A decomposition formula for option prices in the Heston model and applications to option pricing approximation

By means of classical Itô's calculus we decompose option prices asthe sum of the classical Black-Scholes formula with volatility parameterequal to the root-mean-square future average volatility plus a term dueby correlation and a term due to the volatility of the volatility. Thisdecomposition allows us to develop first and second-order approximationformulas for option prices and implied volatilities in the Heston volatilityframework, as well as to study their accuracy. Numerical examples aregiven.

Variance reduction methods for simulation of densities on Wiener space

We develop a general error analysis framework for the Monte Carlo simulationof densities for functionals in Wiener space. We also study variancereduction methods with the help of Malliavin derivatives. For this, wegive some general heuristic principles which are applied to diffusionprocesses. A comparison with kernel density estimates is made.

Incremental neural networks for function approximation

A new strategy for incremental building of multilayer feedforward neural networks is proposed in the context of approximation of functions from R-p to R-q using noisy data. A stopping criterion based on the properties of the noise is also proposed. Experimental results for both artificial and real data are performed and two alternatives of the proposed construction strategy are compared.

Reliability of the density functional approximation to describe the charge transfer and electrostatic complexes involved in the modeling of organic conducting polymers

Both the intermolecular interaction energies and the geometries for M ̄ thiophene, M ̄ pyrrole, M n+ ̄ thiophene, and M n+ ̄ pyrrole ͑with M = Li, Na, K, Ca, and Mg; and M n+ = Li+ , Na+ , K+ , Ca2+, and Mg2+͒ have been estimated using four commonly used density functional theory ͑DFT͒ methods: B3LYP, B3PW91, PBE, and MPW1PW91. Results have been compared to those provided by HF, MP2, and MP4 conventional ab initio methods. The PBE and MPW1PW91 are the only DFT methods able to provide a reasonable description of the M ̄ complexes. Regarding M n+ ̄ ␲ complexes, the four DFT methods have been proven to be adequate in the prediction of these electrostatically stabilized systems, even though they tend to overestimate the interaction energies.

The octagon, the hendecagon and the approximation of pi: the geometric design of the clypeus in the enclosure of Imperial cult in Tarraco

The ancient temple dedicated to the Roman Emperor Augustus on the hilltop of Tarraco (today’s Tarragona), was the main element of the sacred precinct of the Imperial cult. It was a two hectare square, bordered by a portico with an attic decorated with a sequence of clypeus (i.e. monumental shields) made with marble plates from the Luni-Carrara’s quarries. This contribution presents the results of the analysis of a three-dimensional photogrammetric survey of one of these clipeus, partially restored and exhibited at the National Archaeological Museum of Tarragona. The perimeter ring was bounded by a sequence of meanders inscribed in a polygon of 11 sides, a hendecagon. Moreover, a closer geometric analysis suggests that the relationship between the outer meander rim and the oval pearl ring that delimited the divinity of Jupiter Ammon can be accurately determined by the diagonals of an octagon inscribed in the perimeter of the clypeus. This double evidence suggests a combined layout, in the same design, of an octagon and a hendecagon. Hypothetically, this could be achieved by combining the octagon with the approximation to Pi used in antiquity: 22/7 of the circle’s diameter. This method allows the drawing of a hendecagon with a clearly higher precision than with other ancient methods. Even the modelling of the motifs that separate the different decorative stripes corroborates the geometric scheme that we propose.

The effect of skewness and kurtosis on the Kenward-Roger approximation when group distributions differ

This study examined the independent effect of skewness and kurtosis on the robustness of the linear mixed model (LMM), with the Kenward-Roger (KR) procedure, when group distributions are different, sample sizes are small, and sphericity cannot be assumed. Methods: A Monte Carlo simulation study considering a split-plot design involving three groups and four repeated measures was performed. Results: The results showed that when group distributions are different, the effect of skewness on KR robustness is greater than that of kurtosis for the corresponding values. Furthermore, the pairings of skewness and kurtosis with group size were found to be relevant variables when applying this procedure. Conclusions: With sample sizes of 45 and 60, KR is a suitable option for analyzing data when the distributions are: (a) mesokurtic and not highly or extremely skewed, and (b) symmetric with different degrees of kurtosis. With total sample sizes of 30, it is adequate when group sizes are equal and the distributions are: (a) mesokurtic and slightly or moderately skewed, and sphericity is assumed; and (b) symmetric with a moderate or high/extreme violation of kurtosis. Alternative analyses should be considered when the distributions are highly or extremely skewed and samples sizes are small.

