首页 | 本学科首页   官方微博 | 高级检索  
     检索      


NONLINEAR FORECASTING OF THE GOLD MINER SPREAD: AN APPLICATION OF CORRELATION FILTERS
Authors:Christian L Dunis  Jason Laws  Peter W Middleton  Andreas Karathanasopoulos
Institution:1. Horus Partners Wealth Management Group, Geneva, Switzerland and Emeritus Professor of Banking and Finance at Liverpool John Moores University, , Liverpool, UK;2. University of Liverpool, CIBEF, , Liverpool, UK;3. University of Liverpool Management School, University of Liverpool, , Liverpool, UK;4. London Metropolitan University, , London, UK
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.
Keywords:spread trading  multilayer perceptron neural network  particle swarm optimization  radial basis function neural network  genetic programming algorithm  correlation filter
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号