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Trading futures spread portfolios: applications of higher order and recurrent networks

  • Christian Dunis
  • Jason Laws
  • Ben Evans
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    This paper investigates the modelling and trading of oil futures spreads in the context of a portfolio of contracts. A portfolio of six spreads is constructed and each spread forecasted using a variety of modelling techniques, namely, a cointegration fair value model and three different types of neural network (NN), such as multi-layer perceptron (MLP), recurrent, and higher order NN models. In addition, a number of trading filters are employed to further improve the trading statistics of the models. Three different filters are optimized on an in-sample measure of down side risk-adjusted return, and these are then fixed out-of-sample. The filters employed are the threshold filter, correlation filter, and the transitive filter. The results show that the best in-sample model is the MLP with a transitive filter. This model is the best performer out-of-sample and also returns good out-of-sample statistics.

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    Article provided by Taylor & Francis Journals in its journal The European Journal of Finance.

    Volume (Year): 14 (2008)
    Issue (Month): 6 ()
    Pages: 503-521

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    Handle: RePEc:taf:eurjfi:v:14:y:2008:i:6:p:503-521
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