Approximation to the theory of affinities to manage the problems of the groupping process

New economic and enterprise needs have increased the interest and utility of the methods of the grouping process based on the theory of uncertainty. A fuzzy grouping (clustering) process is a key phase of knowledge acquisition and reduction complexity regarding different groups of objects. Here, we considered some elements of the theory of affinities and uncertain pretopology that form a significant support tool for a fuzzy clustering process. A Galois lattice is introduced in order to provide a clearer vision of the results. We made an homogeneous grouping process of the economic regions of Russian Federation and Ukraine. The obtained results gave us a large panorama of a regional economic situation of two countries as well as the key guidelines for the decision-making. The mathematical method is very sensible to any changes the regional economy can have. We gave an alternative method of the grouping process under uncertainty.

Learning Preferences with Kernel-Based Methods

Learning of preference relations has recently received significant attention in machine learning community. It is closely related to the classification and regression analysis and can be reduced to these tasks. However, preference learning involves prediction of ordering of the data points rather than prediction of a single numerical value as in case of regression or a class label as in case of classification. Therefore, studying preference relations within a separate framework facilitates not only better theoretical understanding of the problem, but also motivates development of the efficient algorithms for the task. Preference learning has many applications in domains such as information retrieval, bioinformatics, natural language processing, etc. For example, algorithms that learn to rank are frequently used in search engines for ordering documents retrieved by the query. Preference learning methods have been also applied to collaborative filtering problems for predicting individual customer choices from the vast amount of user generated feedback. In this thesis we propose several algorithms for learning preference relations. These algorithms stem from well founded and robust class of regularized least-squares methods and have many attractive computational properties. In order to improve the performance of our methods, we introduce several non-linear kernel functions. Thus, contribution of this thesis is twofold: kernel functions for structured data that are used to take advantage of various non-vectorial data representations and the preference learning algorithms that are suitable for different tasks, namely efficient learning of preference relations, learning with large amount of training data, and semi-supervised preference learning. Proposed kernel-based algorithms and kernels are applied to the parse ranking task in natural language processing, document ranking in information retrieval, and remote homology detection in bioinformatics domain. Training of kernel-based ranking algorithms can be infeasible when the size of the training set is large. This problem is addressed by proposing a preference learning algorithm whose computation complexity scales linearly with the number of training data points. We also introduce sparse approximation of the algorithm that can be efficiently trained with large amount of data. For situations when small amount of labeled data but a large amount of unlabeled data is available, we propose a co-regularized preference learning algorithm. To conclude, the methods presented in this thesis address not only the problem of the efficient training of the algorithms but also fast regularization parameter selection, multiple output prediction, and cross-validation. Furthermore, proposed algorithms lead to notably better performance in many preference learning tasks considered.

Kernel-Based Ranking. Methods for Learning and Performance Estimation

Machine learning provides tools for automated construction of predictive models in data intensive areas of engineering and science. The family of regularized kernel methods have in the recent years become one of the mainstream approaches to machine learning, due to a number of advantages the methods share. The approach provides theoretically well-founded solutions to the problems of under- and overfitting, allows learning from structured data, and has been empirically demonstrated to yield high predictive performance on a wide range of application domains. Historically, the problems of classification and regression have gained the majority of attention in the field. In this thesis we focus on another type of learning problem, that of learning to rank. In learning to rank, the aim is from a set of past observations to learn a ranking function that can order new objects according to how well they match some underlying criterion of goodness. As an important special case of the setting, we can recover the bipartite ranking problem, corresponding to maximizing the area under the ROC curve (AUC) in binary classification. Ranking applications appear in a large variety of settings, examples encountered in this thesis include document retrieval in web search, recommender systems, information extraction and automated parsing of natural language. We consider the pairwise approach to learning to rank, where ranking models are learned by minimizing the expected probability of ranking any two randomly drawn test examples incorrectly. The development of computationally efficient kernel methods, based on this approach, has in the past proven to be challenging. Moreover, it is not clear what techniques for estimating the predictive performance of learned models are the most reliable in the ranking setting, and how the techniques can be implemented efficiently. The contributions of this thesis are as follows. First, we develop RankRLS, a computationally efficient kernel method for learning to rank, that is based on minimizing a regularized pairwise least-squares loss. In addition to training methods, we introduce a variety of algorithms for tasks such as model selection, multi-output learning, and cross-validation, based on computational shortcuts from matrix algebra. Second, we improve the fastest known training method for the linear version of the RankSVM algorithm, which is one of the most well established methods for learning to rank. Third, we study the combination of the empirical kernel map and reduced set approximation, which allows the large-scale training of kernel machines using linear solvers, and propose computationally efficient solutions to cross-validation when using the approach. Next, we explore the problem of reliable cross-validation when using AUC as a performance criterion, through an extensive simulation study. We demonstrate that the proposed leave-pair-out cross-validation approach leads to more reliable performance estimation than commonly used alternative approaches. Finally, we present a case study on applying machine learning to information extraction from biomedical literature, which combines several of the approaches considered in the thesis. The thesis is divided into two parts. Part I provides the background for the research work and summarizes the most central results, Part II consists of the five original research articles that are the main contribution of this thesis.

