This thesis deals with credit risk modeling and related mathematical issues. In particular we study first-passage models for credit risk, where obligors default upon first passage of a ``credit quality" process to zero. The first passage problem for correlated Brownian motion is a mathematical structure which arises quite naturally in such models, in particular the seminal multivariate Black-Cox model. In general this problem is analytically intractable, however in two dimensions analytic results are available. In addition to correcting mistakes in several published formulae, we derive an exact simulation scheme for sampling the passage times. Our algorithm exploits several interesting properties of planar Brownian motion and conformal local martingales. The main contribution of this thesis is the development of a novel multivariate framework for credit risk. We allow for both stochastic trend and volatility in credit qualities, with dependence introduced by letting these quantities be driven by systematic factors common to all obligors. Exploiting a conditional independence structure we are able to express the proportion of defaults in an asymptotically large portfolio as a path functional of the systematic factors. The functional in question returns crossing probabilities of time-changed Brownian motion to continuous barriers, and is typically not available in closed form. As such the distribution of portfolio losses is in general analytically intractable. As such we devise a scheme for simulating approximate losses and demonstrate almost sure convergence of this approximation. We show that the model calibrates well, across both tranches and maturities, to market quotes for CDX index tranches. In particular we are able to calibrate to data from 2006, as well as more recent ``distressed" data from 2008.

This thesis deals with credit risk modeling and related mathematical issues. In particular we study first-passage models for credit risk, where obligors default upon first passage of a ``credit quality" process to zero. The first passage problem for correlated Brownian motion is a mathematical structure which arises quite naturally in such models, in particular the seminal multivariate Black-Cox model. In general this problem is analytically intractable, however in two dimensions analytic results are available. In addition to correcting mistakes in several published formulae, we derive an exact simulation scheme for sampling the passage times. Our algorithm exploits several interesting properties of planar Brownian motion and conformal local martingales. The main contribution of this thesis is the development of a novel multivariate framework for credit risk. We allow for both stochastic trend and volatility in credit qualities, with dependence introduced by letting these quantities be driven by systematic factors common to all obligors. Exploiting a conditional independence structure we are able to express the proportion of defaults in an asymptotically large portfolio as a path functional of the systematic factors. The functional in question returns crossing probabilities of time-changed Brownian motion to continuous barriers, and is typically not available in closed form. As such the distribution of portfolio losses is in general analytically intractable. As such we devise a scheme for simulating approximate losses and demonstrate almost sure convergence of this approximation. We show that the model calibrates well, across both tranches and maturities, to market quotes for CDX index tranches. In particular we are able to calibrate to data from 2006, as well as more recent ``distressed" data from 2008.

In this thesis we develop and use a continuum random walk framework to solve problems that are usually studied using a discrete random walk on a discrete lattice. Problems studied include; the time it takes for a random walker to be absorbed at a trap on a fractal lattice, the calculation of the spectral dimension for several different classes of networks, the calculation of the density of states for a multi-layered Bethe lattice and the relationship between diffusion exponents and a resistivity exponent that occur in relevant power laws. The majority of the results are obtained by deriving an expression for a Laplace transformed Green’s function or first passage time, and then using Tauberian theorems to find the relevant asymptotic behaviour. The continuum framework is established by studying the diffusion equation on a 1-d bar with non-homogeneous boundary conditions. The result is extended to model diffusion on networks through linear algebra. We derive the transformation linking the Green’s functions and first passage time results in the continuum and discrete settings. The continuum method is used in conjunction with renormalization techniques to calculate the time taken for a random walker to be absorbed at a trap on a fractal lattice and also to find the spectral dimension of new classes of networks. Although these networks can be embedded in the d- dimensional Euclidean plane, they do not have a spectral dimension equal to twice the ratio of the fractal dimension and the random walk dimension when the random walk on the network is transient. The networks therefore violate the Alexander-Orbach law. The fractal Einstein relationship (a relationship relating a diffusion exponent to a resistivity exponent) also does not hold on these networks. Through a suitable scaling argument, we derive a generalised fractal Einstein relationship which holds for our lattices and explains anomalous results concerning transport on diffusion limited aggregates and Eden trees.

