Avaliação de Riscos : Estimação Qualitativa da Probabilidade e da Gravidade em Contexto de Risco Elevado

O processo de gestão de risco consiste, no estudo estruturado de todos os aspetos inerentes ao trabalho e é composto pela análise de risco, avaliação de risco e controlo de risco. Na análise de risco, é efetuada a identificação de todos os perigos presentes e a estimação da probabilidade e da gravidade, de acordo com o método de avaliação de risco escolhido. Este estudo centra-se na primeira etapa do processo de avaliação de risco, mais especificamente na análise de risco e nos marcadores de informação necessários para se efetuar a estimação de risco na industria extrativa a céu aberto (atividade de risco elevado). Considerando que o nível de risco obtido, depende fundamentalmente da estimação da probabilidade e da gravidade, ajustada a cada situação de risco, procurou-se identificar os marcadores e compreender a sua influência nos resultados da avaliação de risco (magnitude). O plano de trabalhos de investigação foi sustentado por uma metodologia qualitativa de recolha, registo e análise dos dados. Neste estudo, a recolha de informação foi feita com recurso às seguintes técnicas de investigação: - Observação estruturada e planeada do desmonte da rocha com recurso a explosivos; - Entrevista individual de formadores e gestores de risco (amostragem de casos típicos); Na análise e discussão qualitativa dos dados das entrevistas recorreu-se às seguintes técnicas: - Triangulação de analistas e tratamento de dados cognitiva (técnicas complementares); - Aposição dos marcadores de informação, versus, três métodos de avaliação de risco validados. Os resultados obtidos apontam no sentido das hipóteses de investigação formuladas, ou seja, o tipo de risco influi da seleção da informação e, existem diferenças significativas no nível de risco obtido, quando na estimação da probabilidade e da gravidade são utilizados marcadores de informação distintos.

Evaluation of a predictive model for Mycosphaerella graminicola for economic and environmental benefits

A method was developed to evaluate crop disease predictive models for their economic and environmental benefits. Benefits were quantified as the value of a prediction measured by costs saved and fungicide dose saved. The value of prediction was defined as the net gain made by using predictions, measured as the difference between a scenario where predictions are available and used and a scenario without prediction. Comparable 'with' and 'without' scenarios were created with the use of risk levels. These risk levels were derived from a probability distribution fitted to observed disease severities. These distributions were used to calculate the probability that a certain disease induced economic loss was incurred. The method was exemplified by using it to evaluate a model developed for Mycosphaerella graminicola risk prediction. Based on the value of prediction, the tested model may have economic and environmental benefits to growers if used to guide treatment decisions on resistant cultivars. It is shown that the value of prediction measured by fungicide dose saved and costs saved is constant with the risk level. The model could also be used to evaluate similar crop disease predictive models.

The underpricing of IPOs on the stock exchange of Mauritius

This paper investigates the underpricing of IPOs on the Stock Exchange of Mauritius (SEM). Taking into account the whole population of firms which went public since the inception of the SEM until 2010, the results show an average degree of underpricing within the range 10 to 20%. Using a regression approach, we demonstrate that the aftermarket risk level and auditor's reputation both have a significant positive impact on initial returns. We propose the use of the Z-score as a composite measure of a firm's ex ante financial strength, and find that it has a significant negative effect on the degree of short-run underpricing.

The conditional performance of UK property funds

The evaluation of investment fund performance has been one of the main developments of modern portfolio theory. Most studies employ the technique developed by Jensen (1968) that compares a particular fund's returns to a benchmark portfolio of equal risk. However, the standard measures of fund manager performance are known to suffer from a number of problems in practice. In particular previous studies implicitly assume that the risk level of the portfolio is stationary through the evaluation period. That is unconditional measures of performance do not account for the fact that risk and expected returns may vary with the state of the economy. Therefore many of the problems encountered in previous performance studies reflect the inability of traditional measures to handle the dynamic behaviour of returns. As a consequence Ferson and Schadt (1996) suggest an approach to performance evaluation called conditional performance evaluation which is designed to address this problem. This paper utilises such a conditional measure of performance on a sample of 27 UK property funds, over the period 1987-1998. The results of which suggest that once the time varying nature of the funds beta is corrected for, by the addition of the market indicators, the average fund performance show an improvement over that of the traditional methods of analysis.

