simulation algorithm to the Heston model, in the sense that our algorithm generates pseudo-random numbers that have the same first four moments as the theoretical diffusion process. We will conduct several in-depth Monte Carlo studies to determine which existing simulation algorithm performs best with respect to these higher moments under certain circumstances. We will conduct the same study for the CIR process, which serves as a diffusion for the latent spot variance in the Heston model. The third part discusses several estimation approaches to the Heston model based on high-frequency data, such as MM, GMM, and (pseudo/quasi) ML. For the GMM approach, we will use the moments derived in the first part as moment conditions. We apply the best methodology to actual high-frequency price series of cryptocurrencies and FIAT stocks to provide benchmark parameter estimates. / Die vorliegende Arbeit untersucht die Rolle von höheren Momenten, also Momente, welche über den Erwartungswert und die Varianz hinausgehen, im Kontext von zeitstetigen Zeitreihenmodellen. Solche Diffusionsprozesse werden häufig genutzt, um sogenannte Hochfrequenzdaten zu beschreiben. Teil 1 der Arbeit beschäftigt sich mit der Herleitung von allgemeinen und in geschlossener Form verfügbaren Ausdrücken der (un)bedingten (Ko-)Momente der Lösung zum CIR-Prozess. Mittels dieser Formeln wird auf einem alternativen Weg bewiesen, dass die Übergangsdichte dieses Prozesses mithilfe einer nichtzentralen Chi-Quadrat-Verteilung beschrieben werden kann, und dass seine stationäre Verteilung einer Gamma-Verteilung entspricht. Im zweiten Teil werden die zuvor entwickelten Ausdrücke genutzt, um einen nahezu exakten Simulationsalgorithmus für das Hestonmodell herzuleiten. Dieser Algorithmus ist in dem Sinne nahezu exakt, dass er Pseudo-Zufallszahlen generiert, welche die gleichen ersten vier Momente besitzen, wie der dem Hestonmodell zugrundeliegende Diffusionsprozess. Ferner werden Monte-Carlo-Studien durchgeführt, die ergründen sollen, welche bereits existierenden Simulationsalgorithmen in Hinblick auf die ersten vier Momente die besten Ergebnisse liefern. Die gleiche Studie wird außerdem für die Simulation des CIR-Prozesses durchgeführt, welcher im Hestonmodell als Diffusion für die latente, instantane Varianz dient. Im dritten Teil werden mehrere Schätzverfahren für das Hestonmodell, wie das MM-, GMM und pseudo- beziehungsweise quasi-ML-Verfahren, diskutiert. Diese werden unter Benutzung von Hochfrequenzdaten studiert. Für das GMM-Verfahren dienen die hergeleiteten Momente aus dem ersten Teil der Arbeit als Momentebedingungen. Um ferner Schätzwerte für das Hestonmodell zu finden, werden die besten Verfahren auf Hochfrequenzmarktdaten von Kryptowährungen, sowie hochliquider Aktientitel angewandt. Diese sollen zukünftig als Orientierungswerte dienen.

