Pricing in stochastic-local volatility models with default

In recent years is becoming increasingly important to handle credit risk. Credit risk is the risk associated with the possibility of bankruptcy. More precisely, if a derivative provides for a payment at cert time T but before that time the counterparty defaults, at maturity the payment cannot be effectively performed, so the owner of the contract loses it entirely or a part of it. It means that the payoff of the derivative, and consequently its price, depends on the underlying of the basic derivative and on the risk of bankruptcy of the counterparty. To value and to hedge credit risk in a consistent way, one needs to develop a quantitative model. We have studied analytical approximation formulas and numerical methods such as Monte Carlo method in order to calculate the price of a bond. We have illustrated how to obtain fast and accurate pricing approximations by expanding the drift and diffusion as a Taylor series and we have compared the second and third order approximation of the Bond and Call price with an accurate Monte Carlo simulation. We have analysed JDCEV model with constant or stochastic interest rate. We have provided numerical examples that illustrate the effectiveness and versatility of our methods. We have used Wolfram Mathematica and Matlab.

Numerical modelling of fluid-structure interaction with application in hemodynamics

The thesis deals with numerical algorithms for fluid-structure interaction problems with application in blood flow modelling. It starts with a short introduction on the mathematical description of incompressible viscous flow with non-Newtonian viscosity and a moving linear viscoelastic structure. The mathematical model consists of the generalized Navier-Stokes equation used for the description of fluid flow and the generalized string model for structure movement. The arbitrary Lagrangian-Eulerian approach is used in order to take into account moving computational domain. A part of the thesis is devoted to the discussion on the non-Newtonian behaviour of shear-thinning fluids, which is in our case blood, and derivation of two non-Newtonian models frequently used in the blood flow modelling. Further we give a brief overview on recent fluid-structure interaction schemes with discussion about the difficulties arising in numerical modelling of blood flow. Our main contribution lies in numerical and experimental study of a new loosely-coupled partitioned scheme called the kinematic splitting fluid-structure interaction algorithm. We present stability analysis for a coupled problem of non-Newtonian shear-dependent fluids in moving domains with viscoelastic boundaries. Here, we assume both, the nonlinearity in convective as well is diffusive term. We analyse the convergence of proposed numerical scheme for a simplified fluid model of the Oseen type. Moreover, we present series of experiments including numerical error analysis, comparison of hemodynamic parameters for the Newtonian and non-Newtonian fluids and comparison of several physiologically relevant computational geometries in terms of wall displacement and wall shear stress. Numerical analysis and extensive experimental study for several standard geometries confirm reliability and accuracy of the proposed kinematic splitting scheme in order to approximate fluid-structure interaction problems.

Random lattice models

This thesis deals with three different physical models, where each model involves a random component which is linked to a cubic lattice. First, a model is studied, which is used in numerical calculations of Quantum Chromodynamics.In these calculations random gauge-fields are distributed on the bonds of the lattice. The formulation of the model is fitted into the mathematical framework of ergodic operator families. We prove, that for small coupling constants, the ergodicity of the underlying probability measure is indeed ensured and that the integrated density of states of the Wilson-Dirac operator exists. The physical situations treated in the next two chapters are more similar to one another. In both cases the principle idea is to study a fermion system in a cubic crystal with impurities, that are modeled by a random potential located at the lattice sites. In the second model we apply the Hartree-Fock approximation to such a system. For the case of reduced Hartree-Fock theory at positive temperatures and a fixed chemical potential we consider the limit of an infinite system. In that case we show the existence and uniqueness of minimizers of the Hartree-Fock functional. In the third model we formulate the fermion system algebraically via C*-algebras. The question imposed here is to calculate the heat production of the system under the influence of an outer electromagnetic field. We show that the heat production corresponds exactly to what is empirically predicted by Joule's law in the regime of linear response.

