Modelling and trading the realised volatility of the FTSE100 futures with higher order neural networks

Georgios Sermpinis, Jason Laws and Christian L. Dunis

The European Journal of Finance, 2013, vol. 19, issue 3, 165-179

Abstract: The motivation for this article is the investigation of the use of a promising class of neural network (NN) models, higher order neural networks (HONNs), when applied to the task of forecasting and trading the 21-day-ahead realised volatility of the FTSE 100 futures index. This is done by benchmarking their results with those of two different NN designs, the multi-layer perceptron (MLP) and the recurrent neural network (RNN), along with a traditional technique, RiskMetrics. More specifically, the forecasting and trading performance of all models is examined over the eight FTSE 100 futures maturities of the period 2007--2008 using the realised volatility of the last 21 trading days of each maturity as the out-of-sample target. The statistical evaluation of our models is done by using a series of measures such as the mean absolute error, the mean absolute percentage error, the root-mean-squared error and the Theil U -statistic. Then we apply a simple trading strategy to exploit our forecasts based on trading at-the-money call options on FTSE 100 futures. As it turns out, HONNs demonstrate a remarkable performance and outperform all other models not only in terms of statistical accuracy but also in terms of trading efficiency. We also note that both the RNNs and MLPs provide sufficient results in the trading application in terms of cumulative profit and average profit per trade.

Date: 2013
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DOI: 10.1080/1351847X.2011.606990

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