Wir betrachten die stochastische Differentialgleichung mit Gedächtnis (SDDE) mit Gedächtnislänge r dX(t) = b(X(u);u in [t-r,t])dt + sigma(X(u);u in [t-r,t])dB(t) mit eindeutiger schwacher Lösung. Dabei ist B eine Brownsche Bewegung, b and sigma sind stetige, lokal beschränkte Funktionen mit Definitionsbereich C[-r,0], und X(u);u in [t-r,t] bezeichnet das Segment der Werte von X(u) für Zeitpunkte u im Intervall [t,t-r]. Unser Ziel ist eine Folge von diskreten Zeitreihen Xh höherer Ordung zu konstruieren, so dass mit h gegen 0 die Zeitreihen Xh schwach gegen die Lösung X der stochastischen Differentialgleichung mit Gedächtnis konvergieren. Desweiteren werden wir Bedingungen angeben, unter denen eine gegeben Folge von Zeitreihen Xh höherer Ordung schwach gegen die Lösung X einer stochastischen Differentialgleichung mit Gedächtnis konvergiert. Als ein Beispiel werden wir den schwachen Grenzwert einer Folge von diskreten GARCH-Prozessen höherer Ordnung ermitteln. Dieser Grenzwert wird sich als schwache Lösung einer stochastischen Differentialgleichung mit Gedächtnis herausstellen. / Consider the stochastic delay differential equation (SDDE) with length of memory r dX(t) = b(X(u);u in [t-r,t])dt + sigma(X(u);u in [t-r,t])dB(t), which has a unique weak solution. Here B is a Brownian motion, b and sigma are continuous, locally bounded functions defined on the space C[-r,0], and X(u);u in [t-r,t] denotes the segment of the values of X(u) for time points u in the interval [t,t-r]. Our aim is to construct a sequence of discrete time series Xh of higher order, such that Xh converges weakly to the solution X of the stochastic differential delay equation as h tends to zero. On the other hand we shall establish under which conditions time series Xh of higher order converge weakly to a weak solution X of a stochastic differential delay equation. As an illustration we shall derive a weak limit of a sequence of GARCH processes of higher order. This limit tends out to be the weak solution of a stochastic differential delay equation.

Sei (X(t), t>= -r) ein stationärer stochastischer Prozess, der die affine stochastische Differentialgleichung mit Gedächtnis dX(t)=L(X(t+s))dt+sigma dW(t), t>= 0, löst, wobei sigma>0, (W(t), t>=0) eine Standard-Brownsche Bewegung und L ein stetiges lineares Funktional auf dem Raum der stetigen Funktionen auf [-r,0], dargestellt durch ein endliches signiertes Maß a, bezeichnet. Wir nehmen an, dass eine Trajektorie (X(t), -r 0, konvergiert. Diese Rate ist schlechter als in vielen klassischen Fällen. Wir beweisen jedoch eine untere Schranke, die zeigt, dass keine Schätzung eine bessere Rate im Minimax-Sinn aufweisen kann. Für zeit-diskrete Beobachtungen von maximalem Abstand Delta konvergiert die Galerkin-Schätzung immer noch mit obiger Rate, sofern Delta is in etwa von der Ordnung T^(-1/2). Hingegen wird bewiesen, dass für festes Delta unabhängig von T die Rate sich signifikant verschlechtern muss, indem eine untere Schranke von T^(-s/(2s+6)) gezeigt wird. Außerdem wird eine adaptive Schätzung basierend auf Wavelet-Thresholding-Techniken für das assoziierte schlechtgestellte Problem konstruiert. Diese nichtlineare Schätzung erreicht die obige Minimax-Rate sogar für die allgemeinere Klasse der Besovräume B^s_(p,infinity) mit p>max(6/(2s+3),1). Die Restriktion p>=max(6/(2s+3),1) muss für jede Schätzung gelten und ist damit inhärent mit dem Schätzproblem verknüpft. Schließlich wird ein Hypothesentest mit nichtparametrischer Alternative vorgestellt, der zum Beispiel für das Testen auf Gedächtnis verwendet werden kann. Dieser Test ist anwendbar für eine L^2-Trennungsrate zwischen Hypothese und Alternative der Ordnung T^(-s/(2s+2.5)). Diese Rate ist wiederum beweisbar optimal für jede mögliche Teststatistik. Für die Beweise müssen die Parameterabhängigkeit der stationären Lösungen sowie die Abbildungseigenschaften der assoziierten Kovarianzoperatoren detailliert bestimmt werden. Weitere Resultate von allgemeinem Interessen beziehen sich auf die Mischungseigenschaft der stationären Lösung, eine Fallstudie zu exponentiellen Gewichtsfunktionen sowie der Approximation des stationären Prozesses durch autoregressive Prozesse in diskreter Zeit. / Let (X(t), t>= -r) be a stationary stochastic process solving the affine stochastic delay differential equation dX(t)=L(X(t+s))dt+sigma dW(t), t>= 0, with sigma>0, (W(t), t>=0) a standard one-dimensional Brownian motion and with a continuous linear functional L on the space of continuous functions on [-r,0], represented by a finite signed measure a. Assume that a trajectory (X(t), -r 0. This rate is worse than those obtained in many classical cases. However, we prove a lower bound, stating that no estimator can attain a better rate of convergence in a minimax sense. For discrete time observations of maximal distance Delta, the Galerkin estimator still attains the above asymptotic rate if Delta is roughly of order T^(-1/2). In contrast, we prove that for observation intervals Delta, with Delta independent of T, the rate must deteriorate