The FCLT with Dependent Errors: an Helicopter Tour of the Quality of the Approximation

This note investigates the adequacy of the finite-sample approximation provided by the Functional Central Limit Theorem (FCLT) when the errors are allowed to be dependent. We compare the distribution of the scaled partial sums of some data with the distribution of the Wiener process to which it converges. Our setup is purposely very simple in that it considers data generated from an ARMA(1,1) process. Yet, this is sufficient to bring out interesting conclusions about the particular elements which cause the approximations to be inadequate in even quite large sample sizes.

Approximation de la distribution a posteriori d'un modèle Gamma-Poisson hiérarchique à effets mixtes

La méthode que nous présentons pour modéliser des données dites de "comptage" ou données de Poisson est basée sur la procédure nommée Modélisation multi-niveau et interactive de la régression de Poisson (PRIMM) développée par Christiansen et Morris (1997). Dans la méthode PRIMM, la régression de Poisson ne comprend que des effets fixes tandis que notre modèle intègre en plus des effets aléatoires. De même que Christiansen et Morris (1997), le modèle étudié consiste à faire de l'inférence basée sur des approximations analytiques des distributions a posteriori des paramètres, évitant ainsi d'utiliser des méthodes computationnelles comme les méthodes de Monte Carlo par chaînes de Markov (MCMC). Les approximations sont basées sur la méthode de Laplace et la théorie asymptotique liée à l'approximation normale pour les lois a posteriori. L'estimation des paramètres de la régression de Poisson est faite par la maximisation de leur densité a posteriori via l'algorithme de Newton-Raphson. Cette étude détermine également les deux premiers moments a posteriori des paramètres de la loi de Poisson dont la distribution a posteriori de chacun d'eux est approximativement une loi gamma. Des applications sur deux exemples de données ont permis de vérifier que ce modèle peut être considéré dans une certaine mesure comme une généralisation de la méthode PRIMM. En effet, le modèle s'applique aussi bien aux données de Poisson non stratifiées qu'aux données stratifiées; et dans ce dernier cas, il comporte non seulement des effets fixes mais aussi des effets aléatoires liés aux strates. Enfin, le modèle est appliqué aux données relatives à plusieurs types d'effets indésirables observés chez les participants d'un essai clinique impliquant un vaccin quadrivalent contre la rougeole, les oreillons, la rub\'eole et la varicelle. La régression de Poisson comprend l'effet fixe correspondant à la variable traitement/contrôle, ainsi que des effets aléatoires liés aux systèmes biologiques du corps humain auxquels sont attribués les effets indésirables considérés.

Solution Methods for Service Network Design with Resource Management Consideration