O Impacto da introdução de opções sobre a volatilidade do ativo objeto: um estudo sobre a função dos mercados derivativos

There is a lot of misunderstanding about derivative markets. Many people believe that they are a kind of casinos and have no utility to the investors. This work looks on the effects of options introduction in the Brazilian market, seeking for another benefit for this introduction: changes in the stocks risk level due to this introduction. Our results are the same found in the US and other markets: the options introduction reduces the stocks volatility. We also found that there is a slight indication that the volatility became more stochastic with this introducion.

Robust statistical modeling of portfolios

Atypical points in the data may result in meaningless e±cient frontiers. This follows since portfolios constructed using classical estimates may re°ect neither the usual nor the unusual days patterns. On the other hand, portfolios constructed using robust approaches are able to capture just the dynamics of the usual days, which constitute the majority of the business days. In this paper we propose an statistical model and a robust estimation procedure to obtain an e±cient frontier which would take into account the behavior of both the usual and most of the atypical days. We show, using real data and simulations, that portfolios constructed in this way require less frequent rebalancing, and may yield higher expected returns for any risk level.

Options listing and the volatility of the underlying asset: a study on the derivative market function

Os mercados de derivativos são vistos com muita desconfiança por inúmeras pessoas. O trabalho analisa o efeito da introdução de opções sobre ações no mercado brasileiro buscando identificar uma outra justificativa para a existência destes mercados: a alteração no nível de risco dos ativos objetos destas opções. A evidência empírica encontrada neste mercado está de acordo com os resultados obtidos em outros mercados - a introdução de opções é benéfica para o investidor posto que reduz a volatilidade do ativo objeto. Existe também uma tênue indicação de que a volatilidade se torna mais estocástica com a introdução das opções.

Sirex noctilio (Hymenoptera: Siricidae) e seus inimigos naturais em Pinus taeda e caracterização morfológica de Deladenus siricidicola

Pós-graduação em Agronomia (Entomologia Agrícola) - FCAV

SEQUENTIAL SAMPLING AND MICROSATELLITES FOR EVALUATION OF THE GENETIC QUALITY IN CORN SEEDS LOTS

In the seed production system, genetic purity is one of the fundamental requirements for its commercialization. The present work had the goal of determined the sample size for genetic purity evaluation, in order to protect the seed consumer and the producer and to evaluate the sensitivity of microsatellite technique for discriminating hybrids from their respective relatives and for detecting mixtures when they are present in small amounts in the samples. For the sequential sampling, hybrid seeds were marked and mixed in with the seed lots, simulating the following levels of contamination: 0.25, 0.5, 1.0, 2.0, 4.0, and 6.0%. After this, groups of 40 seeds were taken in sequence, up to a maximum of 400 seeds, with the objective of determining the quantity of seeds necessary to detect the percentage of mixture mentioned above. The sensitivity of microsatellite technique was evaluated by mixing different proportions of DNA from the hybrids with their respective seed lines. For the level of mixture was higher than 1:8 (1P1:8P2; 8P1:1P2), the sensitivity of the marker in detecting different proportions of the mixture varied according to the primer used. In terms of the sequential sampling, it was verified that in order to detect mixture levels higher than 1% within the seed lot- with a risk level for both the producer and the consumer of 0.05- the size of the necessary sample was smaller than the size needed for the fixed sample size. This also made it possible to reduce costs, making it possible to use microsatellites to certify the genetic purity of corn seeds lots.

Classificação e mapeamento de áreas de risco de enchentes e inundações no bairro Jardim Inocoop - Rio Claro - SP

In the last few decades, Brazil has experienced an accelerated urbanization process in which many cities have grown in a disorderly way occupying environmentally fragile areas unsuitable for habitation. Anthropogenic actions such as high levels of impermeable soil, structural changes in watercourses, lack of riparian vegetation, illegal presence of trash and rubbish along the river banks added to irregular settlements in floodplains result in the rise of high risk areas. When accompanied by intense and prolonged rainfall phenomena, those areas have been the scenery of serious accidents such as floods. This study aims to classify the level of the risk of floods in the neighborhood of Jardim Inocoop, in the town of Rio Claro, São Paulo countryside, Brazil. One of the main technical support to tackle this issue is the identification and classification of the risks. In order to classify the risk level of flood in this case study, the methodology adopted was developed by the Ministry of Cities and Technology Research Institute, and take into account the arrangement of the hydrological scenario, vulnerability of households and dangerous process according to the distance of the houses from the axis of drainage. Therefore, the risk levels adopted to classify are listed below: very high (MA), high risk (A), moderate (M) and low risk (B). In conclusion, it is imperative to develop prevention plans in order to avoid or to minimize the damages caused by natural disasters. Therefore, the zoning of the risk sceneries remains as an important issue once it helps to identify the areas with high level risk of flood. Consequently, the occupation must be regulated where there is low or absent risk and it must be often forbidden where the high risk of flood is detected. Thus, the present study remains as an attempt to notify the risk of floods through its spatialization on a map, remainig...(Complete abstract click electronic access below)