significantly by providing the rate estimate T^(-s/(2s+6)) from below. Furthermore, we construct an adaptive estimator by applying wavelet thresholding techniques to the corresponding ill-posed inverse problem. This nonlinear estimator attains the above minimax rate even for more general classes of Besov spaces B^s_(p,infinity) with p>max(6/(2s+3),1). The restriction p >= 6/(2s+3) is shown to hold for any estimator, hence to be inherently associated with the estimation problem. Finally, a hypothesis test with a nonparametric alternative is constructed that could for instance serve to decide whether a trajectory has been generated by a stationary process with or without time delay. The test works for an L^2-separation rate between hypothesis and alternative of order T^(-s/(2s+2.5)). This rate is again shown to be optimal among all conceivable tests. For the proofs, the parameter dependence of the stationary solutions has to be studied in detail and the mapping properties of the associated covariance operators have to be determined exactly. Other results of general interest concern the mixing properties of the stationary solution, a case study for exponential weight functions and the approximation of the stationary process by discrete time autoregressive processes.

Für einen R^d-wertigen stochastischen Prozess X auf R bezeichne X_t den Segmentprozess X_t:={X(t+u): u = 0. Es wird folgende affine stochastische Differentialgleichung mit unendlichem Gedächtnis betrachtet: dX(t)=L(X_t)dt + dW(t) für t >= 0, X_0=F, (A) wobei L:B -> R^d ein lineares stetiges Funktional, W einen Wiener-Prozess mit Werten in R^d sowie B einen semi-normierten linearen Unterraum von {f:(-00, 0] -> R^d} bezeichnen. Die Anfangsbedingung F ist eine B-wertige Zufallsvariable. Die Lösung X der Gleichung (A) lässt sich mittels einer Formel der Variation der Konstanten darstellen. Für die Existenz einer stationären Lösung werden hinreichende und notwendige Bedingungen vorgestellt. Für eine spezielle Klasse von Funktionalen L kann Gleichung (A) auf ein System gewöhnlicher stochastischer Gleichungen ohne Gedächtnis reduziert werden. Diese Reduktion wird im Detail untersucht, insbesondere gewinnt man hierdurch ein einfaches äquivalentes Kriterium für die Existenz stationärer Lösungen von Gleichungen mit Funktionalen L dieser Klasse. Durch Einbettung der Gleichung (A) in den Bidualraum B** gelingt die Bestimmung der Lyapunov-Exponenten der Lösung. Hierzu wird ein neuer Zusammenhang der Lösung der sogenannten adjungierten Gleichung von (A) und einer Spektralzerlegung des Raumes B benutzt. Die Untersuchung der stetigen Abhängigkeit der Lösung von dem Funktional L und der Anfangsbedingung F ermöglicht die Behandlung anwendungsorientierter Aspekte. In Verbindung mit den Ergebnissen über reduzierbare Gleichungen wird ein Verfahren zur Approximation der Lösung von Gleichung (A) durch Ornstein-Uhlenbeck-Prozesse vorgestellt. Eine allgemeine Klasse von Ito-Differentialgleichungen mit nichtlinearen vergangenheitsabhängigen Drift- und Dispersionskoeffizienten wird eingeführt, in der die Gleichung (A) als eine spezielle affine Gleichung verstanden werden kann. Für diese allgemeinen Gleichungen wird ein Existenz- und Eindeutigkeitssatz nachgewiesen. / For an R^d-valued stochastic process X denote the segment process by X_t:={X(t+u): u = 0. We consider the following affine stochastic differential equation with infinite delay: dX(t)=L(X_t)dt + dW(t) for t >= 0, X_0= F, (A) where L:B -> R^d denotes a linear continuous functional, W denotes a Wiener process with values in R^d and B is a semi-normed linear subspace of {f: (-00, 0] -> R^d}. The initial condition F is a B-valued random variable. The solution X of equation (A) can be represented by a variation of constants formula. We provide sufficient and necessary conditions for the existence of a stationary solution. For a special class of functionals L the equation (A) can be reduced to a system of ordinary stochastic differential equations without memory. This reduction is studied in detail. In particular, we deduce a simple equivalent condition for the existence of stationary solutions of equations with functionals L in this class. The embedding of equation (A) into the bidualspace B** enables us to calculate the Lyapunov exponents of the solution. For this purpose we exploit a new connection between the solution of the so-called adjoint equation of (A) and a spectral decompositon of the space B. By considering the continuous dependence of the solution on the functional L and the initial condition F we obtain results useful in applications. In conjunction with results on reducible equations we establish an approximation scheme for the solution of equation (A) by Ornstein-Uhlenbeck processes. Moreover, we introduce a general class of Ito differential equations with non-linear drift and dispersion hereditary coefficients. We deduce a result on the existence of unique solutions for this general class of equations. Equation (A) can be regarded as a special affine equation in this class.

