Celma de Oliveira Ribeiro

Guidelines to help practitioners of design of experiments

Purpose – The purpose of this paper is to focus the application of design of experiments (DOE) using industrial equipments, reinforcing idea that non‐statistical aspects in planning and conducting experiments are so important as formal design and analysis.Design/methodology/approach – Two case studies are presented to illustrate typical industrial applications and difficulties. Supported on these case studies and literature, this paper presents guidelines to planning, conducting and analysis involving technical and organizational aspects.Findings – Solving problems in industry, including in companies recognized as competent in the respective industrial sector, is not just a question of applying the right technique. Ceramic industry case study illustrates how important are non‐statistical issues in DOE application. Paint industry case study illustrates the strong relationship of the results with incorporating presented guidelines into practice. Moreover, both case studies consolidating a fundamental advant...

A Hybrid Commodity Price-Forecasting Model Applied to the Sugar-alcohol Sector

Accurate price forecasting for agricultural commodities can have significant decisionmaking implications for suppliers, especially those of biofuels, where the agriculture and energy sectors intersect. Environmental pressures and high oil prices affect demand for biofuels and have reignited the discussion about effects on food prices. Suppliers in the sugar–alcohol sector need to decide the ideal proportion of ethanol and sugar to optimise their financial strategy. Prices can be affected by exogenous factors, such as exchange rates and interest rates, as well as non-observable variables like the convenience yield, which is related to supply shortages. The literature generally uses two approaches: artificial neural networks (ANNs), which are recognised as being in the forefront of exogenous-variable analysis, and stochastic models such as the Kalman filter, which is able to account for non-observable variables. This article proposes a hybrid model for forecasting the prices of agricultural commodities that is built upon both approaches and is applied to forecast the price of sugar. The Kalman filter considers the structure of the stochastic process that describes the evolution of prices. Neural networks allow variables that can impact asset prices in an indirect, nonlinear way, what cannot be incorporated easily into traditional econometric models.

Uma contribuição ao problema de composição de carteiras de mínimo valor em risco

O trabalho propoe um modelo baseado em aproximacao estocastica para composicao de carteiras de ativos financeiros de minimo risco. A medida de risco estudada, o Valor em Risco, e bastante utilizada na pratica de gestao financeira como um sinalizador para tomada de decisao, porem poucas vezes e empregada para definir a composicao otima de carteiras em decorrencia das dificuldades de implementacao computacional. O modelo proposto permite que o problema de composicao de carteira de minimo Valor em Risco seja resolvido de uma maneira simples. O artigo analisa o desempenho do modelo em um problema de gestao de carteiras de acoes no mercado brasileiro.

Modelos determinísticos de gestão de ativo/passivo: uma aplicação no Brasil

This paper presents an application of Asset Liability Management (ALM) optimization models in Brazil. As opposed to traditional profit maximization models, which are subject to risk limitations, these models seek to optimize the riskreward relation.This paper aims to apply and adapt some existing asset/liability portfolio optimization models, presented in literature, to the Brazilian reality. Some concepts about Brazilian pension funds are discussed and the applicability of the models is analyzed on the basis of data from a Brazilian pension fund.

Classical-Equivalent Bayesian Portfolio Optimization for Electricity Generation Planning

There are several electricity generation technologies based on different sources such as wind, biomass, gas, coal, and so on. The consideration of the uncertainties associated with the future costs of such technologies is crucial for planning purposes. In the literature, the allocation of resources in the available technologies has been solved as a mean-variance optimization problem assuming knowledge of the expected values and the covariance matrix of the costs. However, in practice, they are not exactly known parameters. Consequently, the obtained optimal allocations from the mean-variance optimization are not robust to possible estimation errors of such parameters. Additionally, it is usual to have electricity generation technology specialists participating in the planning processes and, obviously, the consideration of useful prior information based on their previous experience is of utmost importance. The Bayesian models consider not only the uncertainty in the parameters, but also the prior information from the specialists. In this paper, we introduce the classical-equivalent Bayesian mean-variance optimization to solve the electricity generation planning problem using both improper and proper prior distributions for the parameters. In order to illustrate our approach, we present an application comparing the classical-equivalent Bayesian with the naive mean-variance optimal portfolios.

Shale Gas and the Replacement of Coal-Fired Power Plants

This article aims to analyze the role played by the shale gas supply in the substitution of coal-fired power plants by gas-fired systems in the USA and the possible implications for the Brazilian electricity generation sector. It is known that energetic resources availability affects the energy demand and its end-use. An example of this was the American shale gas boom, a process that peaked around 2005. By studying the economical and environmental contexts that guided this so called revolution, with oil and gas productions, prices, and changes in the numbers of thermal power plants and in the installed capacity by type, we were able to question the role of the natural gas as a driving force to effective changes towards a low carbon future and discuss potential benefits its adoption.

Portfolio optimization through Kriging methods

ABSTRACT This article presents a new methodology for optimizing financial asset portfolios. The proposed methodology, based on the Kriging method, allows for approximating the risk surface – and thus the optimal solution to the problem – in a generalized fashion, relaxing every restrictive hypothesis inherent to the available methods and with the ability to estimate the error in the risk surface approximation. Illustratively, the proposed methodology is applied to the portfolio problem with the Variance, VaR and CVaR as objective functions. The results are compared to those obtained using the Khun–Tucker technique, for the former, and the Rockafellar method, for the latter.

Hedging na produção de açúcar e álcool: uma integração de decisões financeiras e de produção

O risco financeiro ao qual o produtor agricola esta exposto no momento da comercializacao do produto final demanda o uso de instrumentos de reducao de risco, a fim de assegurar um preco que viabilize economicamente o processo produtivo. Este artigo analisa o problema de elaboracao de estrategias de protecao financeira na presenca de restricoes de producao, atraves de um modelo de otimizacao multiperiodo deterministico. A incerteza e descrita atraves de arvores de cenarios e o risco analisado atraves das abordagens classicas de media-variância. O comportamento do modelo proposto e analisado no caso do mercado sucroalcooleiro, para a gestao financeira de usinas sucroalcooleiras, sendo as estrategias de hedging construidas com base no mercado futuro de alcool e acucar.

Bayesian Portfolio Optimization for Electricity Generation Planning

Nowadays, there are several electricity generation technologies based on the different sources, such as wind, biomass, gas, coal, and so on. Considering the uncertainties associated with the future costs of such technologies is crucial for planning purposes. In the literature, the allocation of resources in the available technologies have been solved as a mean-variance optimization problem using the expected costs and the correspondent covariance matrix. However, in practice, the expected values and the covariance matrix of interest are not exactly known parameters. Consequently, the optimal allocations obtained from the mean-variance optimization are not robust to possible errors in the estimation of such parameters. Additionally, there are specialists in the electricity generation technologies participating in the planning process and, obviously, the consideration of useful prior information based on their previous experience is of utmost importance. The Bayesian models consider not only the uncertainty in the parameters, but also the prior information from the specialists. In this paper, we introduce the Bayesian mean-variance optimization to solve the electricity generation planning problem using both improper and proper prior distributions for the parameters. In order to illustrate our approach, we present an application comparing the Bayesian with the naive mean-variance optimal portfolios.

Positive externalities from the complementarity of wind and hydro power generation in Brazil

This article estimates how much the wind farms in operation contributed to the preservation of reservoir levels in the years 2013, 2014 and 2015 in Brazil, due to the complementarity between the wind and water. Additionally, this study evaluates the economic benefit of wind power to the electrical system. The results show that the generation profile of wind energy allows for better optimization of resources, reallocating orders between the regions of the country over the period.