In this thesis we develop and use a continuum random walk framework to solve problems that are usually studied using a discrete random walk on a discrete lattice. Problems studied include; the time it takes for a random walker to be absorbed at a trap on a fractal lattice, the calculation of the spectral dimension for several different classes of networks, the calculation of the density of states for a multi-layered Bethe lattice and the relationship between diffusion exponents and a resistivity exponent that occur in relevant power laws. The majority of the results are obtained by deriving an expression for a Laplace transformed Green’s function or first passage time, and then using Tauberian theorems to find the relevant asymptotic behaviour. The continuum framework is established by studying the diffusion equation on a 1-d bar with non-homogeneous boundary conditions. The result is extended to model diffusion on networks through linear algebra. We derive the transformation linking the Green’s functions and first passage time results in the continuum and discrete settings. The continuum method is used in conjunction with renormalization techniques to calculate the time taken for a random walker to be absorbed at a trap on a fractal lattice and also to find the spectral dimension of new classes of networks. Although these networks can be embedded in the d- dimensional Euclidean plane, they do not have a spectral dimension equal to twice the ratio of the fractal dimension and the random walk dimension when the random walk on the network is transient. The networks therefore violate the Alexander-Orbach law. The fractal Einstein relationship (a relationship relating a diffusion exponent to a resistivity exponent) also does not hold on these networks. Through a suitable scaling argument, we derive a generalised fractal Einstein relationship which holds for our lattices and explains anomalous results concerning transport on diffusion limited aggregates and Eden trees.

This thesis solves estimation and control problems in computational neuroscience, mathematically dealing with the first-passage times of diffusion stochastic processes. We first derive estimation algorithms for model parameters from first-passage time observations, and then we derive algorithms for the control of first-passage times. Finally, we solve an optimal design problem which combines elements of the first two: we ask how to elicit first-passage times such as to facilitate model estimation based on said first-passage observations. The main mathematical tools used are the Fokker-Planck partial differential equation for evolution of probability densities, the Hamilton-Jacobi-Bellman equation of optimal control and the adjoint optimization principle from optimal control theory. The focus is on developing computational schemes for the solution of the problems. The schemes are implemented and are tested for a wide range of parameters.

In dieser Arbeit werden zwei intrazelluläre Signalwege, betreffend den Tumorsuppressor p53 und das Signalmolekül Ca2+ , diskutiert und modelliert. Einzelzellmessungen des Tumorsuppressors p53 zeigen pulsatile Antwor- ten nach Zufügung von DNA Doppelstrangbrüchen (DSBs). Außer für sehr hohe Schadensdosen, ist das zeitliche auftreten dieser Pulse unregelmäßig. Mithilfe eines Wavelet basierten Pulsdetektors werden die einzelzell Trajek- torien untersucht und die inter-Puls Intervall (IPI) Verteilungen extrahiert. Diese weisen auf nicht-oszillatorische Regime in den Daten hin. Die Theorie der anregbaren Systeme angewendet auf regulatorische Netzwerke ermöglicht dieses komplexe Verhalten mathematisch zu beschreiben. Die Kopplung von Schadens-Sensor-Kinase Dynamik mit dem kanonischen p53 negativen feedback loop, ergibt ein anregbares p53 Modell. Detaillier- te Bifurkationsanalysen zeigen ein robustes anregbares Regime, welches durch ein starkes Schadenssignal auch in Oszillationen überführt werden kann. Treibt man das p53 Modell mit einem stochastischen DNA-Schadens-Prozess, kann sowohl das oszillatorische Verhalten nach hohem Schaden, als auch das unregelmäßige pulsatile Verhalten ohne äußere Stimulation reproduziert werden. Intrazelluläre Ca 2+ Spikes entstehen durch eine hierarchische Kaskade stochastischer prozesse. Die Anwendung einer semi-markovschen Beschreibung führt zu praktischen analytischen Lösungen des erstpassagezeiten Problems. Eine hierbei entdeckte Zeitskalenseparation ermöglicht ein neues allgemeines Ca2+ -Modell. Dieses erklärt auf äußerst prägnante Weise viele wesentliche experimentelle Ergebnisse, insbesondere die Momentenbeziehungen der inter-Spike Intervall Verteilungen. Schließlich erlaubt die hier vorgestellte Theorie Berechnungen der Stimulus-Enkodierung, also die Adaption des Ca 2+ Signals auf veränderliche extrazelluläre Stimuli. Die Vorhersage einer fold change Enkodierung kann durch Experimente gestützt werden. / In this work two signaling pathways, involving the tumor suppressor p53 and the second messenger Ca2+ , are to be discussed and modelled. The tumor suppressor p53 shows a pulsatile response in single cells after induction of DNA double strand breaks (DSBs). Except for very high amounts of damage, these pulses appear at irregular times. The concept of excitable systems is employed as a convenient way to model such observed dynamics. An application to biomolecular reaction networks shows the need for a positive feedback within the p53 regulatory network. Exploiting the reported ultrasensitive dynamics of the upstream damage sensor kinases, leads to a simplified excitable kinase-phosphatase model. Coupling that to the canonical negative feedback p53 regulatory loop, is the core idea behind the construction of the excitable p53 model. A detailed bifurcation analysis of the model establishes a robust excitable regime, which can be switched to oscillatory dynamics via a strong DNA damage signal. Driving the p53 model with a stochastic DSB process yields pulsatile dynamics which reflect different experimental scenarios. Intracellular Ca 2+ concentration spikes arise from a hierarchic cascade of stochastic events. An analytical solution strategy, employing a semi-Markovian description and involving Laplace transformations, is devised and successfully applied to a specific Ca2+ model. The new gained insights are then used, to construct a new generic Ca2+ model, which elegantly captures many known features of Ca2+ signaling. In particular the experimentally observed relations between the average and the standard deviation of the inter spike intervals (ISIs) can be explained in a concise way. Finally, the theoretical considerations allow to calculate the stimulus encoding relation, which governs the adaption of the Ca 2+ signals to varying extracellular stimuli. This is predicted to be a fold change response and new experimental results display a strong support of this idea.

