NONLINEAR FORECASTING OF THE GOLD MINER SPREAD: AN APPLICATION OF CORRELATION FILTERS

SUMMARY This paper models and forecasts the Gold Miner Spread from 23 May 2006 to 30 June 2011. The Gold Miner Spread acts as a suitable performance indicator for the relationship between physical gold and US gold equity. The contribution of this investigation is twofold. First, the accuracy of each...

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Veröffentlicht in:Intelligent systems in accounting, finance & management Jg. 20; H. 4; S. 207 - 231
Hauptverfasser: Dunis, Christian L., Laws, Jason, Middleton, Peter W., Karathanasopoulos, Andreas
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Chichester Blackwell Publishing Ltd 01.10.2013
Wiley Periodicals Inc
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ISSN:1550-1949, 2160-0074
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Zusammenfassung:SUMMARY This paper models and forecasts the Gold Miner Spread from 23 May 2006 to 30 June 2011. The Gold Miner Spread acts as a suitable performance indicator for the relationship between physical gold and US gold equity. The contribution of this investigation is twofold. First, the accuracy of each model is evaluated from a statistical perspective. Second, various forecasting methodologies are then applied to trade the spread. Trading models include an ARMA (12,12) model, a cointegration model, a multilayer perceptron neural network (NN), a particle swarm optimization radial basis function NN and a genetic programming algorithm (GPA). Results obtained from an out‐of‐sample trading simulation validate the in‐sample back test as the GPA model produced the highest risk‐adjusted returns. Correlation filters are also applied to enhance performance and, as a consequence, volatility is reduced by 5%, on average, while returns are improved between 2.54% and 8.11% across five of the six models. Copyright © 2013 John Wiley & Sons, Ltd.
Bibliographie:ArticleID:ISAF1345
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ISSN:1550-1949
2160-0074
DOI:10.1002/isaf.1345