Long-term price range forecast applied to risk management using regression models

Long-term contractual decisions are the basis of an efficient risk management. However those types of decisions have to be supported with a robust price forecast methodology. This paper reports a different approach for long-term price forecast which tries to give answers to that need. Making use of regression models, the proposed methodology has as main objective to find the maximum and a minimum Market Clearing Price (MCP) for a specific programming period, and with a desired confidence level α. Due to the problem complexity, the meta-heuristic Particle Swarm Optimization (PSO) was used to find the best regression parameters and the results compared with the obtained by using a Genetic Algorithm (GA). To validate these models, results from realistic data are presented and discussed in detail.

A short-term risk management tool applied to OMEL electricity market using particle swarm optimization

Short-term risk management is highly dependent on long-term contractual decisions previously established; risk aversion factor of the agent and short-term price forecast accuracy. Trying to give answers to that problem, this paper provides a different approach for short-term risk management on electricity markets. Based on long-term contractual decisions and making use of a price range forecast method developed by the authors, the short-term risk management tool presented here has as main concern to find the optimal spot market strategies that a producer should have for a specific day in function of his risk aversion factor, with the objective to maximize the profits and simultaneously to practice the hedge against price market volatility. Due to the complexity of the optimization problem, the authors make use of Particle Swarm Optimization (PSO) to find the optimal solution. Results from realistic data, namely from OMEL electricity market, are presented and discussed in detail.

Multi-Objective and Financial Portfolio Optimization of Carrier-Sense Multiple Access Protocols with Cooperative Diversity

8th International Workshop on Multiple Access Communications (MACOM2015), Helsinki, Finland.

Inflammatory and Cardiometabolic Risk on Obesity: Role of Environmental Xenoestrogens

Context: Some chemicals used in consumer products or manufacturing (eg, plastics, pesticides) have estrogenic activities; these xenoestrogens (XEs) may affect immune responses and have recently emerged as a new risk factors for obesity and cardiovascular disease. However, the extent and impact on health of chronic exposure of the general population to XEs are still unknown. Objective: The objective of the study was to investigate the levels of XEs in plasma and adipose tissue (AT) depots in a sample of pre- and postmenopausal obese women undergoing bariatric surgery and their cardiometabolic impact in an obese state. Design and Participants: We evaluated XE levels in plasma and visceral and subcutaneous AT samples of Portuguese obese (body mass index ≥ 35 kg/m2) women undergoing bariatric surgery. Association with metabolic parameters and 10-year cardiovascular disease risk was assessed, according to menopausal status (73 pre- and 48 postmenopausal). Levels of XEs were determined by gas chromatography with electron-capture detection. Anthropometric and biochemical data were collected prior to surgery. Adipocyte size was determined on tissue sections obtained during surgery. Results: Our data show that XEs are pervasive in this obese population. Distribution of individual and concentration of total XEs differed between plasma, visceral AT, and subcutaneous AT, and the pattern of accumulation was different between pre- and postmenopausal women. Significant associations between XE levels and metabolic and inflammatory parameters were found. In premenopausal women, XEs in plasma seem to be a predictor of 10-year cardiovascular disease risk. Conclusions: Our findings point toward a different distribution of XE between plasma and AT in pre- and postmenopausal women, and reveal the association between XEs on the development of metabolic abnormalities in obese premenopausal women