This thesis examines the relationship between stock market returns and systematic risk factors in twelve industrial countries. Using the APT framework, the thesis investigates the notion of international stock market integration versus segmentation in terms of pricing risk, international stock market efficiency in terms of eliminating arbitrage opportunities across domestic markets, and the validity of the international version of the APT according to a model that specifies purely domestic factors. Starting with ordinary least squares estimation the thesis investigates the responses of investors in their national stock markets to systematic shocks. By employing iterative non-linear multivariate seemingly unrelated regression estimation, this work avoids the statistical problems encountered in the second-pass test of the two-stage procedure. This study found that the international stock market was neither integrated nor efficient and that the IAPT was not supported by the results during the period investigated. It was demonstrated that partial and regional integration, regional efficiency, and regional IAPT validity cannot be ruled out. Moreover, the alternative model proved to be practically valid.

This thesis examines the relationship between stock market returns and systematic risk factors in twelve industrial countries. Using the APT framework, the thesis investigates the notion of international stock market integration versus segmentation in terms of pricing risk, international stock market efficiency in terms of eliminating arbitrage opportunities across domestic markets, and the validity of the international version of the APT according to a model that specifies purely domestic factors. Starting with ordinary least squares estimation the thesis investigates the responses of investors in their national stock markets to systematic shocks. By employing iterative non-linear multivariate seemingly unrelated regression estimation, this work avoids the statistical problems encountered in the second-pass test of the two-stage procedure. This study found that the international stock market was neither integrated nor efficient and that the IAPT was not supported by the results during the period investigated. It was demonstrated that partial and regional integration, regional efficiency, and regional IAPT validity cannot be ruled out. Moreover, the alternative model proved to be practically valid. / United Arab Emirates University

Nous nous intéressons à estimer la moyenne d'une variable aléatoire de loi à queue lourde. Nous adoptons une approche plus robuste que la moyenne empirique classique communément utilisée. L'objectif est de développer des inégalités de concentration de type sous-gaussien sur l'erreur d'estimation. En d'autres termes, nous cherchons à garantir une forte concentration sous une hypothèse plus faible que la bornitude : une variance finie. Deux estimateurs de la moyenne pour une loi à support réel sont invoqués et leurs résultats de concentration sont rappelés. Plusieurs adaptations en dimension supérieure sont envisagées. L'utilisation appropriée de ces estimateurs nous permet d'introduire une nouvelle technique de minimisation du risque empirique pour des variables aléatoires à queue lourde. Quelques applications de cette technique sont développées. Nous appuyons ces résultats sur des simulations sur des jeux de données simulées. Dans un troisième temps, nous étudions un problème d'estimation multivarié dans le cadre des U-statistiques où les estimateurs précédents offrent, là aussi, une généralisation naturelle d'estimateurs présents dans la littérature. / In this thesis, we are interested in estimating the mean of heavy-tailed random variables. We focus on a robust estimation of the mean approach as an alternative to the classical empirical mean estimation. The goal is to develop sub-Gaussian concentration inequalities for the estimating error. In other words, we seek strong concentration results usually obtained for bounded random variables, in the context where the bounded condition is replaced by a finite variance condition. Two existing estimators of the mean of a real-valued random variable are invoked and their concentration results are recalled. Several new higher dimension adaptations are discussed. Using those estimators, we introduce a new version of empirical risk minimization for heavy-tailed random variables. Some applications are developed. These results are illustrated by simulations on artificial data samples. Lastly, we study the multivariate case in the U-statistics context. A natural generalization of existing estimators is offered, once again, by previous estimators.

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