Multiple testing in spatial epidemiology: a Bayesian approach

In this work we aim to propose a new approach for preliminary epidemiological studies on Standardized Mortality Ratios (SMR) collected in many spatial regions. A preliminary study on SMRs aims to formulate hypotheses to be investigated via individual epidemiological studies that avoid bias carried on by aggregated analyses. Starting from collecting disease counts and calculating expected disease counts by means of reference population disease rates, in each area an SMR is derived as the MLE under the Poisson assumption on each observation. Such estimators have high standard errors in small areas, i.e. where the expected count is low either because of the low population underlying the area or the rarity of the disease under study. Disease mapping models and other techniques for screening disease rates among the map aiming to detect anomalies and possible high-risk areas have been proposed in literature according to the classic and the Bayesian paradigm. Our proposal is approaching this issue by a decision-oriented method, which focus on multiple testing control, without however leaving the preliminary study perspective that an analysis on SMR indicators is asked to. We implement the control of the FDR, a quantity largely used to address multiple comparisons problems in the eld of microarray data analysis but which is not usually employed in disease mapping. Controlling the FDR means providing an estimate of the FDR for a set of rejected null hypotheses. The small areas issue arises diculties in applying traditional methods for FDR estimation, that are usually based only on the p-values knowledge (Benjamini and Hochberg, 1995; Storey, 2003). Tests evaluated by a traditional p-value provide weak power in small areas, where the expected number of disease cases is small. Moreover tests cannot be assumed as independent when spatial correlation between SMRs is expected, neither they are identical distributed when population underlying the map is heterogeneous. The Bayesian paradigm oers a way to overcome the inappropriateness of p-values based methods. Another peculiarity of the present work is to propose a hierarchical full Bayesian model for FDR estimation in testing many null hypothesis of absence of risk.We will use concepts of Bayesian models for disease mapping, referring in particular to the Besag York and Mollié model (1991) often used in practice for its exible prior assumption on the risks distribution across regions. The borrowing of strength between prior and likelihood typical of a hierarchical Bayesian model takes the advantage of evaluating a singular test (i.e. a test in a singular area) by means of all observations in the map under study, rather than just by means of the singular observation. This allows to improve the power test in small areas and addressing more appropriately the spatial correlation issue that suggests that relative risks are closer in spatially contiguous regions. The proposed model aims to estimate the FDR by means of the MCMC estimated posterior probabilities b i's of the null hypothesis (absence of risk) for each area. An estimate of the expected FDR conditional on data (\FDR) can be calculated in any set of b i's relative to areas declared at high-risk (where thenull hypothesis is rejected) by averaging the b i's themselves. The\FDR can be used to provide an easy decision rule for selecting high-risk areas, i.e. selecting as many as possible areas such that the\FDR is non-lower than a prexed value; we call them\FDR based decision (or selection) rules. The sensitivity and specicity of such rule depend on the accuracy of the FDR estimate, the over-estimation of FDR causing a loss of power and the under-estimation of FDR producing a loss of specicity. Moreover, our model has the interesting feature of still being able to provide an estimate of relative risk values as in the Besag York and Mollié model (1991). A simulation study to evaluate the model performance in FDR estimation accuracy, sensitivity and specificity of the decision rule, and goodness of estimation of relative risks, was set up. We chose a real map from which we generated several spatial scenarios whose counts of disease vary according to the spatial correlation degree, the size areas, the number of areas where the null hypothesis is true and the risk level in the latter areas. In summarizing simulation results we will always consider the FDR estimation in sets constituted by all b i's selected lower than a threshold t. We will show graphs of the\FDR and the true FDR (known by simulation) plotted against a threshold t to assess the FDR estimation. Varying the threshold we can learn which FDR values can be accurately estimated by the practitioner willing to apply the model (by the closeness between\FDR and true FDR). By plotting the calculated sensitivity and specicity (both known by simulation) vs the\FDR we can check the sensitivity and specicity of the corresponding\FDR based decision rules. For investigating the over-smoothing level of relative risk estimates we will compare box-plots of such estimates in high-risk areas (known by simulation), obtained by both our model and the classic Besag York Mollié model. All the summary tools are worked out for all simulated scenarios (in total 54 scenarios). Results show that FDR is well estimated (in the worst case we get an overestimation, hence a conservative FDR control) in small areas, low risk levels and spatially correlated risks scenarios, that are our primary aims. In such scenarios we have good estimates of the FDR for all values less or equal than 0.10. The sensitivity of\FDR based decision rules is generally low but specicity is high. In such scenario the use of\FDR = 0:05 or\FDR = 0:10 based selection rule can be suggested. In cases where the number of true alternative hypotheses (number of true high-risk areas) is small, also FDR = 0:15 values are well estimated, and \FDR = 0:15 based decision rules gains power maintaining an high specicity. On the other hand, in non-small areas and non-small risk level scenarios the FDR is under-estimated unless for very small values of it (much lower than 0.05); this resulting in a loss of specicity of a\FDR = 0:05 based decision rule. In such scenario\FDR = 0:05 or, even worse,\FDR = 0:1 based decision rules cannot be suggested because the true FDR is actually much higher. As regards the relative risk estimation, our model achieves almost the same results of the classic Besag York Molliè model. For this reason, our model is interesting for its ability to perform both the estimation of relative risk values and the FDR control, except for non-small areas and large risk level scenarios. A case of study is nally presented to show how the method can be used in epidemiology.

