Search results

The purpose of this paper is to identify, measure and optimise financial risk and its effect on returns from innovation projects on an accrual basis and on a cash basis in a commodity industry.

A hypothetical case study, based on a real case, of a petrochemical commodity industry in Brazil was analysed with commodities pricing rules based on actual contracts. Earnings at risk (EaR) and cash flow at risk (CFaR) measures were applied, as well as a metric proposed in this paper called cash balance at risk (CBaR).

The paper demonstrates that financial risk measurement and optimisation are important issues in the decision-making process in the petrochemical industry. EaR, CFaR and CBaR measures are helpful when used alongside standard procedures of project evaluation. The findings also show that innovative technologies, in certain conditions, may act as “natural hedging”. It was found that the time delay between revenues and expenses leads to financial risk exposure to changes in prices and foreign exchange rates. Projects can use financing and hedging to boost their results.

An innovative project was compared with an expansion project in a petrochemical industry. A model for petrochemical commodities contract pricing was added in an analysis that included financing and hedging. The findings in this paper suggest that it is important to consider financial risk measures in project evaluation.

O objetivo deste trabalho é identificar, medir e otimizar o risco financeiro e seus efeitos sobre os resultados de projetos com inovação, tanto na perspectiva do regime contábil quanto do regime de caixa, em uma indústria de commodities.

Um estudo de caso hipotético, baseado em um caso real de uma indústria petroquímica brasileira, foi analisado com regras de precificação de commodities baseados em contratos reais. As métricas Earnings at Risk (EaR) e Cash Flow at Risk (CFaR) foram utilizadas, assim como uma métrica proposta neste trabalho, denominada Cash Balance at Risk (CBaR).

Este artigo demonstrou que a mensuração e otimização do risco financeiro são questões importantes no processo de tomada de decisão em uma indústria petroquímica. As medidas EaR, CFaR e CBaR se apresentaram como contribuições ao processo padrão de avaliação de projetos. Os resultados também demonstraram que inovações tecnológicas, em certas condições, podem funcionar como um “hedge natural”. Foi verificado que descasamentos temporais entre recebimentos e despesas geram uma exposição financeira a oscilações em preços e em valores de moedas estrangeiras. Financiamento e hedge podem ser utilizados em conjunto para aprimorar resultados de projetos.

Um projeto com inovação foi comparado com um projeto de expansão em uma indústria petroquímica. Foi realizada uma analise de risco que agrega ao financiamento e ao hedge o uso de contratos de precificação de commodities. Os resultados desse projeto demonstram que é importante considerar medidas de risco financeiro nas avaliações de projetos.

The purpose of this paper is to evaluate the seasonal and spatial variations in the statistical descriptors of the Markov chain model as well as the expected values of the length of dry and wet days and to estimate the probability of dry and rainy sequences in the state of Santa Catarina.

Daily rainfall data from 1970 to 2013 of five rainfall stations in the state of Santa Catarina were used. To model the sequence of dry and wet days, the first order of the Markov chain was used. The statistical descriptors of the Markov model were evaluated, as well as the expected values of the length of dry and wet days and the number of dry and rainy days for each month. Along with geometric distribution, the probability of occurrence of sequences of dry and rainy days was determined. The adherence of the data to geometric distribution was evaluated using the Kolmogorov-Smirnov test.

The results showed that there is a seasonal and spatial variation in Markov model descriptors and also in the duration of the dry and rainy periods. These variations may be related to the mechanisms responsible for the formation and distribution of rainfall in the state, such as the air masses and relief. The Lages station, located in the Plateau of Santa Catarina, had the highest P₀₀ values, reflecting more stable conditions of the atmosphere. In autumn and winter, no marked differences were found between the coastal stations and west of the state. The geometric distribution was adequate for estimating the probability of dry and rainy days.

Although some work has already been carried out on the modeling of the Markov chain in the state of Santa Catarina, this study was found to be more complete with the use of various statistical descriptors of the model and its application in estimating the duration of the cycles of dry and wet periods and the number of rainy days in the period.