Cell migration, the active translocation of cells is involved in various biological processes, e.g. development of tissues and organs, tumor invasion and wound healing. Cell migration behavior can be divided into two distinct classes: single cell migration and collective cell migration. Single cell migration describes the migration of cells without interaction with other cells in their environment. Collective cell migration is the joint, active movement of multiple cells, e.g. in the form of strands, cohorts or sheets which emerge as the result of individual cell-cell interactions. Collective cell migration can be observed during branching morphogenesis, vascular sprouting and embryogenesis. Experimental studies of single cell migration have been extensive. Collective cell migration is less well investigated due to more difficult experimental conditions than for single cell migration. Especially, experimentally identifying the impact of individual differences in cell phenotypes on individual cell migration behavior inside cell populations is challenging because the tracking of individual cell trajectories is required. In this thesis, a novel mathematical modeling approach, individual-based lattice-gas cellular automata (IB-LGCA), that allows to investigate the migratory behavior of individual cells inside migrating cell populations by enabling the tracking of individual cells is introduced. Additionally, stochastic differential equation (SDE) approximations of individual cell trajectories for IB-LGCA models are constructed. Such SDE approximations allow the analytical description of the trajectories of individual cells during single cell migration. For a complete analytical description of the trajectories of individual cell during collective cell migration the aforementioned SDE approximations alone are not sufficient. Analytical approximations of the time development of selected observables for the cell population have to be added. What observables have to be considered depends on the specific cell migration mechanisms that is to be modeled. Here, partial integro-differential equations (PIDE) that approximate the time evolution of the expected cell density distribution in IB-LGCA are constructed and coupled to SDE approximations of individual cell trajectories. Such coupled PIDE and SDE approximations provide an analytical description of the trajectories of individual cells in IB-LGCA with density-dependent cell-cell interactions. Finally, an IB-LGCA model and corresponding analytical approximations were applied to investigate the impact of changes in cell-cell and cell-ECM forces on the migration behavior of an individual, labeled cell inside a population of epithelial cells. Specifically, individual cell migration during the epithelial-mesenchymal transition (EMT) was considered. EMT is a change from epithelial to mesenchymal cell phenotype which is characterized by cells breaking adhesive bonds with surrounding epithelial cells and initiating individual migration along the extracellular matrix (ECM). During the EMT, a transition from collective to single cell migration occurs. EMT plays an important role during cancer progression, where it is believed to be linked to metastasis development. In the IB-LGCA model epithelial cells are characterized by balanced cell-cell and cell-ECM forces. The IB-LGCA model predicts that the balance between cell-cell and cell-ECM forces can be disturbed to some degree without being accompanied by a change in individual cell migration behavior. Only after the cell force balance has been strongly interrupted mesenchymal migration behavior is possible. The force threshold which separates epithelial and mesenchymal migration behavior in the IB-LGCA has been identified from the corresponding analytical approximation. The IB-LGCA model allows to obtain quantitative predictions about the role of cell forces during EMT which in the context of mathematical modeling of EMT is a novel approach.