Portfolio-optimization models for small investors

Since 2010, the client base of online-trading service providers has grown significantly. Such companies enable small investors to access the stock market at advantageous rates. Because small investors buy and sell stocks in moderate amounts, they should consider fixed transaction costs, integral transaction units, and dividends when selecting their portfolio. In this paper, we consider the small investor’s problem of investing capital in stocks in a way that maximizes the expected portfolio return and guarantees that the portfolio risk does not exceed a prescribed risk level. Portfolio-optimization models known from the literature are in general designed for institutional investors and do not consider the specific constraints of small investors. We therefore extend four well-known portfolio-optimization models to make them applicable for small investors. We consider one nonlinear model that uses variance as a risk measure and three linear models that use the mean absolute deviation from the portfolio return, the maximum loss, and the conditional value-at-risk as risk measures. We extend all models to consider piecewise-constant transaction costs, integral transaction units, and dividends. In an out-of-sample experiment based on Swiss stock-market data and the cost structure of the online-trading service provider Swissquote, we apply both the basic models and the extended models; the former represent the perspective of an institutional investor, and the latter the perspective of a small investor. The basic models compute portfolios that yield on average a slightly higher return than the portfolios computed with the extended models. However, all generated portfolios yield on average a higher return than the Swiss performance index. There are considerable differences between the four risk measures with respect to the mean realized portfolio return and the standard deviation of the realized portfolio return.

Portfolio-optimization models for small investors

Since 2010, the client base of online-trading service providers has grown significantly. Such companies enable small investors to access the stock market at advantageous rates. Because small investors buy and sell stocks in moderate amounts, they should consider fixed transaction costs, integral transaction units, and dividends when selecting their portfolio. In this paper, we consider the small investor’s problem of investing capital in stocks in a way that maximizes the expected portfolio return and guarantees that the portfolio risk does not exceed a prescribed risk level. Portfolio-optimization models known from the literature are in general designed for institutional investors and do not consider the specific constraints of small investors. We therefore extend four well-known portfolio-optimization models to make them applicable for small investors. We consider one nonlinear model that uses variance as a risk measure and three linear models that use the mean absolute deviation from the portfolio return, the maximum loss, and the conditional value-at-risk as risk measures. We extend all models to consider piecewise-constant transaction costs, integral transaction units, and dividends. In an out-of-sample experiment based on Swiss stock-market data and the cost structure of the online-trading service provider Swissquote, we apply both the basic models and the extended models; the former represent the perspective of an institutional investor, and the latter the perspective of a small investor. The basic models compute portfolios that yield on average a slightly higher return than the portfolios computed with the extended models. However, all generated portfolios yield on average a higher return than the Swiss performance index. There are considerable differences between the four risk measures with respect to the mean realized portfolio return and the standard deviation of the realized portfolio return.

