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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| Vydané v: | Intelligent systems in accounting, finance & management Ročník 20; číslo 4; s. 207 - 231 |
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| Hlavní autori: | , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Chichester
Blackwell Publishing Ltd
01.10.2013
Wiley Periodicals Inc |
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| ISSN: | 1550-1949, 2160-0074 |
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| Abstract | 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. |
|---|---|
| AbstractList | 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. 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. [PUBLICATION 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. |
| Author | Dunis, Christian L. Middleton, Peter W. Laws, Jason Karathanasopoulos, Andreas |
| Author_xml | – sequence: 1 givenname: Christian L. surname: Dunis fullname: Dunis, Christian L. organization: Horus Partners Wealth Management Group, Geneva, Switzerland and Emeritus Professor of Banking and Finance at Liverpool John Moores University, Hatton Garden, Liverpool, UK – sequence: 2 givenname: Jason surname: Laws fullname: Laws, Jason organization: CIBEF, University of Liverpool, Liverpool, UK – sequence: 3 givenname: Peter W. surname: Middleton fullname: Middleton, Peter W. email: peter.william.middleton@gmail.com organization: CIBEF, University of Liverpool, Liverpool, UK – sequence: 4 givenname: Andreas surname: Karathanasopoulos fullname: Karathanasopoulos, Andreas organization: London Metropolitan University, Holloway Road, London, UK |
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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... 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... |
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| SubjectTerms | 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 |
| Title | NONLINEAR FORECASTING OF THE GOLD MINER SPREAD: AN APPLICATION OF CORRELATION FILTERS |
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