Modelling and forecasting fossil fuels, CO2 and electricity prices and their volatilities

García-Martos, Carolina and Rodríguez, Julio and Sánchez Naranjo, María Jesús (2013). Modelling and forecasting fossil fuels, CO2 and electricity prices and their volatilities. "Applied Energy", v. 101 ; pp. 363-375. ISSN 0306-2619. https://doi.org/10.1016/j.apenergy.2012.03.046.

Description

Title: Modelling and forecasting fossil fuels, CO2 and electricity prices and their volatilities
Author/s:
  • García-Martos, Carolina
  • Rodríguez, Julio
  • Sánchez Naranjo, María Jesús
Item Type: Article
Título de Revista/Publicación: Applied Energy
Date: January 2013
Volume: 101
Subjects:
Freetext Keywords: Time series models; Forecasting; Unobserved components; Fossil fuels; Electricity; CO2 emission prices
Faculty: E.T.S.I. Industriales (UPM)
Department: Ingeniería de Organización, Administración de Empresas y Estadística
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

In the current uncertain context that affects both the world economy and the energy sector, with the rapid increase in the prices of oil and gas and the very unstable political situation that affects some of the largest raw materials’ producers, there is a need for developing efficient and powerful quantitative tools that allow to model and forecast fossil fuel prices, CO2 emission allowances prices as well as electricity prices. This will improve decision making for all the agents involved in energy issues. Although there are papers focused on modelling fossil fuel prices, CO2 prices and electricity prices, the literature is scarce on attempts to consider all of them together. This paper focuses on both building a multivariate model for the aforementioned prices and comparing its results with those of univariate ones, in terms of prediction accuracy (univariate and multivariate models are compared for a large span of days, all in the first 4 months in 2011) as well as extracting common features in the volatilities of the prices of all these relevant magnitudes. The common features in volatility are extracted by means of a conditionally heteroskedastic dynamic factor model which allows to solve the curse of dimensionality problem that commonly arises when estimating multivariate GARCH models. Additionally, the common volatility factors obtained are useful for improving the forecasting intervals and have a nice economical interpretation. Besides, the results obtained and methodology proposed can be useful as a starting point for risk management or portfolio optimization under uncertainty in the current context of energy markets.

More information

Item ID: 29395
DC Identifier: http://oa.upm.es/29395/
OAI Identifier: oai:oa.upm.es:29395
DOI: 10.1016/j.apenergy.2012.03.046
Official URL: http://www.sciencedirect.com/science/article/pii/S0306261912002668
Deposited by: Memoria Investigacion
Deposited on: 25 Nov 2014 15:20
Last Modified: 19 May 2017 15:31
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