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)
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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