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

Sermpinis, G. , Laws, J. and Dunis, C.L. (2014) Modelling commodity value at risk with Psi Sigma neural networks using open–high–low–close data. European Journal of Finance, 21(4), pp. 316-336. (doi: 10.1080/1351847X.2012.744763)

74500.pdf - Accepted Version



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 5% and 1% confidence levels, providing an accurate number of independent violations with small magnitude.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Sermpinis, Professor Georgios
Authors: Sermpinis, G., Laws, J., and Dunis, C.L.
Subjects:H Social Sciences > HG Finance
College/School:College of Social Sciences > Adam Smith Business School > Economics
Journal Name:European Journal of Finance
Publisher:Taylor & Francis
ISSN (Online):1466-4364
Published Online:22 January 2013
Copyright Holders:Copyright © 2014 Taylor and Francis
First Published:First published in European Journal of Finance 21(4):316-336
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher.

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