Opções reais em decisões de investimento de exploração e produção

Este trabalho faz uma revisão dos principais conceitos que definem a Teoria de Opções Reais. Tem como objetivo discutir o problema da decisão de investimento sob incerteza aplicado a problemas de Exploração e Produção de petróleo (E&P). Foram priorizados modelos simples que podem ser facilmente implantados no dia a dia de uma empresa, incluindo o clássico de Paddock, Siegel e Smith (1988). Os modelos discutidos são elaborados com Movimento Geométrico Browniano, que pode ser uma aproximação razoável para a modelagem de preços, a depender dos parâmetros considerados. Em particular, é apresentado um modelo de opção composta para exploração, que se revela mais apropriado por considerar o risco geológico e os estágios da opção com expiração diferenciada. A priorização de investimentos com auxílio de OR para uma carteira representativa de um portfolio de projetos de Produção também é testada, resultando numa maior relação VPL / Investimento da carteira selecionada.

Forecasting electricity demand using generalized long memory

This paper studies the electricity hourly load demand in the area covered by a utility situated in the southeast of Brazil. We propose a stochastic model which employs generalized long memory (by means of Gegenbauer processes) to model the seasonal behavior of the load. The model is proposed for sectional data, that is, each hour’s load is studied separately as a single series. This approach avoids modeling the intricate intra-day pattern (load profile) displayed by the load, which varies throughout days of the week and seasons. The forecasting performance of the model is compared with a SARIMA benchmark using the years of 1999 and 2000 as the out-of-sample. The model clearly outperforms the benchmark. We conclude for general long memory in the series.

An evolving possibilistic fuzzy modeling approach for value-at-risk estimation

Market risk exposure plays a key role for nancial institutions risk management. A possible measure for this exposure is to evaluate losses likely to incurwhen the price of the portfolio's assets declines using Value-at-Risk (VaR) estimates, one of the most prominent measure of nancial downside market risk. This paper suggests an evolving possibilistic fuzzy modeling approach for VaR estimation. The approach is based on an extension of the possibilistic fuzzy c-means clustering and functional fuzzy rule-based modeling, which employs memberships and typicalities to update clusters and creates new clusters based on a statistical control distance-based criteria. ePFM also uses an utility measure to evaluate the quality of the current cluster structure. Computational experiments consider data of the main global equity market indexes of United States, London, Germany, Spain and Brazil from January 2000 to December 2012 for VaR estimation using ePFM, traditional VaR benchmarks such as Historical Simulation, GARCH, EWMA, and Extreme Value Theory and state of the art evolving approaches. The results show that ePFM is a potential candidate for VaR modeling, with better performance than alternative approaches.