Modelling commodity value at risk with Psi Sigma neural networks using open-high-low-close data

Created by W.Langdon from gp-bibliography.bib Revision:1.4202

  author =       "Georgios Sermpinis and Jason Laws and 
                 Christian L. Dunis",
  title =        "Modelling commodity value at risk with Psi Sigma
                 neural networks using open-high-low-close data",
  journal =      "The European Journal of Finance",
  year =         "2015",
  volume =       "21",
  number =       "4",
  pages =        "316--336",
  keywords =     "genetic algorithms, genetic programming, artificial
                 intelligence, forecasting, value at risk, extreme value
                 theory, loss function",
  ISSN =         "1351-847X",
  bibsource =    "OAI-PMH server at",
  oai =          "",
  URL =          "",
  DOI =          "doi:10.1080/1351847X.2012.744763",
  type =         "Peer Reviewed",
  size =         "21 pages",
  abstract =     "The motivation for this paper is to investigate the
                 use of a promising class of neural network models, Psi
                 Sigma (PSI), when applied to the task of forecasting
                 the one-day ahead value at risk (VaR) of the oil Brent
                 and gold bullion series using open--high--low--close
                 data. In order to benchmark our results, we also
                 consider VaR forecasts from two different neural
                 network designs, the multilayer perceptron and the
                 recurrent neural network, a genetic programming
                 algorithm, an extreme value theory model along with
                 some traditional techniques such as an ARMA-Glosten,
                 Jagannathan, and Runkle (1,1) model and the RiskMetrics
                 volatility. The forecasting performance of all models
                 for computing the VaR of the Brent oil and the gold
                 bullion is examined over the period September
                 2001--August 2010 using the last year and half of data
                 for out-of-sample testing. The evaluation of our models
                 is done by using a series of backtesting algorithms
                 such as the Christoffersen tests, the violation ratio
                 and our proposed loss function that considers not only
                 the number of violations but also their magnitude. Our
                 results show that the PSI outperforms all other models
                 in forecasting the VaR of gold and oil at both the
                 5percent and 1percent confidence levels, providing an
                 accurate number of independent violations with small
  notes =        "See also \cite{}

                 University of Glasgow Business School, University of
                 Glasgow, Gilbert Scott Building, Glasgow G12 8QQ, UK;
                 University of Liverpool Management School, The
                 University of Liverpool, Chatham Street, Liverpool L69
                 7ZH, UK; Horus Partners Wealth Management Group SA, 1
                 Rue de la Rotisserie, 1204 Geneve, Switzerland",

Genetic Programming entries for Georgios Sermpinis Jason Laws Christian L Dunis