What drives corporate default risk premia? Evidence from the CDS market

This paper studies the evolution of the default risk premia for European firms during the years surrounding the recent credit crisis. We employ the information embedded in Credit Default Swaps (CDS) and Moody’s KMV EDF default probabilities to analyze the common factors driving this risk premia. The risk premium is characterized in several directions: Firstly, we perform a panel data analysis to capture the relationship between CDS spreads and actual default probabilities. Secondly, we employ the intensity framework of Jarrow et al. (2005) in order to measure the theoretical effect of risk premium on expected bond returns. Thirdly, we carry out a dynamic panel data to identify the macroeconomic sources of risk premium. Finally, a vector autoregressive model analyzes which proportion of the co-movement is attributable to financial or macro variables. Our estimations report coefficients for risk premium substantially higher than previously referred for US firms and a time varying behavior. A dominant factor explains around 60% of the common movements in risk premia. Additionally, empirical evidence suggests a public-to-private risk transfer between the sovereign CDS spreads and corporate risk premia.

The role of an illiquidity factor in the Portuguese stock market

This study examines the role of illiquidity (proxied by the proportion of zero returns) as an additional risk factor in asset pricing. We use Portuguese monthly data, covering the period between January 1988 and December 2008. We compute an illiquidity factor using the Fama and French [Fama, E. F., and K. R. French (1993), "Common risk factors in the returns on stocks and bonds", Journal of Financial Economics, Vol. 33, Nº. 1, pp. 3-56] procedure and analyze the performance of CAPM, Fama-French three-factor model and illiquidity-augmented versions of these models in explaining both the time-series and the cross-section of returns. Our results reveal that the effect of characteristic liquidity is subsumed by the models considered, but the risk of illiquidity is not priced in the Portuguese stock market.

Adult learners in higher education

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Contributos para a implementação dum modelo de gestão da qualidade num serviço de tomografia computorizada: uma análise prospectiva

Mestrado em Intervenção Sócio-Organizacional na Saúde - Área de especialização: Políticas de Administração e Gestão de Serviços de Saúde.

El estrato socioeconómico como factor predictor del uso constante de condón en adolescentes

OBJETIVO: El estrato socioeconómico juega un rol importante en las desigualdades en salud. En México, la prevalencia más alta de casos de SIDA se encuentra en población de estratos más bajos. El propósito de lo estudio fue describir el estrato socioeconómico (ajustado por variables psicosociales, situacionales y demográficas) como un factor predictor del uso consistente del condón, en adolescentes. MÉTODOS: Se incluyó en el estudio una muestra de una encuesta previa aplicada a 1.410 adolescentes de 15 a 19 años y estratificada por edad, género y estrato socioeconómico de Guadalajara, México. El análisis fue aplicado sobre los 251 adolescentes que reportaron actividad sexual. El análisis estadístico se realizó mediante Ji Cuadrada, t-test, ANOVA y regresión logística. RESULTADOS: La frecuencia de uso consistente de condón fue 30,7% y hubo una prevalencia de uso irregular. El estrato socioeconómico alto fue el principal predictor (OR= 11,1, CI95%= 2,6-47,6). Otros predictores significativos fueron el género masculino, el soporte de los pares y el nivel alto de conocimientos sobre VIH/SIDA. CONCLUSIÓN: El estrato socioeconómico es un importante factor predictor del uso consistente del condón.

Quantificação de fluxo do líquido cefalorraquidiano (LCR) por ressonância magnética

Mestrado em Radiações Aplicadas às Tecnologias da Saúde. Especialização: Ressonância Magnética.

Ankle dynamic in stroke patients: agonist vs. antagonist muscle relations

O documento em anexo encontra-se na versão post-print (versão corrigida pelo editor).

