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:1.8120

@Article{Sermpinis:2013:EJF,

• 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 eprints.gla.ac.uk",
• oai = "oai:eprints.gla.ac.uk:74500",
• URL = "http://eprints.gla.ac.uk/74500/",
• 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 magnitude.",
• notes = "See also \cite{oai:eprints.gla.ac.uk:74500}

  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

Citations