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Nonlinear Forecasting Of The Gold Miner Spread: An Application Of Correlation Filters

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  • Christian L. Dunis
  • Jason Laws
  • Peter W. Middleton
  • Andreas Karathanasopoulos

Abstract

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.

Suggested Citation

  • Christian L. Dunis & Jason Laws & Peter W. Middleton & Andreas Karathanasopoulos, 2013. "Nonlinear Forecasting Of The Gold Miner Spread: An Application Of Correlation Filters," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 20(4), pages 207-231, October.
    • Handle: RePEc:wly:isacfm:v:20:y:2013:i:4:p:207-231
    DOI: 10.1002/isaf.1345
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    References listed on IDEAS

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    1. C. L. Dunis & Jason Laws & Ben Evans, 2006. "Trading futures spreads: an application of correlation and threshold filters," Applied Financial Economics, Taylor & Francis Journals, vol. 16(12), pages 903-914.
    2. MacKinnon, James G, 1996. "Numerical Distribution Functions for Unit Root and Cointegration Tests," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(6), pages 601-618, Nov.-Dec..
    3. Johansen, Soren, 1988. "Statistical analysis of cointegration vectors," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 231-254.
    4. Jegadeesh, Narasimhan & Titman, Sheridan, 1993. "Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency," Journal of Finance, American Finance Association, vol. 48(1), pages 65-91, March.
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