Risk Acceptance in the Furniture Sector: Analysis of Acceptance Level and Relevant Influence Factors

Risk acceptance has been broadly discussed in relation to hazardous risk activities and/or technologies. A better understanding of risk acceptance in occupational settings is also important; however, studies on this topic are scarce. It seems important to understand the level of risk that stakeholders consider sufficiently low, how stakeholders form their opinion about risk, and why they adopt a certain attitude toward risk. Accordingly, the aim of this study is to examine risk acceptance in regard to occupational accidents in furniture industries. The safety climate analysis was conducted through the application of the Safety Climate in Wood Industries questionnaire. Judgments about risk acceptance, trust, risk perception, benefit perception, emotions, and moral values were measured. Several models were tested to explain occupational risk acceptance. The results showed that the level of risk acceptance decreased as the risk level increased. High-risk and death scenarios were assessed as unacceptable. Risk perception, emotions, and trust had an important influence on risk acceptance. Safety climate was correlated with risk acceptance and other variables that influence risk acceptance. These results are important for the risk assessment process in terms of defining risk acceptance criteria and strategies to reduce risks.

A importância do relacionamento bancário no financiamento das PME

Dissertação de Mestrado em Finanças Empresariais

Modelo de avaliação de risco no sector bancário português

A globalização dos sistemas financeiros, ao longo dos anos, tem estimulado uma crescente necessidade de supervisão bancária nas instituições financeiras. O Comité de Supervisão Bancária de Basileia tem tido um papel crucial nesta área, estabelecendo princípios por via dos seus acordos entre as várias entidades nacionais de regulação e supervisão das maiores economias mundiais. Em 1988, foi criado o Acordo de Basileia (Basileia I) pelo Comité de Supervisão Bancária de forma a harmonizar os padrões de supervisão bancária. Este acordo estabeleceu mínimos de solvabilidade para o sistema bancário internacional no sentido de reforçar a sua solidez e estabilidade. Com o desenvolvimento de novas potências económicas e novas necessidades regulamentares, em Junho de 2004, foi publicado o novo Acordo de Capital – o Basileia II. Este acordo pretendia tornar os requisitos de capital mais sensíveis ao risco, promover a atuação das autoridades de supervisão e a disciplina de mercado (através do seu Pilar II) e encorajar a capacidade de cada instituição mensurar e gerir o seu risco. Em Setembro de 2010, o Acordo de Basileia III, com adoção prevista até 2019, veio reforçar estas medidas com a criação de um quadro regulamentar e de supervisão mais sólido, por parte das instituições de crédito. Surge, assim neste contexto, o Modelo de Avaliação de Risco (MAR) para o sector bancário. Em Portugal, o MAR tem como objetivo avaliar o perfil de risco das instituições de crédito, sujeitas à supervisão do Banco de Portugal, assim como apresentar o perfil de risco e a solidez da situação financeira de cada instituição de crédito. Este trabalho pretende avaliar o surgimento e a caracterização deste modelo e identificar as variáveis a ter em conta nos modelos de avaliação de risco a nível qualitativo e quantitativo.

A long-term risk management tool for electricity markets using swarm intelligence

This paper addresses the optimal involvement in derivatives electricity markets of a power producer to hedge against the pool price volatility. To achieve this aim, a swarm intelligence meta-heuristic optimization technique for long-term risk management tool is proposed. This tool investigates the long-term opportunities for risk hedging available for electric power producers through the use of contracts with physical (spot and forward contracts) and financial (options contracts) settlement. The producer risk preference is formulated as a utility function (U) expressing the trade-off between the expectation and the variance of the return. Variance of return and the expectation are based on a forecasted scenario interval determined by a long-term price range forecasting model. This model also makes use of particle swarm optimization (PSO) to find the best parameters allow to achieve better forecasting results. On the other hand, the price estimation depends on load forecasting. This work also presents a regressive long-term load forecast model that make use of PSO to find the best parameters as well as in price estimation. The PSO technique performance has been evaluated by comparison with a Genetic Algorithm (GA) based approach. A case study is presented and the results are discussed taking into account the real price and load historical data from mainland Spanish electricity market demonstrating the effectiveness of the methodology handling this type of problems. Finally, conclusions are dully drawn.

Age-period-cohort effects in the incidence of hip fractures: political and economic events are coincident with changes in risk

Introduction: Healthcare improvements have allowed prevention but have also increased life expectancy, resulting in more people being at risk. Our aim was to analyse the separate effects of age, period and cohort on incidence rates by sex in Portugal, 2000–2008. Methods: From the National Hospital Discharge Register, we selected admissions (aged ≥49 years) with hip fractures (ICD9-CM, codes 820.x) caused by low/moderate trauma (falls from standing height or less), readmissions and bone cancer cases. We calculated person-years at risk using population data from Statistics Portugal. To identify period and cohort effects for all ages, we used an age–period–cohort model (1-year intervals) followed by generalised additive models with a negative binomial distribution of the observed incidence rates of hip fractures. Results: There were 77,083 hospital admissions (77.4 % women). Incidence rates increased exponentially with age for both sexes (age effect). Incidence rates fell after 2004 for women and were random for men (period effect). There was a general cohort effect similar in both sexes; risk of hip fracture altered from an increasing trend for those born before 1930 to a decreasing trend following that year. Risk alterations (not statistically significant) coincident with major political and economic change in the history of Portugal were observed around birth cohorts 1920 (stable–increasing), 1940 (decreasing–increasing) and 1950 (increasing–decreasing only among women). Conclusions: Hip fracture risk was higher for those born during major economically/politically unstable periods. Although bone quality reflects lifetime exposure, conditions at birth may determine future risk for hip fractures.