Black-scholes type equations

In this work we are concerned with the analysis and numerical solution of Black-Scholes type equations arising in the modeling of incomplete financial markets and an inverse problem of determining the local volatility function in a generalized Black-Scholes model from observed option prices. In the first chapter a fully nonlinear Black-Scholes equation which models transaction costs arising in option pricing is discretized by a new high order compact scheme. The compact scheme is proved to be unconditionally stable and non-oscillatory and is very efficient compared to classical schemes. Moreover, it is shown that the finite difference solution converges locally uniformly to the unique viscosity solution of the continuous equation. In the next chapter we turn to the calibration problem of computing local volatility functions from market data in a generalized Black-Scholes setting. We follow an optimal control approach in a Lagrangian framework. We show the existence of a global solution and study first- and second-order optimality conditions. Furthermore, we propose an algorithm that is based on a globalized sequential quadratic programming method and a primal-dual active set strategy, and present numerical results. In the last chapter we consider a quasilinear parabolic equation with quadratic gradient terms, which arises in the modeling of an optimal portfolio in incomplete markets. The existence of weak solutions is shown by considering a sequence of approximate solutions. The main difficulty of the proof is to infer the strong convergence of the sequence. Furthermore, we prove the uniqueness of weak solutions under a smallness condition on the derivatives of the covariance matrices with respect to the solution, but without additional regularity assumptions on the solution. The results are illustrated by a numerical example.

Reinforcement Lernen mit Regularisierungsnetzwerken

Die Arbeit behandelt das Problem der Skalierbarkeit von Reinforcement Lernen auf hochdimensionale und komplexe Aufgabenstellungen. Unter Reinforcement Lernen versteht man dabei eine auf approximativem Dynamischen Programmieren basierende Klasse von Lernverfahren, die speziell Anwendung in der Künstlichen Intelligenz findet und zur autonomen Steuerung simulierter Agenten oder realer Hardwareroboter in dynamischen und unwägbaren Umwelten genutzt werden kann. Dazu wird mittels Regression aus Stichproben eine Funktion bestimmt, die die Lösung einer "Optimalitätsgleichung" (Bellman) ist und aus der sich näherungsweise optimale Entscheidungen ableiten lassen. Eine große Hürde stellt dabei die Dimensionalität des Zustandsraums dar, die häufig hoch und daher traditionellen gitterbasierten Approximationsverfahren wenig zugänglich ist. Das Ziel dieser Arbeit ist es, Reinforcement Lernen durch nichtparametrisierte Funktionsapproximation (genauer, Regularisierungsnetze) auf -- im Prinzip beliebig -- hochdimensionale Probleme anwendbar zu machen. Regularisierungsnetze sind eine Verallgemeinerung von gewöhnlichen Basisfunktionsnetzen, die die gesuchte Lösung durch die Daten parametrisieren, wodurch die explizite Wahl von Knoten/Basisfunktionen entfällt und so bei hochdimensionalen Eingaben der "Fluch der Dimension" umgangen werden kann. Gleichzeitig sind Regularisierungsnetze aber auch lineare Approximatoren, die technisch einfach handhabbar sind und für die die bestehenden Konvergenzaussagen von Reinforcement Lernen Gültigkeit behalten (anders als etwa bei Feed-Forward Neuronalen Netzen). Allen diesen theoretischen Vorteilen gegenüber steht allerdings ein sehr praktisches Problem: der Rechenaufwand bei der Verwendung von Regularisierungsnetzen skaliert von Natur aus wie O(n**3), wobei n die Anzahl der Daten ist. Das ist besonders deswegen problematisch, weil bei Reinforcement Lernen der Lernprozeß online erfolgt -- die Stichproben werden von einem Agenten/Roboter erzeugt, während er mit der Umwelt interagiert. Anpassungen an der Lösung müssen daher sofort und mit wenig Rechenaufwand vorgenommen werden. Der Beitrag dieser Arbeit gliedert sich daher in zwei Teile: Im ersten Teil der Arbeit formulieren wir für Regularisierungsnetze einen effizienten Lernalgorithmus zum Lösen allgemeiner Regressionsaufgaben, der speziell auf die Anforderungen von Online-Lernen zugeschnitten ist. Unser Ansatz basiert auf der Vorgehensweise von Recursive Least-Squares, kann aber mit konstantem Zeitaufwand nicht nur neue Daten sondern auch neue Basisfunktionen in das bestehende Modell einfügen. Ermöglicht wird das durch die "Subset of Regressors" Approximation, wodurch der Kern durch eine stark reduzierte Auswahl von Trainingsdaten approximiert wird, und einer gierigen Auswahlwahlprozedur, die diese Basiselemente direkt aus dem Datenstrom zur Laufzeit selektiert. Im zweiten Teil übertragen wir diesen Algorithmus auf approximative Politik-Evaluation mittels Least-Squares basiertem Temporal-Difference Lernen, und integrieren diesen Baustein in ein Gesamtsystem zum autonomen Lernen von optimalem Verhalten. Insgesamt entwickeln wir ein in hohem Maße dateneffizientes Verfahren, das insbesondere für Lernprobleme aus der Robotik mit kontinuierlichen und hochdimensionalen Zustandsräumen sowie stochastischen Zustandsübergängen geeignet ist. Dabei sind wir nicht auf ein Modell der Umwelt angewiesen, arbeiten weitestgehend unabhängig von der Dimension des Zustandsraums, erzielen Konvergenz bereits mit relativ wenigen Agent-Umwelt Interaktionen, und können dank des effizienten Online-Algorithmus auch im Kontext zeitkritischer Echtzeitanwendungen operieren. Wir demonstrieren die Leistungsfähigkeit unseres Ansatzes anhand von zwei realistischen und komplexen Anwendungsbeispielen: dem Problem RoboCup-Keepaway, sowie der Steuerung eines (simulierten) Oktopus-Tentakels.

