The visual sense has outstanding importance for humans’ interaction with the environment and visual attention is the key mechanism that bundles our limited cognitive resources in order to enhance the perceptual processing of the most relevant environmental features at a certain point in time. Eye-Tracking technology enables us to accurately observe peoples’ eye movement behavior i.e. overt attention. In the last decade, overt attention on real-world scenes gained increasing popularity in vision research. The higher ecological validity of such scenes in combination with a free-viewing task allows us to investigate human viewing behavior under natural conditions. In this context, the majority of previous studies focused on the impact of basal image properties, such as color and luminance differences, to quantify the extent to which our fixation behavior is guided by these so-called low-level image properties. However, in most experimental studies complex images are observed only one time, whereas we are continually confronted with repeated visual impressions in everyday life. Therefore, I introduce a repeated-presentation-design that allows scrutinizing the impact of low-level image properties and the power of scene types over time. Besides these low-level factors, I also address inter-individual differences in motivation as well as emotional components as so-called high-level factors in overt attention. Previous research on visual attention has widely neglected these factors, especially in the context of real-world images. On the basis of novel study designs and by means of various analysis techniques, I show how several low- and high-level factors influence overt attention on complex scenes, how they interact, and how eye movement parameters are interrelated. In addition to that, I provide a comprehensive review of the previous literature on emotions’ and personality traits’ impacts on visual attention. On the basis of the inconsistent understanding of core concepts in the literature, I describe how behaviorally oriented studies investigate these high-level factors in visual attention, how the interplay between emotion and attention is conceptualized from a neuroscientific perspective, and I derive several conceptual and practical recommendations for future research. Finally, I outline some new ideas and venues for future research in the general discussion of the present work, for example how eye-tracking might overcome some fundamental problems of classical testing in psychological diagnostics, or how the view of embodied cognition can help us to get a better understanding of high- and low-level factors in visual attention.

近幾年針對資本結構的研究發現,在比較先進國家的企業確實有逐步調整回自己的最適資本結構的動作。本論文進一步將研究擴大到開發中國家，發現研究中的32個國家，不論是已開發或是發開中國家的企業，確實都有維持最適資本結構的動作。當資本結構偏離時，企業會逐步地調整回其最適值，然而每個國家調整回最適資本結構的調整速度則存在著差異性。本論文進一步利用國家間法律、會計、制度以及規範面的差異下去分析，發現國家發展程度以及會計制度是影響調整回最適資本結構的速度快慢的重要因子。此外，本論文亦探討融資順位理論及擇時理論的影響，發現加入融資順位理論因子後，調整速度會有相當程度的減緩，而減緩的幅度，則與國家發展程度、法律保護、公司稅率以及會計制度有顯著的關連性。 / Recent empirical literature provides evidences that firms in most developed countries do partially adjust toward their target capital structure. In this paper, we show that no only firms in developed countries, but also those in emerging countries gradually move back to their long-run equilibrium when they are away from it. But the adjustment speeds vary from country to country. We study the determinants of adjustment speeds around the world by focusing on differences in laws and regulations across countries. Our evidences show that firms in countries with common-law tradition, stronger shareholder right, or more completed accounting standards tend to move back to their optimal leverage quicker. Furthermore, we add two variables related to other two main capital structures (pecking order and market timing) in our analysis to capture their effects. Both theories add some information in explaining capital structure, but the impacts differ when applying different leverage measures. When we define leverage ratio as long-term debts dividend by net assets, we observe that pecking order factor lowers the adjustment speed a lot. And the magnitude of decrease on adjustment speed is significantly correlated with market condition, law enforcement, corporate tax rate and accounting standard. More developed countries and countries with stronger law enforcement, higher corporate tax rate, or more completed accounting standards tend to have less reduction on adjustment speed when including pecking order factor, because they have less information asymmetries.

In the first part of this thesis, we introduce a new methodology for stress-test exercises. Our approach allows to consider richer stress-test exercises, which assess the impact of a modification of the whole distribution of asset prices' factors, rather than focusing as the common practices on a single realization of these factors, and take into account the potential reaction to the shock of the portfolio manager. The second part of the thesis is devoted to the pricing of bonds with very long-term time-to-maturity (more than ten years). Modeling the volatility of very long-term rates is a challenge, due to the constraints put by no-arbitrage assumption. As a consequence, most of the no-arbitrage term structure models assume a constant limiting rate (of infinite maturity). The second chapter investigates the compatibility of the so-called "level" factor, whose variations have a uniform impact on the modeled yield curve, with the no-arbitrage assumptions. We introduce in the third chapter a new class of arbitrage-free term structure factor models, which allows the limiting rate to be stochastic, and present its empirical properties on a dataset of US T-Bonds.

La première partie de cette thèse introduit une nouvelle méthodologie pour la réalisation d’exercices de stress-tests. Notre approche permet de considérer des scénarios de stress beaucoup plus riches qu’en pratique, qui évaluent l’impact d’une modification de la distribution statistique des facteurs influençant les prix d’actifs, pas uniquement les conséquences d’une réalisation particulière de ces facteurs, et prennent en compte la réaction du gestionnaire de portefeuille au choc. La deuxième partie de la thèse est consacrée à la valorisation des obligations à maturité très longues (supérieure à 10 ans). La modélisation de la volatilité des taux de très long terme est un défi, notamment du fait des contraintes posées par l’absence d’opportunités d’arbitrage, et la plupart des modèles de taux d’intérêt en absence d’opportunités d’arbitrage impliquent un taux limite (de maturité infinie) constant. Le deuxième chapitre étudie la compatibilité du facteur "niveau", dont les variations ont un impact uniforme sur l’ensemble des taux modélisés, a fortiori les plus longs, avec l’absence d’opportunités d’arbitrage. Nous introduisons dans le troisième chapitre une nouvelle classe de modèle de taux d’intérêt, sans opportunités d’arbitrage, où le taux limite est stochastique, dont nous présentons les propriétés empiriques sur une base de données de prix d’obligations du Trésor américain. / In the first part of this thesis, we introduce a new methodology for stress-test exercises. Our approach allows to consider richer stress-test exercises, which assess the impact of a modification of the whole distribution of asset prices’ factors, rather than focusing as the common practices on a single realization of these factors, and take into account the potential reaction to the shock of the portfolio manager.
 The second part of the thesis is devoted to the pricing of bonds with very long-term time-to-maturity (more than ten years). Modeling the volatility of very long-term rates is a challenge, due to the constraints put by no-arbitrage assumption. As a consequence, most of the no-arbitrage term structure models assume a constant limiting rate (of infinite maturity). The second chapter investigates the compatibility of the so-called "level" factor, whose variations have a uniform impact on the modeled yield curve, with the no-arbitrage assumptions. We introduce in the third chapter a new class of arbitrage-free term structure factor models, which allows the limiting rate to be stochastic, and present its empirical properties on a dataset of US T-Bonds.