In this thesis the interplay between hydrodynamic transport and specific adhesion is theoretically investigated. An important biological motivation for this work is the rolling adhesion of white blood cells experimentally investigated in flow chambers. There, specific adhesion is mediated by weak bonds between complementary molecular building blocks which are either located on the cell surface (receptors) or attached to the bottom plate of the flow chamber (ligands). The model system under consideration is a hard sphere covered with receptors moving above a planar ligand-bearing wall. The motion of the sphere is influenced by a simple shear flow, deterministic forces, and Brownian motion. An algorithm is given that allows to numerically simulate this motion as well as the formation and rupture of bonds between receptors and ligands. The presented algorithm spatially resolves receptors and ligands. This opens up the perspective to apply the results also to flow chamber experiments done with patterned substrates based on modern nanotechnological developments. In the first part the influence of flow rate, as well as of the number and geometry of receptors and ligands, on the probability for initial binding is studied. This is done by determining the mean time that elapses until the first encounter between a receptor and a ligand occurs. It turns out that besides the number of receptors, especially the height by which the receptors are elevated above the surface of the sphere plays an important role. These findings are in good agreement with observations of actual biological systems like white blood cells or malaria-infected red blood cells. Then, the influence of bonds which have formed between receptors and ligands, but easily rupture in response to force, on the motion of the sphere is studied. It is demonstrated that different states of motion-for example rolling-can be distinguished. The appearance of these states depending on important model parameters is then systematically investigated. Furthermore, it is shown by which bond property the ability of cells to stably roll in a large range of applied flow rates is increased. Finally, the model is applied to another biological process, the transport of spherical cargo particles by molecular motors. In analogy to the so far described systems molecular motors can be considered as bonds that are able to actively move. In this part of the thesis the mean distance the cargo particles are transported is determined. / In der vorliegenden Arbeit wird das Zusammenspiel zwischen hydrodynamischem Transport und spezifischer Adhäsion theoretisch untersucht. Eine wichtige biologische Motivation für diese Arbeit ist die rollende Adhäsion weißer Blutkörperchen, die experimentell in Flusskammern untersucht wird. Die spezifische Adhäsion wird durch schwache Bindungen zwischen komplementären molekularen Bausteinen vermittelt, die sich einerseits auf der Zelloberfläche, Rezeptoren genannt, andererseits auf der unteren begrenzenden Platte der Flusskammer, Liganden genannt, befinden. Das untersuchte Modellsystem besteht aus einer festen Kugel, die mit Rezeptoren bedeckt ist und sich unter dem Einfluss einer einfachen Scherströmung, deterministischer Kräfte und der Brownschen Molekularbewegung oberhalb einer mit Liganden bedeckten Wand bewegt. Es wird ein Algorithmus angegeben, mit dessen Hilfe diese Bewegung sowie das Entstehen und Reißen von Bindungen zwischen Rezeptoren und Liganden numerisch simuliert werden kann. In der numerischen Modellierung werden die Positionen von Rezeptoren und Liganden räumlich aufgelöst, wodurch sich die Möglichkeit ergibt, die Ergebnisse auch mit Flusskammerexperimenten, in denen moderne nanotechnologisch strukturierte Substrate verwendet werden, zu vergleichen. Als Erstes wird der Einfluss von Strömungsrate sowie Zahl und Form der Rezeptoren bzw. Liganden auf die Wahrscheinlichkeit, mit der es zu einer Bindung kommen kann, untersucht. Hierfür wird die mittlere Zeit bestimmt, die vergeht bis zum ersten Mal ein Rezeptor mit einem Liganden in Kontakt kommt. Dabei stellt sich heraus, dass neben der Anzahl der Rezeptoren auf der Kugel insbesondere der Abstand, welchen die Rezeptoren von der Oberfläche haben, eine große Rolle spielt. Dieses Ergebnis ist in sehr guter Übereinstimmung mit tatsächlichen biologischen Systemen wie etwa weißen Blutkörperchen oder mit Malaria infizierten roten Blutkörperchen. Als Nächstes wird betrachtet, welchen Einfluss Bindungen haben, die sich zwischen Rezeptoren und Liganden bilden, aber unter Kraft auch leicht wieder reißen. Dabei zeigt sich, dass verschiedene Bewegungstypen auftreten, beispielsweise Rollen, deren Erscheinen in Abhängigkeit wichtiger Modellparameter dann systematisch untersucht wird. Weiter wird der Frage nachgegangen, welche Eigenschaften von Bindungen dazu führen können, dass Zellen in einem großen Bereich von Strömungsraten ein stabiles Rollverhalten zeigen. Abschließend wird das Modell auf einen etwas anderen biologischen Prozess angewendet, nämlich den Transport kugelförmiger Lastpartikeln durch molekulare Motoren. In Analogie zu den bisher beschriebene Systemen können diese molekularen Motoren als sich aktiv bewegende Bindungen betrachtet werden. In diesem Teil der Arbeit wird ermittelt, wie weit die Lastpartikel im Mittel transportiert werden.