Zoomed simulations of Halo segregation in cosmological models with two species of coupled dark matter

La materia ordinaria copre soli pochi punti percentuali della massa-energia totale dell'Universo, che è invece largamente dominata da componenti “oscure”. Il modello standard usato per descriverle è il modello LambdaCDM. Nonostante esso sembri consistente con la maggior parte dei dati attualmente disponibili, presenta alcuni problemi fondamentali che ad oggi restano irrisolti, lasciando spazio per lo studio di modelli cosmologici alternativi. Questa Tesi mira a studiare un modello proposto recentemente, chiamato “Multi-coupled Dark Energy” (McDE), che presenta interazioni modificate rispetto al modello LambdaCDM. In particolare, la Materia Oscura è composta da due diversi tipi di particelle con accoppiamento opposto rispetto ad un campo scalare responsabile dell'Energia Oscura. L'evoluzione del background e delle perturbazioni lineari risultano essere indistinguibili da quelle del modello LambdaCDM. In questa Tesi viene presentata per la prima volta una serie di simulazioni numeriche “zoomed”. Esse presentano diverse regioni con risoluzione differente, centrate su un singolo ammasso di interesse, che permettono di studiare in dettaglio una singola struttura senza aumentare eccessivamente il tempo di calcolo necessario. Un codice chiamato ZInCo, da me appositamente sviluppato per questa Tesi, viene anch'esso presentato per la prima volta. Il codice produce condizioni iniziali adatte a simulazioni cosmologiche, con differenti regioni di risoluzione, indipendenti dal modello cosmologico scelto e che preservano tutte le caratteristiche dello spettro di potenza imposto su di esse. Il codice ZInCo è stato usato per produrre condizioni iniziali per una serie di simulazioni numeriche del modello McDE, le quali per la prima volta mostrano, grazie all'alta risoluzione raggiunta, che l'effetto di segregazione degli ammassi avviene significativamente prima di quanto stimato in precedenza. Inoltre, i profili radiale di densità ottenuti mostrano un appiattimento centrale nelle fasi iniziali della segregazione. Quest'ultimo effetto potrebbe aiutare a risolvere il problema “cusp-core” del modello LambdaCDM e porre limiti ai valori dell'accoppiamento possibili.

2D and 3D numerical modelling of multilayer detachment folding and salt tectonics

Numerical modelling was performed to study the dynamics of multilayer detachment folding and salt tectonics. In the case of multilayer detachment folding, analytically derived diagrams show several folding modes, half of which are applicable to crustal scale folding. 3D numerical simulations are in agreement with 2D predictions, yet fold interactions result in complex fold patterns. Pre-existing salt diapirs change folding patterns as they localize the initial deformation. If diapir spacing is much smaller than the dominant folding wavelength, diapirs appear in fold synclines or limbs.rnNumerical models of 3D down-building diapirism show that sedimentation rate controls whether diapirs will form and influences the overall patterns of diapirism. Numerical codes were used to retrodeform modelled salt diapirs. Reverse modelling can retrieve the initial geometries of a 2D Rayleigh-Taylor instability with non-linear rheologies. Although intermediate geometries of down-built diapirs are retrieved, forward and reverse modelling solutions deviate. rnFinally, the dynamics of fold-and-thrusts belts formed over a tilted viscous detachment is studied and it is demonstrated that mechanical stratigraphy has an impact on the deformation style, switching from thrust- to folding-dominated. The basal angle of the detachment controls the deformation sequence of the fold-and-thrust belt and results are consistent with critical wedge theory.rn

Testing of subgrid scale (SGS) models for large-eddy simulation (LES) of turbulent channel flow

Sub-grid scale (SGS) models are required in order to model the influence of the unresolved small scales on the resolved scales in large-eddy simulations (LES), the flow at the smallest scales of turbulence. In the following work two SGS models are presented and deeply analyzed in terms of accuracy through several LESs with different spatial resolutions, i.e. grid spacings. The first part of this thesis focuses on the basic theory of turbulence, the governing equations of fluid dynamics and their adaptation to LES. Furthermore, two important SGS models are presented: one is the Dynamic eddy-viscosity model (DEVM), developed by \cite{germano1991dynamic}, while the other is the Explicit Algebraic SGS model (EASSM), by \cite{marstorp2009explicit}. In addition, some details about the implementation of the EASSM in a Pseudo-Spectral Navier-Stokes code \cite{chevalier2007simson} are presented. The performance of the two aforementioned models will be investigated in the following chapters, by means of LES of a channel flow, with friction Reynolds numbers $Re_\tau=590$ up to $Re_\tau=5200$, with relatively coarse resolutions. Data from each simulation will be compared to baseline DNS data. Results have shown that, in contrast to the DEVM, the EASSM has promising potentials for flow predictions at high friction Reynolds numbers: the higher the friction Reynolds number is the better the EASSM will behave and the worse the performances of the DEVM will be. The better performance of the EASSM is contributed to the ability to capture flow anisotropy at the small scales through a correct formulation for the SGS stresses. Moreover, a considerable reduction in the required computational resources can be achieved using the EASSM compared to DEVM. Therefore, the EASSM combines accuracy and computational efficiency, implying that it has a clear potential for industrial CFD usage.

