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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Published in:Intelligent systems in accounting, finance & management Vol. 20; no. 4; pp. 207 - 231
Main Authors: Dunis, Christian L., Laws, Jason, Middleton, Peter W., Karathanasopoulos, Andreas
Format: Journal Article
Language:English
Published: Chichester Blackwell Publishing Ltd 01.10.2013
Wiley Periodicals Inc
Subjects:
correlation filter
Forecasting techniques
Genetic algorithms
genetic programming algorithm
Gold
Gold markets
multilayer perceptron neural network
Neural networks
particle swarm optimization
radial basis function neural network
Spread
spread trading
Stochastic models
Studies
United States > US
ISSN:1550-1949, 2160-0074
Online Access:Get full text
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Summary: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.
Bibliography:ArticleID:ISAF1345
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SourceType-Scholarly Journals-1
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content type line 14
ISSN:1550-1949
2160-0074
DOI:10.1002/isaf.1345