CONTROVERCY IN THE EVOLUTION OF THE WORLD AND EUROPEAN FINANCIAL REGULATION

The financial regulation structures are linked to traditions and differ from onecountry to another. The financial revolution, and in Europe, the creation of unique market ofthe financial services launched again debates over the suitability of the regulation structures. 5-10 years ago, the debates were quartered over the virtues, especially on thedisadvantages that the self-regulation system shows vs. the regulation practiced by the publicorganisms within the law provisions. The debate is based now on types of problems: the roleof the central bank and the problem of the specialization. Regarding the role of the central bank, there are arguments if the central bank is theproper authority to supervise banking and act in accordance with it, as a councilor, afterall. Regarding the specialization, there are arguments if a super or hiper authority,authorized to regulate all the types of services and financial institutions is more adequatethan the regulation by specialized agencies. At the level of the European Union, there is an extra problem if the nationalauthorities must keep the prerogative of financial services supervision, however inaccordance with the basic principle of the economic and monetary integration, or if, on thecontrary, it is better to form a European supervision authority for the financial services.globalization; financial regulation; financial supervision; financial markets efficiency,financial conglomerates.

Apprentissage statistique

National audienceLa complexification des données que les statisticiens sont amenés à analyser requiert le développement de nouvelles techniques et modèles. Dans la pratique, les données peuvent sortir du cadre euclidien ou la frontière entre le cadre supervisé et non supervisé peut devenir trop rigide quand les données sont labellisées de manière imprécise ou incertaine. Les méthodes traditionnelles d'apprentissage statistique sont alors insuffisantes ou incomplètes. Le but de cette session est de présenter de nouveaux résultats théoriques et des applications en apprentissage statistique et classification en passant en revue, dans l'ensemble des présentations, le cadre supervisé, non supervisé, mais aussi semi ou partiellement supervisé

A descriptive method to evaluate the number of regimes in a switching autoregressive model

International audienceThis paper proposes a descriptive method for an open problem in time-series analysis : determining the number of regimes in a switching autoregressive model. We will translate this problem into a classification one and define a criterion for hierarchically clustering different model fittings. Finally, the method will be tested on simulated examples and real-life data

Self-Organizing Maps for clustering and visualization of bipartite graphs

National audienceGraphs (also frequently called networks) have attracted a burst of attention in the last years, with applications to social science, biology, computer science... The present paper proposes a data mining method for visualizing and clustering the nodes of a peculiar class of graphs: bipartite graphs. The method is based on a self-organizing map algorithm and relies on an extension of this approach to data described by a dissimilarity matrix

A MATHEMATICAL MODEL FOR ASSESSING THE FACTORING ACTIVITY

Originally–being over 4,000 years old–factoring was first used in the fertile territory of old Mesopotamia at a time when the famous Code of Hammurabi was drawn up. However, many years passed until the British colonists started to use it on a large scale at a time when the metropolis would pay them sums of money for the merchandise that colonists sent to the old continent until they collected the invoices.In Romania factoring started to play a major role in financial operations for it led to the increase of liquidities on the market.According to the Romanian legislation, factoring is a contract concluded between a party known as “the client”, which supplies merchandise or provides services, and a banking institution or specialized financial institution known as “the factor”, whereby the latter ensures the financing source, collects the receivables and protects credit risks, while the client assigns to the factor the receivables resulting from the sale of goods or the provision of services to third parties

Asymptotic properties of autoregressive regime-switching models

International audienceThe statistical properties of the likelihood ratio test statistic (LRTS) for autoregressive regime-switching models are addressed in this paper. This question is particularly important for estimating the number of regimes in the model. Our purpose is to extend the existing results for mixtures (Liu and Shao, 2003) and hidden Markov chains (Gassiat, 2002). First, we study the case of mixtures of autoregressive models (i.e. independent regime switches). In this framework, we give sufficient conditions to keep the LRTS tight and compute its the asymptotic distribution. Second, we consider the extension of the ideas in Gassiat (2002) to autoregressive models with regimes switches according to a Markov chain. In this case, it is shown that the marginal likelihood is no longer a contrast function and cannot be used to select the number of regimes. Some numerical examples illustrate the results and their convergence properties

Utiliser SOMbrero pour la classification et la visualisation de graphes

International audienceGraphs have attracted a burst of attention in the last years, with applications to social science, biology, computer science... In the present paper, we illustrate how self-organizing maps (SOM) can be used to enlighten the structure of the graph, performing clustering of the graph together with visualization of a simplified graph. In particular, we present the R package SOMbrero which implements a stochastic version of the so-called relational algorithm: the method is able to process any dissimilarity data and several dissimilarities adapted to graphs are described and compared. The use of the package is illustrated on two real-world datasets: one, included in the package itself, is small enough to allow for a full investigation of the influence of the choice of a dissimilarity to measure the proximity between the vertices on the results. The other example comes from an application in biology and is based on a large bipartite graph of chemical reactions with several thousands vertices.L'analyse de graphes a connu un intérêt croissant dans les dernières années, avec des applications en sciences sociales, biologie, informatique, ... Dans cet article, nous illustrons comment les cartes auto-organisatrices (SOM) peuvent être utilisées pour mettre en lumière la structure d'un graphe en combinant la classification de ses sommets avec une visualisation simplifiée de celui-ci. En particulier, nous présentons le package R SOMbrero dans lequel est implémentée une version stochastique de l'approche dite « relationnelle » de l'algorithme de cartes auto-organisatrices. Cette méthode permet d'utiliser les cartes auto-organisatrices avec des données décrites par des mesures de dissimilarité et nous discutons et comparons ici plusieurs types de dissimilarités adaptées aux graphes. L'utilisation du package est illustrée sur deux jeux de données réelles : le premier, inclus dans le package lui-même, est suffisamment petit pour permettre l'analyse complète de l'influence du choix de la mesure de dissimilarité sur les résultats. Le second exemple provient d'une application en biologie et est basé sur un graphe biparti de grande taille, issu de réactions chimiques et qui contient plusieurs milliers de noeuds

Hidden Markov models for time series of counts with excess zeros

International audienceInteger-valued time series are often modeled with Markov models or hidden Markov models (HMM). However, when the series represents count data it is often subject to excess zeros. In this case, usual distributions such as binomial or Poisson are unable to estimate the zero mass correctly. In order to overcome this issue, we introduce zero-inflated distributions in the hidden Markov model. The empirical results on simulated and real data show good convergence properties, while excess zeros are better estimated than with classical HMM

Estimating the Number of Components in a Mixture of Multilayer Perceptrons

BIC criterion is widely used by the neural-network community for model selection tasks, although its convergence properties are not always theoretically established. In this paper we will focus on estimating the number of components in a mixture of multilayer perceptrons and proving the convergence of the BIC criterion in this frame. The penalized marginal-likelihood for mixture models and hidden Markov models introduced by Keribin (2000) and, respectively, Gassiat (2002) is extended to mixtures of multilayer perceptrons for which a penalized-likelihood criterion is proposed. We prove its convergence under some hypothesis which involve essentially the bracketing entropy of the generalized score-functions class and illustrate it by some numerical examples