Statistical mechanics of polymer models: rigorous results and applications

Monomer-dimer models are amongst the models in statistical mechanics which found application in many areas of science, ranging from biology to social sciences. This model describes a many-body system in which monoatomic and diatomic particles subject to hard-core interactions get deposited on a graph. In our work we provide an extension of this model to higher-order particles. The aim of our work is threefold: first we study the thermodynamic properties of the newly introduced model. We solve analytically some regular cases and find that, differently from the original, our extension admits phase transitions. Then we tackle the inverse problem, both from an analytical and numerical perspective. Finally we propose an application to aggregation phenomena in virtual messaging services.

Numerical assessment on the effective mechanical stimuli for matrix-associated metabolism in chondrocyte-seeded constructs

The self-regeneration capacity of articular cartilage is limited, due to its avascular and aneural nature. Loaded explants and cell cultures demonstrated that chondrocyte metabolism can be regulated via physiologic loading. However, the explicit ranges of mechanical stimuli that correspond to favourable metabolic response associated with extracellular matrix (ECM) synthesis are elusive. Unsystematic protocols lacking this knowledge produce inconsistent results. This study aims to determine the intrinsic ranges of physical stimuli that increase ECM synthesis and simultaneously inhibit nitric oxide (NO) production in chondrocyte-agarose constructs, by numerically re-evaluating the experiments performed by Tsuang et al. (2008). Twelve loading patterns were simulated with poro-elastic finite element models in ABAQUS. Pressure on solid matrix, von Mises stress, maximum principle stress and pore pressure were selected as intrinsic mechanical stimuli. Their development rates and magnitudes at the steady state of cyclic loading were calculated with MATLAB at the construct level. Concurrent increase in glycosaminoglycan and collagen was observed at 2300 Pa pressure and 40 Pa/s pressure rate. Between 0-1500 Pa and 0-40 Pa/s, NO production was consistently positive with respect to controls, whereas ECM synthesis was negative in the same range. A linear correlation was found between pressure rate and NO production (R = 0.77). Stress states identified in this study are generic and could be used to develop predictive algorithms for matrix production in agarose-chondrocyte constructs of arbitrary shape, size and agarose concentration. They could also be helpful to increase the efficacy of loading protocols for avascular tissue engineering. Copyright (c) 2010 John Wiley \& Sons, Ltd.

Robust Inferences For Covariate Effects On Survival Time With Censored Linear Regression Models

Various inference procedures for linear regression models with censored failure times have been studied extensively. Recent developments on efficient algorithms to implement these procedures enhance the practical usage of such models in survival analysis. In this article, we present robust inferences for certain covariate effects on the failure time in the presence of "nuisance" confounders under a semiparametric, partial linear regression setting. Specifically, the estimation procedures for the regression coefficients of interest are derived from a working linear model and are valid even when the function of the confounders in the model is not correctly specified. The new proposals are illustrated with two examples and their validity for cases with practical sample sizes is demonstrated via a simulation study.

Gauss-Seidel Estimation of Generalized Linear Mixed Models with Application to Poisson Modeling of Spatially Varying Disease Rates

Generalized linear mixed models (GLMMs) provide an elegant framework for the analysis of correlated data. Due to the non-closed form of the likelihood, GLMMs are often fit by computational procedures like penalized quasi-likelihood (PQL). Special cases of these models are generalized linear models (GLMs), which are often fit using algorithms like iterative weighted least squares (IWLS). High computational costs and memory space constraints often make it difficult to apply these iterative procedures to data sets with very large number of cases. This paper proposes a computationally efficient strategy based on the Gauss-Seidel algorithm that iteratively fits sub-models of the GLMM to subsetted versions of the data. Additional gains in efficiency are achieved for Poisson models, commonly used in disease mapping problems, because of their special collapsibility property which allows data reduction through summaries. Convergence of the proposed iterative procedure is guaranteed for canonical link functions. The strategy is applied to investigate the relationship between ischemic heart disease, socioeconomic status and age/gender category in New South Wales, Australia, based on outcome data consisting of approximately 33 million records. A simulation study demonstrates the algorithm's reliability in analyzing a data set with 12 million records for a (non-collapsible) logistic regression model.

