2019, Bayer, Christian, Hammouda, Chiheb Ben, Tempone, Raúl F.

The rough Bergomi (rBergomi) model, introduced recently in [4], is a promising rough volatility model in quantitative finance. It is a parsimonious model depending on only three parameters, and yet exhibits remarkable fit to empirical implied volatility surfaces. In the absence of analytical European option pricing methods for the model, and due to the non-Markovian nature of the fractional driver, the prevalent option is to use the Monte Carlo (MC) simulation for pricing. Despite recent advances in the MC method in this context, pricing under the rBergomi model is still a timeconsuming task. To overcome this issue, we design a novel, hierarchical approach, based on i) adaptive sparse grids quadrature (ASGQ), and ii) quasi Monte Carlo (QMC). Both techniques are coupled with Brownian bridge construction and Richardson extrapolation. By uncovering the available regularity, our hierarchical methods demonstrate substantial computational gains with respect to the standard MC method, when reaching a sufficiently small relative error tolerance in the price estimates across different parameter constellations, even for very small values of the Hurst parameter. Our work opens a new research direction in this field, i.e., to investigate the performance of methods other than Monte Carlo for pricing and calibrating under the rBergomi model.

2011, Patterson, Robert I.A., Kraft, Markus, Wagner, Wolfgang

A class of stochastic algorithms for the numerical treatment of population balance equations is introduced. The algorithms are based on systems of weighted particles, in which coagulation events are modelled by a weight transfer that keeps the number of computational particles constant. The weighting mechanisms are designed in such a way that physical processes changing individual particles (such as growth, or other surface reactions) can be conveniently treated by the algorithms. Numerical experiments are performed for complex laminar premixed flame systems. Two members of the class of stochastic weighted particle methods are compared to each other and to a direct simulation algorithm. One weighted algorithm is shown to be consistently better than the other with respect to the statistical noise generated. Finally, run times to achieve fixed error tolerances for a real flame system are measured and the better weighted algorithm is found to be up to three times faster than the direct simulation algorithm.

2022, Bayer, Christian, Ben Hammouda, Chiheb, Tempone, Raúl F.

When approximating the expectation of a functional of a stochastic process, the efficiency and performance of deterministic quadrature methods, such as sparse grid quadrature and quasi-Monte Carlo (QMC) methods, may critically depend on the regularity of the integrand. To overcome this issue and reveal the available regularity, we consider cases in which analytic smoothing cannot be performed, and introduce a novel numerical smoothing approach by combining a root finding algorithm with one-dimensional integration with respect to a single well-selected variable. We prove that under appropriate conditions, the resulting function of the remaining variables is a highly smooth function, potentially affording the improved efficiency of adaptive sparse grid quadrature (ASGQ) and QMC methods, particularly when combined with hierarchical transformations (i.e., Brownian bridge and Richardson extrapolation on the weak error). This approach facilitates the effective treatment of high dimensionality. Our study is motivated by option pricing problems, and our focus is on dynamics where the discretization of the asset price is necessary. Based on our analysis and numerical experiments, we show the advantages of combining numerical smoothing with the ASGQ and QMC methods over ASGQ and QMC methods without smoothing and the Monte Carlo approach.

2019, Bayer, Christian, Tempone , Raúl F., Wolfers, Sören

We present a novel method for the numerical pricing of American options based on Monte Carlo simulation and the optimization of exercise strategies. Previous solutions to this problem either explicitly or implicitly determine so-called optimal exercise regions, which consist of points in time and space at which a given option is exercised. In contrast, our method determines the exercise rates of randomized exercise strategies. We show that the supremum of the corresponding stochastic optimization problem provides the correct option price. By integrating analytically over the random exercise decision, we obtain an objective function that is differentiable with respect to perturbations of the exercise rate even for finitely many sample paths. The global optimum of this function can be approached gradually when starting from a constant exercise rate. Numerical experiments on vanilla put options in the multivariate Black-Scholes model and a preliminary theoretical analysis underline the efficiency of our method, both with respect to the number of time-discretization steps and the required number of degrees of freedom in the parametrization of the exercise rates. Finally, we demonstrate the flexibility of our method through numerical experiments on max call options in the classical Black-Scholes model, and vanilla put options in both the Heston model and the non-Markovian rough Bergomi model.

2018, Feix, Felix Ihbo, Riechert, Henning, Benson, Oliver, O'Reilly, Eoin

(In,Ga)N/GaN-Leuchtdioden wurden vor mehr als 10 Jahren kommerzialisiert, dennoch ist das Verständnis über den Einfluss von Lokalisierung auf die Rekombinationsdynamik in den (In,Ga)N/GaN Quantengräben (QG) unvollständig. In dieser Arbeit nutzen wir die temperaturabhängige stationäre und zeitaufgelöste Spektroskopie der Photolumineszenz (PL), um diesen Einfluss in einer typischen Ga-polaren, planaren (In,Ga)N/GaN-QG-Struktur zu untersuchen. Zusätzlich dehnen wir unsere Studie auf N-polare, axiale (In,Ga)N/GaN Quantumscheiben, nichtpolare Kern/Mantel GaN/(In,Ga)N µ-Drähte und Ga-polare, submonolage InN/GaN Übergitter aus. Während wir einen einfach exponentiellen Abfall der PL-Intensität in den nichtpolaren QG beobachten (Hinweise auf die Rekombination von Exzitonen), folgen die PL-Transienten in polaren QG asymptotisch einem Potenzgesetz. Dieses Potenzgesetz weist auf eine Rekombination zwischen individuell lokalisierten, räumlich getrennten Elektronen und Löchern hin. Für einen solchen Zerfall kann keine eindeutige PL-Lebensdauer definiert werden, was die Schätzung der internen Quanteneffizienz und die Bestimmung einer Diffusionslänge erschwert. Um nützliche Rekombinationsparameter und Diffusivitäten für die polaren QG zu extrahieren, analysieren wir die PL-Transienten mit positionsabhängigen Diffusionsreaktionsgleichungen, die durch einen Monte-Carlo-Algorithmus effizient gelöst werden. Aus diesen Simulationen ergibt sich, dass das asymptotische Potenzgesetz auch bei effizienter nichtstrahlender Rekombination (z. B. in den Nanodrähten) erhalten bleibt. Zudem stellen wir fest, dass sich die InN/GaN Übergitter elektronisch wie konventionelle (In,Ga)N/GaN QG verhalten, aber mit starkem, thermisch aktiviertem nichtstrahlenden Kanal. Des Weiteren zeigen wir, dass das Verhältnis von Lokalisierungs- und Exzitonenbindungsenergie bestimmt, dass die Rekombination entweder durch das Tunneln von Elektronen und Löchern oder durch den Zerfall von Exzitonen dominiert wird.