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Krill-Herd support vector regression and heterogeneous autoregressive leverage: evidence from forecasting and trading commodities

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Charalambos Stasinakis, Georgios Sermpinis, Ioannis Psaradellis, Thanos Verousis

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In this study, a Krill-Herd Support Vector Regression (KH-vSVR) model is introduced. The Krill Herd (KH) algorithm is a novel metaheuristic optimization technique inspired by the behaviour of krill herds. The KH optimizes the SVR parameters by balancing the search between local and global optima. The proposed model is applied to the task of forecasting and trading three commodity exchange traded funds on a daily basis over the period 2012–2014. The inputs of the KH-vSVR models are selected through the model confidence set from a large pool of linear predictors. The KH-vSVR’s statistical and trading performance is benchmarked against traditionally adjusted SVR structures and the best linear predictor. In addition to a simple strategy, a time-varying leverage trading strategy is applied based on heterogeneous autoregressive volatility estimations. It is shown that the KH-vSVR outperforms its counterparts in terms of statistical accuracy and trading efficiency, while the leverage strategy is found to be successful.


Original languageEnglish
JournalQuantitative Finance
Issue number12
Early online date14 Sep 2016
Publication statusPublished - 2016

    Research areas

  • Krill Herd, Support vector regression, Commodities, ETF, Leverage

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