On the Behaviour of Marginal and Conditional Akaike Information Criteria in Linear Mixed Models

In linear mixed models, model selection frequently includes the selection of random effects. Two versions of the Akaike information criterion (AIC) have been used, based either on the marginal or on the conditional distribution. We show that the marginal AIC is no longer an asymptotically unbiased estimator of the Akaike information, and in fact favours smaller models without random effects. For the conditional AIC, we show that ignoring estimation uncertainty in the random effects covariance matrix, as is common practice, induces a bias that leads to the selection of any random effect not predicted to be exactly zero. We derive an analytic representation of a corrected version of the conditional AIC, which avoids the high computational cost and imprecision of available numerical approximations. An implementation in an R package is provided. All theoretical results are illustrated in simulation studies, and their impact in practice is investigated in an analysis of childhood malnutrition in Zambia.

Checking Assumptions in Latent Class Regression Models via a Markov Chain Monte Carlo Estimation Approach: An Application to Depression and Socio-Economic Status

Latent class regression models are useful tools for assessing associations between covariates and latent variables. However, evaluation of key model assumptions cannot be performed using methods from standard regression models due to the unobserved nature of latent outcome variables. This paper presents graphical diagnostic tools to evaluate whether or not latent class regression models adhere to standard assumptions of the model: conditional independence and non-differential measurement. An integral part of these methods is the use of a Markov Chain Monte Carlo estimation procedure. Unlike standard maximum likelihood implementations for latent class regression model estimation, the MCMC approach allows us to calculate posterior distributions and point estimates of any functions of parameters. It is this convenience that allows us to provide the diagnostic methods that we introduce. As a motivating example we present an analysis focusing on the association between depression and socioeconomic status, using data from the Epidemiologic Catchment Area study. We consider a latent class regression analysis investigating the association between depression and socioeconomic status measures, where the latent variable depression is regressed on education and income indicators, in addition to age, gender, and marital status variables. While the fitted latent class regression model yields interesting results, the model parameters are found to be invalid due to the violation of model assumptions. The violation of these assumptions is clearly identified by the presented diagnostic plots. These methods can be applied to standard latent class and latent class regression models, and the general principle can be extended to evaluate model assumptions in other types of models.

Numerical study on the curling and warping of hardened rigid pavement slabs

In-service hardened concrete pavement suffers from environmental loadings caused by curling and warping of the slab. Traditionally, these loadings are computed on the basis of treating the slab as an elastic material, and of evaluating separately the curling and warping components. This dissertation simulates temperature distribution and moisture distribution through the slabs by use of a developed numerical model that couples the heat transfer and moisture transport. The computation of environmental loadings treats the slab as an elastic-viscous material, which considers the relaxation behavior and Pickett effect of the concrete. The heat transfer model considers the impacts of solar radiation, wind speed, air temperature, pavement slab albedo, etc. on the pavement temperature distribution. This dissertation assesses the difference between documented models that aim to predict pavement temperature, highlighting their pros and cons. The moisture transport model is unique for the documented models; it mimics the wetting and drying events occurring at the slab surface. These events are estimated by a proposed statistical algorithm, which is verified by field rainfall data. Analysis of the predicted results examines on the roles of the local air RH (relative humidity), wind speed, rainy pattern in the moisture distribution through the slab. The findings reveal that seasonal air RH plays a decisive role on the slab‘s moisture distribution; but wind speed and its daily variation, daily RH variation, and seasonal rainfall pattern plays only a secondary role. This dissertation sheds light on the computation of environmental loadings that in-service pavement slabs suffer from. Analysis of the computed stresses centers on the stress relaxation near the surface, stress evolution after the curing ends, and the impact of construction season on the stress‘s magnitude. An unexpected finding is that the total environmental loadings at the cyclically-stable state divert from the thermal stresses. At such a state, the total stress at the daytime is roughly equal to the thermal stress; whereas the total stress during the nighttime is far greater than the thermal stress. An explanation for this phenomenon is that during the night hours, the decline of the slab‘s near-surface temperature leads to a drop of the near-surface RH. This RH drop results in contraction therein and develops additional tensile stresses. The dissertation thus argues that estimating the environmental loadings by solely computing the thermally-induced stresses may reach delusive results. It recommends that the total environmental loadings of in-service slabs should be estimated by a sophisticated model coupling both moisture component and temperature component.