Rita Laura D'Ecclesia

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A new machine learning approach for price modeling is proposed. The use of neural networks as an advanced signal processing tool may be successfully used to model and forecast energy commodity prices, such as crude oil, coal, natural gas, and electricity prices. Energy commodities have shown explosive growth in the last decade. They have become a new asset(More)
The dynamics of commodity prices has become a major field of analysis in the last 20 years. Standard econometric procedures to describe the behavior of prices have not been able to provide accurate description of the real dynamics. In this paper we apply filter banks to predict prices of specific energy commodities: crude oil, natural gas and electricity,(More)
We develop optimization models to analyze the demand for financial assets by heterogeneous agents. The models extend Frankel’s (1985) earlier approach, and relax the assumption of normality of asset returns. We assume, instead, that investors maximize an expected utility of terminal wealth based on heterogeneous attitudes toward risk. Solving a bi-level(More)
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