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

García-Martos, Carolina; Rodríguez, Julio y 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.

Descripción

Título: Modelling and forecasting fossil fuels, CO2 and electricity prices and their volatilities
Autor/es:
  • García-Martos, Carolina
  • Rodríguez, Julio
  • Sánchez Naranjo, María Jesús
Tipo de Documento: Artículo
Título de Revista/Publicación: Applied Energy
Fecha: Enero 2013
Volumen: 101
Materias:
Palabras Clave Informales: Time series models; Forecasting; Unobserved components; Fossil fuels; Electricity; CO2 emission prices
Escuela: E.T.S.I. Industriales (UPM)
Departamento: Ingeniería de Organización, Administración de Empresas y Estadística
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

Texto completo

[img]
Vista Previa
PDF (Document Portable Format) - Se necesita un visor de ficheros PDF, como GSview, Xpdf o Adobe Acrobat Reader
Descargar (4MB) | Vista Previa

Resumen

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.

Más información

ID de Registro: 29395
Identificador DC: http://oa.upm.es/29395/
Identificador OAI: oai:oa.upm.es:29395
Identificador DOI: 10.1016/j.apenergy.2012.03.046
URL Oficial: http://www.sciencedirect.com/science/article/pii/S0306261912002668
Depositado por: Memoria Investigacion
Depositado el: 25 Nov 2014 15:20
Ultima Modificación: 19 May 2017 15:31
  • Open Access
  • Open Access
  • Sherpa-Romeo
    Compruebe si la revista anglosajona en la que ha publicado un artículo permite también su publicación en abierto.
  • Dulcinea
    Compruebe si la revista española en la que ha publicado un artículo permite también su publicación en abierto.
  • Recolecta
  • e-ciencia
  • Observatorio I+D+i UPM
  • OpenCourseWare UPM