La gestion des ressources, équipements, équipes de travail, et autres, devrait être prise en compte lors de la conception de tout plan réalisable pour le problème de conception de réseaux de services. Cependant, les travaux de recherche portant sur la gestion des ressources et la conception de réseaux de services restent limités. La présente thèse a pour objectif de combler cette lacune en faisant l’examen de problèmes de conception de réseaux de services prenant en compte la gestion des ressources. Pour ce faire, cette thèse se décline en trois études portant sur la conception de réseaux. La première étude considère le problème de capacitated multi-commodity fixed cost network design with design-balance constraints(DBCMND). La structure multi-produits avec capacité sur les arcs du DBCMND, de même que ses contraintes design-balance, font qu’il apparaît comme sous-problème dans de nombreux problèmes reliés à la conception de réseaux de services, d’où l’intérêt d’étudier le DBCMND dans le contexte de cette thèse. Nous proposons une nouvelle approche pour résoudre ce problème combinant la recherche tabou, la recomposition de chemin, et une procédure d’intensification de la recherche dans une région particulière de l’espace de solutions. Dans un premier temps la recherche tabou identifie de bonnes solutions réalisables. Ensuite la recomposition de chemin est utilisée pour augmenter le nombre de solutions réalisables. Les solutions trouvées par ces deux méta-heuristiques permettent d’identifier un sous-ensemble d’arcs qui ont de bonnes chances d’avoir un statut ouvert ou fermé dans une solution optimale. Le statut de ces arcs est alors fixé selon la valeur qui prédomine dans les solutions trouvées préalablement. Enfin, nous utilisons la puissance d’un solveur de programmation mixte en nombres entiers pour intensifier la recherche sur le problème restreint par le statut fixé ouvert/fermé de certains arcs. Les tests montrent que cette approche est capable de trouver de bonnes solutions aux problèmes de grandes tailles dans des temps raisonnables. Cette recherche est publiée dans la revue scientifique Journal of heuristics. La deuxième étude introduit la gestion des ressources au niveau de la conception de réseaux de services en prenant en compte explicitement le nombre fini de véhicules utilisés à chaque terminal pour le transport de produits. Une approche de solution faisant appel au slope-scaling, la génération de colonnes et des heuristiques basées sur une formulation en cycles est ainsi proposée. La génération de colonnes résout une relaxation linéaire du problème de conception de réseaux, générant des colonnes qui sont ensuite utilisées par le slope-scaling. Le slope-scaling résout une approximation linéaire du problème de conception de réseaux, d’où l’utilisation d’une heuristique pour convertir les solutions obtenues par le slope-scaling en solutions réalisables pour le problème original. L’algorithme se termine avec une procédure de perturbation qui améliore les solutions réalisables. Les tests montrent que l’algorithme proposé est capable de trouver de bonnes solutions au problème de conception de réseaux de services avec un nombre fixe des ressources à chaque terminal. Les résultats de cette recherche seront publiés dans la revue scientifique Transportation Science. La troisième étude élargie nos considérations sur la gestion des ressources en prenant en compte l’achat ou la location de nouvelles ressources de même que le repositionnement de ressources existantes. Nous faisons les hypothèses suivantes: une unité de ressource est nécessaire pour faire fonctionner un service, chaque ressource doit retourner à son terminal d’origine, il existe un nombre fixe de ressources à chaque terminal, et la longueur du circuit des ressources est limitée. Nous considérons les alternatives suivantes dans la gestion des ressources: 1) repositionnement de ressources entre les terminaux pour tenir compte des changements de la demande, 2) achat et/ou location de nouvelles ressources et leur distribution à différents terminaux, 3) externalisation de certains services. Nous présentons une formulation intégrée combinant les décisions reliées à la gestion des ressources avec les décisions reliées à la conception des réseaux de services. Nous présentons également une méthode de résolution matheuristique combinant le slope-scaling et la génération de colonnes. Nous discutons des performances de cette méthode de résolution, et nous faisons une analyse de l’impact de différentes décisions de gestion des ressources dans le contexte de la conception de réseaux de services. Cette étude sera présentée au XII International Symposium On Locational Decision, en conjonction avec XXI Meeting of EURO Working Group on Locational Analysis, Naples/Capri (Italy), 2014. En résumé, trois études différentes sont considérées dans la présente thèse. La première porte sur une nouvelle méthode de solution pour le "capacitated multi-commodity fixed cost network design with design-balance constraints". Nous y proposons une matheuristique comprenant la recherche tabou, la recomposition de chemin, et l’optimisation exacte. Dans la deuxième étude, nous présentons un nouveau modèle de conception de réseaux de services prenant en compte un nombre fini de ressources à chaque terminal. Nous y proposons une matheuristique avancée basée sur la formulation en cycles comprenant le slope-scaling, la génération de colonnes, des heuristiques et l’optimisation exacte. Enfin, nous étudions l’allocation des ressources dans la conception de réseaux de services en introduisant des formulations qui modèlent le repositionnement, l’acquisition et la location de ressources, et l’externalisation de certains services. À cet égard, un cadre de solution slope-scaling développé à partir d’une formulation en cycles est proposé. Ce dernier comporte la génération de colonnes et une heuristique. Les méthodes proposées dans ces trois études ont montré leur capacité à trouver de bonnes solutions.

The Navier-Stokes Equations with Particle Methods

The non-stationary nonlinear Navier-Stokes equations describe the motion of a viscous incompressible fluid flow for 0

Approximate Gauss-Newton methods for nonlinear least squares problems

The Gauss–Newton algorithm is an iterative method regularly used for solving nonlinear least squares problems. It is particularly well suited to the treatment of very large scale variational data assimilation problems that arise in atmosphere and ocean forecasting. The procedure consists of a sequence of linear least squares approximations to the nonlinear problem, each of which is solved by an “inner” direct or iterative process. In comparison with Newton’s method and its variants, the algorithm is attractive because it does not require the evaluation of second-order derivatives in the Hessian of the objective function. In practice the exact Gauss–Newton method is too expensive to apply operationally in meteorological forecasting, and various approximations are made in order to reduce computational costs and to solve the problems in real time. Here we investigate the effects on the convergence of the Gauss–Newton method of two types of approximation used commonly in data assimilation. First, we examine “truncated” Gauss–Newton methods where the inner linear least squares problem is not solved exactly, and second, we examine “perturbed” Gauss–Newton methods where the true linearized inner problem is approximated by a simplified, or perturbed, linear least squares problem. We give conditions ensuring that the truncated and perturbed Gauss–Newton methods converge and also derive rates of convergence for the iterations. The results are illustrated by a simple numerical example. A practical application to the problem of data assimilation in a typical meteorological system is presented.