Trading and filtering futures spread portfolios: Further applications of threshold and correlation filters

Abstract

This study researches the topic of trading futures spreads, that is, trading the pricing differential between two futures contracts. We trade an equally weighted portfolio of three oil spreads using four trading models: the fair value cointegration, Generalised AutoRegressive Conditional Heteroskedastic, Moving Average Convergence Divergence and Neural Network Regression (NNR) models. The motivation of this research is twofold. First, the profitability of spread markets has been tested further than the traditional fair value model. Second, the correlation filter has been extended by investigating the effect of combining inputs from both a threshold filter and a correlation filter. The results indicate that the best model type for trading spreads is the NNR, with an out-of-sample annualised percentage return of 10.76 per cent and drawdown of -1.52 per cent, resulting in a leverage factor of 7.0964 in the case of the hybrid filter. Further, the results show the hybrid filter to be a sound development, proving to be the best out-of-sample filter on the occasion it is selected. This study extends the work of Dunis et al 2 in two ways. First, we test the same trading rule models on a portfolio of three spreads, thus testing the efficiency of more markets. By looking at the drawdown and standard deviation of these portfolios, we will also be able to draw some conclusions as to the diversifying effect of spread portfolios. Journal of DerivativesThe second way we have extended previous work is by the development of a new filtering technique. Dunis et al 2 look at threshold and correlation filters, and in this study this is developed further with the hybrid filter. This filter uses a combination of inputs from both a threshold and a correlation filter to further refine the trading rules that are used. The correlation filter is explained in the section entitled 'The correlation filter', and the hybrid filter is explained in the section entitled 'The hybrid filter'.The case for spread trading has been made by many academics, most of whom call on reduced margin requirements 3-6 or consistently tradable patterns 7-10 to encourage interest in spread trading. However, these same studies fail to explicitly explain that with reduced margin comes reduced potential reward. The reason for the reduced margin when trading a spread is the reduced chance of large moves. This is because the two legs of the spread are highly correlated over the long term and will therefore move in generally the same direction.Figures A1 and A2 show the PDF of the WTI and the Brent series differences, respectively. Figure A3 shows the PDF of the WTI-Brent spread differences. It is evident 11 that the average change of the spread is smaller than the average change for either underlying by a significant amount. This validates the decision to offer a lower margin for spreads. In contrast, the kurtosis of the spread PDF is extremely high(11.48), indicating that large moves of the spread are consistent features. Further, the maximum move of the spread...

Nonlinear Forecasting of the Gold Miner Spread: An Application of Correlation Filters

“…For instance, Dunis et al . (, ) also developed profitable trading models using rolling correlation filters.…”