Dynamic artificial neural network for electricity market prices forecast

This paper presents an artificial neural network applied to the forecasting of electricity market prices, with the special feature of being dynamic. The dynamism is verified at two different levels. The first level is characterized as a re-training of the network in every iteration, so that the artificial neural network can able to consider the most recent data at all times, and constantly adapt itself to the most recent happenings. The second level considers the adaptation of the neural network’s execution time depending on the circumstances of its use. The execution time adaptation is performed through the automatic adjustment of the amount of data considered for training the network. This is an advantageous and indispensable feature for this neural network’s integration in ALBidS (Adaptive Learning strategic Bidding System), a multi-agent system that has the purpose of providing decision support to the market negotiating players of MASCEM (Multi-Agent Simulator of Competitive Electricity Markets).

Self-optimization for dynamic scheduling in manufacturing systems

Scheduling is a critical function that is present throughout many industries and applications. A great need exists for developing scheduling approaches that can be applied to a number of different scheduling problems with significant impact on performance of business organizations. A challenge is emerging in the design of scheduling support systems for manufacturing environments where dynamic adaptation and optimization become increasingly important. In this paper, we describe a Self-Optimizing Mechanism for Scheduling System through Nature Inspired Optimization Techniques (NIT).

A hybrid intelligent system for distributed dynamic scheduling

This chapter addresses the resolution of dynamic scheduling by means of meta-heuristic and multi-agent systems. Scheduling is an important aspect of automation in manufacturing systems. Several contributions have been proposed, but the problem is far from being solved satisfactorily, especially if scheduling concerns real world applications. The proposed multi-agent scheduling system assumes the existence of several resource agents (which are decision-making entities based on meta-heuristics) distributed inside the manufacturing system that interact with other agents in order to obtain optimal or near-optimal global performances.

MASDSheGATS – Scheduling System for Dynamic Manufacturing Environments

This chapter addresses the resolution of scheduling in manufacturing systems subject to perturbations. The planning of Manufacturing Systems involves frequently the resolution of a huge amount and variety of combinatorial optimisation problems with an important impact on the performance of manufacturing organisations. Examples of those problems are the sequencing and scheduling problems in manufacturing management, routing and transportation, layout design and timetabling problems.

A study on dynamic state information (DSI) around users for safe urban life

To select each node by devices and by contexts in urban computing, users have to put their plan information and their requests into a computing environment (ex. PDA, Smart Devices, Laptops, etc.) in advance and they will try to keep the optimized states between users and the computing environment. However, because of bad contexts, users may get the wrong decision, so, one of the users’ demands may be requesting the good server which has higher security. To take this issue, we define the structure of Dynamic State Information (DSI) which takes a process about security including the relevant factors in sending/receiving contexts, which select the best during user movement with server quality and security states from DSI. Finally, whenever some information changes, users and devices get the notices including security factors, then an automatic reaction can be possible; therefore all users can safely use all devices in urban computing.

MASDScheGATS: a prototype system for dynamic scheduling

A manufacturing system has a natural dynamic nature observed through several kinds of random occurrences and perturbations on working conditions and requirements over time. For this kind of environment it is important the ability to efficient and effectively adapt, on a continuous basis, existing schedules according to the referred disturbances, keeping performance levels. The application of Meta-Heuristics and Multi-Agent Systems to the resolution of this class of real world scheduling problems seems really promising. This paper presents a prototype for MASDScheGATS (Multi-Agent System for Distributed Manufacturing Scheduling with Genetic Algorithms and Tabu Search).

Multi-agent electricity market simulation with dynamic strategies & virtual power producers

Distributed energy resources will provide a significant amount of the electricity generation and will be a normal profitable business. In the new decentralized grid, customers will be among the many decentralized players and may even help to co-produce the required energy services such as demand-side management and load shedding. So, they will gain the opportunity to be more active market players. The aggregation of DG plants gives place to a new concept: the Virtual Power Producer (VPP). VPPs can reinforce the importance of these generation technologies making them valuable in electricity markets. In this paper we propose the improvement of MASCEM, a multi-agent simulation tool to study negotiations in electricity spot markets based on different market mechanisms and behavior strategies, in order to take account of decentralized players such as VPP.