Cell migration, the active translocation of cells is involved in various biological processes, e.g. development of tissues and organs, tumor invasion and wound healing. Cell migration behavior can be divided into two distinct classes: single cell migration and collective cell migration. Single cell migration describes the migration of cells without interaction with other cells in their environment. Collective cell migration is the joint, active movement of multiple cells, e.g. in the form of strands, cohorts or sheets which emerge as the result of individual cell-cell interactions. Collective cell migration can be observed during branching morphogenesis, vascular sprouting and embryogenesis. Experimental studies of single cell migration have been extensive. Collective cell migration is less well investigated due to more difficult experimental conditions than for single cell migration. Especially, experimentally identifying the impact of individual differences in cell phenotypes on individual cell migration behavior inside cell populations is challenging because the tracking of individual cell trajectories is required. In this thesis, a novel mathematical modeling approach, individual-based lattice-gas cellular automata (IB-LGCA), that allows to investigate the migratory behavior of individual cells inside migrating cell populations by enabling the tracking of individual cells is introduced. Additionally, stochastic differential equation (SDE) approximations of individual cell trajectories for IB-LGCA models are constructed. Such SDE approximations allow the analytical description of the trajectories of individual cells during single cell migration. For a complete analytical description of the trajectories of individual cell during collective cell migration the aforementioned SDE approximations alone are not sufficient. Analytical approximations of the time development of selected observables for the cell population have to be added. What observables have to be considered depends on the specific cell migration mechanisms that is to be modeled. Here, partial integro-differential equations (PIDE) that approximate the time evolution of the expected cell density distribution in IB-LGCA are constructed and coupled to SDE approximations of individual cell trajectories. Such coupled PIDE and SDE approximations provide an analytical description of the trajectories of individual cells in IB-LGCA with density-dependent cell-cell interactions. Finally, an IB-LGCA model and corresponding analytical approximations were applied to investigate the impact of changes in cell-cell and cell-ECM forces on the migration behavior of an individual, labeled cell inside a population of epithelial cells. Specifically, individual cell migration during the epithelial-mesenchymal transition (EMT) was considered. EMT is a change from epithelial to mesenchymal cell phenotype which is characterized by cells breaking adhesive bonds with surrounding epithelial cells and initiating individual migration along the extracellular matrix (ECM). During the EMT, a transition from collective to single cell migration occurs. EMT plays an important role during cancer progression, where it is believed to be linked to metastasis development. In the IB-LGCA model epithelial cells are characterized by balanced cell-cell and cell-ECM forces. The IB-LGCA model predicts that the balance between cell-cell and cell-ECM forces can be disturbed to some degree without being accompanied by a change in individual cell migration behavior. Only after the cell force balance has been strongly interrupted mesenchymal migration behavior is possible. The force threshold which separates epithelial and mesenchymal migration behavior in the IB-LGCA has been identified from the corresponding analytical approximation. The IB-LGCA model allows to obtain quantitative predictions about the role of cell forces during EMT which in the context of mathematical modeling of EMT is a novel approach.

In this thesis, we present a new existence result for Lévy-type processes. Lévy-type processes behave locally like a Lévy process, but the Lévy triplet may depend on the current position of the process. They can be characterized by their so-called symbol; this is the analogue of the characteristic exponent in the Lévy case. Using a parametrix construction, we prove the existence of Lévy-type processes with a given symbol under weak regularity assumptions on the regularity of the symbol. Applications range from existence results for stable-like processes and mixed processes to uniqueness results for Lévy-driven stochastic differential equations. Moreover, we discuss sufficient conditions for the existence of moments of Lévy-type processes and derive estimates for fractional moments.