This dissertation is cumulative and encompasses three self-contained research articles. These essays share one common theme: the probability that a given stochastic process crosses a certain boundary function, namely the boundary crossing probability, and the related financial and statistical applications. In the first paper, we propose a new Monte Carlo method to price a type of barrier option called the Parisian option by simulating the first and last hitting time of the barrier. This research work aims at filling the gap in the literature on pricing of Parisian options with general curved boundaries while providing accurate results compared to the other Monte Carlo techniques available in the literature. Some numerical examples are presented for illustration. The second paper proposes a Monte Carlo method for analyzing the sensitivity of boundary crossing probabilities of the Brownian motion to small changes of the boundary. Only for few boundaries the sensitivities can be computed in closed form. We propose an efficient Monte Carlo procedure for general boundaries and provide upper bounds for the bias and the simulation error. The third paper focuses on the inverse first-passage-times. The inverse first-passage-time problem deals with finding the boundary given the distribution of hitting times. Instead of a known distribution, we are given a sample of first hitting times and we propose and analyze estimators of the boundary. Firstly, we consider the empirical estimator and prove that it is strongly consistent and derive (an upper bound of) its asymptotic convergence rate. Secondly, we provide a Bayes estimator based on an approximate likelihood function. Monte Carlo experiments suggest that the empirical estimator is simple, computationally manageable and outperforms the alternative procedure considered in this paper.