Project Details
Description
The objective of this project, as part of the financial field, is to fundamentally study the dynamics of the prices of conventional and renewable energy assets through the prediction of their volatility and their returns.
For this, conventional econometric techniques and non-linear models known in the literature as Machine Learning models are used.
The foregoing is relevant within the literature as it contributes to the understanding of the operation of energy market prices and by presenting evidence of the performance of innovative forecasting techniques (Machine Learning) comparing them with traditional econometric techniques in the area of financial econometrics.
For this, conventional econometric techniques and non-linear models known in the literature as Machine Learning models are used.
The foregoing is relevant within the literature as it contributes to the understanding of the operation of energy market prices and by presenting evidence of the performance of innovative forecasting techniques (Machine Learning) comparing them with traditional econometric techniques in the area of financial econometrics.
Commitments / Obligations
2 scientific articles.
| Status | Finished |
|---|---|
| Effective start/end date | 6/1/21 → 11/20/23 |
UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):
Main Funding Source
- Installed Capacity (Academic Unit)
- Starter Funds
Location
- Bogotá D.C.
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