Parahydrogen induced polarization on a clinical MRI system : polarization transfer of two spin order

Hyperpolarization techniques enhance the nuclear spin polarization and thus allow for new nuclear magnetic resonance applications like in vivo metabolic imaging. One of these techniques is Parahydrogen Induced Polarization (PHIP). It leads to a hyperpolarized 1H spin state which can be transferred to a heteronucleus like 13C by a radiofrequency (RF) pulse sequence. In this work, timing of such a sequence was analyzed and optimized for the molecule hydroxyethyl propionate. The pulse sequence was adapted for the work on a clinical magnetic resonance imaging (MRI) system which is usually equipped only with a single RF transmit channel. Optimal control theory optimizations were performed to achieve an optimized polarization transfer. A drawback of hyperpolarization is its limited lifetime due to relaxation processes. The lifetime can be increased by storing the hyperpolarization in a spin singlet state. The second part of this work therefore addresses the spin singlet state of the Cs-symmetric molecule dimethyl maleate which needs to be converted to the spin triplet state to be detectable. This conversion was realized on a clinical MRI system, both by field cycling and by two RF pulse sequences which were adapted and optimized for this purpose. Using multiple conversions enables the determination of the lifetime of the singlet state as well as the conversion efficiency of the RF pulse sequence. Both, the hyperpolarized 13C spin state and the converted singlet state were utilized for MR imaging. Careful choice of the echo time was shown to be crucial for both molecules.

Management control systems : subcomponents, optimal design and the role of time as a contingency

Management Control System (MCS) research is undergoing turbulent times. For a long time related to cybernetic instruments of management accounting only, MCS are increasingly seen as complex systems comprising not only formal accounting-driven instruments, but also informal mechanisms of control based on organizational culture. But not only have the means of MCS changed; researchers increasingly ap-ply MCS to organizational goals other than strategy implementation.rnrnTaking the question of "How do I design a well-performing MCS?" as a starting point, this dissertation aims at providing a comprehensive and integrated overview of the "current-state" of MCS research. Opting for a definition of MCS, broad in terms of means (all formal as well as informal MCS instruments), but focused in terms of objectives (behavioral control only), the dissertation contributes to MCS theory by, a) developing an integrated (contingency) model of MCS, describing its contingencies, as well as its subcomponents, b) refining the equifinality model of Gresov/Drazin (1997), c) synthesizing research findings from contingency and configuration research concerning MCS, taking into account case studies on research topics such as ambi-dexterity, equifinality and time as a contingency.

Field-free control of magnetism in nanostructured materials probed with high resolution X-ray microscopy

Magnetic memories are a backbone of today's digital data storage technology, where the digital information is stored as the magnetic configuration of nanostructured ferromagnetic bits. Currently, the writing of the digital information on the magnetic memory is carried out with the help of magnetic fields. This approach, while viable, is not optimal due to its intrinsically high energy consumption and relatively poor scalability. For this reason, the research for different mechanisms that can be used to manipulate the magnetic configuration of a material is of interest. In this thesis, the control of the magnetization of different nanostructured materials with field-free mechanisms is investigated. The magnetic configuration of these nanostructured materials was imaged directly with high resolution x-ray magnetic microscopy. rnFirst of all, the control of the magnetic configuration of nanostructured ferromagnetic Heusler compounds by fabricating nanostructures with different geometries was analyzed. Here, it was observed that the magnetic configuration of the nanostructured elements is given by the competition of magneto-crystalline and shape anisotropy. By fabricating elements with different geometries, we could alter the point where these two effects equilibrate, allowing for the possibility to tailor the magnetic configuration of these nanostructured elements to the required necessities.rnThen, the control of the magnetic configuration of Ni nanostructures fabricated on top of a piezoelectric material with the magneto-elastic effect (i.e. by applying a piezoelectric strain to the Ni nanostructures) was investigated. Here, the magneto-elastic coupling effect gives rise to an additional anisotropy contribution, proportional to the strain applied to the magnetic material. For this system, a reproducible and reversible control of the magnetic configuration of the nanostructured Ni elements with the application of an electric field across the piezoelectric material was achieved.rnFinally, the control of the magnetic configuration of La0.7Sr0.3MnO3 (LSMO) nanostructures with spin-polarized currents was studied. Here, the spin-transfer torque effect was employed to achieve the displacement of magnetic domain walls in the LSMO nanostructures. A high spin-transfer torque efficiency was observed for LSMO at low temperatures, and a Joule-heating induced hopping of the magnetic domain walls was observed at room temperatures, allowing for the analysis of the energetics of the domain walls in LSMO.rnThe results presented in this thesis give thus an overview on the different field-free approaches that can be used to manipulate and tailor the magnetization configuration of a nanostructured material to the various technological requirements, opening up novel interesting possibilities for these materials.