Das Ziel dieser Arbeit ist die Entwicklung eines effizienten parametrischen Schätzverfahrens für den Drift einer durch einen Lévy-Prozess getriebenen Sprungdiffusion. Zunächst werden zeit-stetige Beobachtungen angenommen und auf dieser Basis eine Likelihoodtheorie entwickelt. Dieser Schritt umfasst die Frage nach lokaler Äquivalenz der zu verschiedenen Parametern auf dem Pfadraum induzierten Maße. Wir diskutieren in dieser Arbeit Schätzer für Prozesse vom Ornstein-Uhlenbeck-Typ, Cox-Ingersoll-Ross Prozesse und Lösungen linearer stochastischer Differentialgleichungen mit Gedächtnis im Detail und zeigen starke Konsistenz, asymptotische Normalität und Effizienz im Sinne von Hájek und Le Cam für den Likelihood-Schätzer. In Sprungdiffusionsmodellen ist die Likelihood-Funktion eine Funktion des stetigen Martingalanteils des beobachteten Prozesses, der im Allgemeinen nicht direkt beobachtet werden kann. Wenn nun nur Beobachtungen an endlich vielen Zeitpunkten gegeben sind, so lässt sich der stetige Anteil der Sprungdiffusion nur approximativ bestimmen. Diese Approximation des stetigen Anteils ist ein zentrales Thema dieser Arbeit und es wird uns auf das Filtern von Sprüngen führen. Der zweite Teil dieser Arbeit untersucht die Schätzung der Drifts, wenn nur diskrete Beobachtungen gegeben sind. Dabei benutzen wir die Likelihood-Schätzer aus dem ersten Teil und approximieren den stetigen Martingalanteil durch einen sogenannten Sprungfilter. Wir untersuchen zuerst den Fall endlicher Aktivität und zeigen, dass die Driftschätzer im Hochfrequenzlimes die effiziente asymptotische Verteilung erreichen. Darauf aufbauend beweisen wir dann im Falle unendlicher Sprungaktivität asymptotische Effizienz für den Driftschätzer im Ornstein-Uhlenbeck Modell. Im letzten Teil werden die theoretischen Ergebnisse für die Schätzer auf endlichen Stichproben aus simulierten Daten geprüft und es zeigt sich, dass das Sprungfiltern zu einem deutlichen Effizienzgewinn führen. / The problem of parametric drift estimation for a a Lévy-driven jump diffusion process is considered in two different settings: time-continuous and high-frequency observations. The goal is to develop explicit maximum likelihood estimators for both observation schemes that are efficient in the Hájek-Le Cam sense. The likelihood function based on time-continuous observations can be derived explicitly for jump diffusion models and leads to explicit maximum likelihood estimators for several popular model classes. We consider Ornstein-Uhlenbeck type, square-root and linear stochastic delay differential equations driven by Lévy processes in detail and prove strong consistency, asymptotic normality and efficiency of the likelihood estimators in these models. The appearance of the continuous martingale part of the observed process under the dominating measure in the likelihood function leads to a jump filtering problem in this context, since the continuous part is usually not directly observable and can only be approximated and the high-frequency limit. In the second part of this thesis the problem of drift estimation for discretely observed processes is considered. The estimators are constructed from discretizations of the time-continuous maximum likelihood estimators from the first part, where the continuous martingale part is approximated via a thresholding technique. We are able to proof that even in the case of infinite activity jumps of the driving Lévy process the estimator is asymptotically normal and efficient under weak assumptions on the jump behavior. Finally, the finite sample behavior of the estimators is investigated on simulated data. We find that the maximum likelihood approach clearly outperforms the least squares estimator when jumps are present and that the efficiency gap between both techniques becomes even more severe with growing jump intensity.

In this thesis, we present a new existence result for Lévy-type processes. Lévy-type processes behave locally like a Lévy process, but the Lévy triplet may depend on the current position of the process. They can be characterized by their so-called symbol; this is the analogue of the characteristic exponent in the Lévy case. Using a parametrix construction, we prove the existence of Lévy-type processes with a given symbol under weak regularity assumptions on the regularity of the symbol. Applications range from existence results for stable-like processes and mixed processes to uniqueness results for Lévy-driven stochastic differential equations. Moreover, we discuss sufficient conditions for the existence of moments of Lévy-type processes and derive estimates for fractional moments.