Plan de manejo de la costa de Río Negro : observatorio del ecosistema litoral y monitoreo de la biodiversidad

La Provincia de Río Negro, a través de su Constitución y de leyes específicas, adhiere “a los principios que sustentan el desarrollo sustentable de conformidad con la Carta de Naciones Unidas." En la costa marítima de Río Negro existen poblaciones relativamente densas y en aumento básicamente por la inmigración en busca de nuevos horizontes laborales. Los conflictos ambientales que se presentan son comunes a otras zonas costeras: contaminación de las aguas costeras por insuficiente o falta de servicios de tratamiento de aguas residuales, modificación, para desarrollo urbano, industrial y comercial, de hábitats críticos para el sostenimiento de pesquerías, vida silvestre, desarrollo de infraestructura costera inapropiada o mal diseñada que favorece procesos erosivos acelerados y/o interrumpen procesos ecológicos básicos, ocupación espacial desordenada que impide el acceso público a playas y otros terrenos públicos, manejo inapropiado de desechos sólidos, incumplimiento de la legislación en la zona costera; generación de conflictos intersectoriales, desarrollo desordenado de infraestructura con altos costos socioeconómicos, crecimiento de la frontera agropecuaria, deterioro de los suelos (sobrepastoreo, desertificación), introducción de especies exóticas, etc. En Río Negro se han relevado, aunque a diferente escala y muchas veces en forma discontinua, la mayor parte de los ambientes costeros considerados de mayor interés desde el punto de vista ecológico y/o productivo. Ejemplo de productos de estos estudios son las Areas Naturales Protegidas de Punta Bermeja, Caleta de Loros, Bahía de San Antonio, Complejo Islote Lobos y Puerto Lobos, así como la Reserva Pesquera Golfo San Matías. Sin embargo, la información se halla dispersa, por lo que la mayor parte de las veces no se puede contar con un panorama actualizado y globalizador que permita la toma de decisión en forma ágil y un real manejo de las especies y/o de su ambiente. Por ello se considera necesario g enerar un Plan de Manejo de la Costa Marítima de Río Negro, esto es, una clasificación del territorio de acuerdo a su grado de sensibilidad ecológica, expresado en unidades cartográficas ambientales, y estableciendo pautas de manejo para las mismas. Se optó por un Sistema de Inventario y Planificación de Recursos, adaptado a un método de Planificación Participativa en el que se involucra en forma directa, a través de encuentros y talleres, a los diferentes estamentos provinciales, municipales, centros de investigación, organizaciones no gubernamentales ambientalistas, organizaciones intermedias, especialistas. Para la Evaluación de los Elementos e identificación de Zonas de Mayor Sensibilidad Ecológica se utilizó un Método de Evaluación de Riesgos que permite cartografiar grado de Amenazas, Vulnerabilidad y Riesgo.

Characterization of spontaneous combustuion tendency of dried sewage sludge

The general purpose of this study was the determination of the safety conditions to avoid the presence of explosive atmospheres in the wastewater industry. Eight Spanish plants located in Madrid, Barcelona and Málaga were considered and several sludge samples were taken in different seasons. The base for the assessment of the spontaneous ignition behaviour of dust accumulations is the experimental determination of the self-ignition temperature under isothermal conditions. Self-ignition temperatures at four volumes were obtained for one sample of sewage sludge, allowing their extrapolation to large storage facilities. A simple test method, based also on an isothermal study of samples, is the UN classification of substances liable to spontaneous combustion. Two different samples were so tested, obtaining unlike results if transported in packages of different volumes. By means of thermogravimetric techniques it is possible to analyse the thermal susceptibility of dried sewage sludge. Apparent activation energy can be obtained from the rate of weight loss. It is also applied to the study of self-ignition susceptibility by modifying test conditions when oxygen stream is introduced. As a consequence of this oxidant contribution, sample behaviour can be very different during testing and a step drop or sudden loss of weight is observed at a characteristic temperature for every substance, associated to a rapid combustion. Plotting both the activation energy and the characteristic temperature, a map of self-ignition risk was obtained for 10 samples, showing different risk levels for samples taken in different locations and at different seasons. A prediction of the self-ignition risk level can be also determined.