Krill-Herd Support Vector Regression and heterogeneous autoregressive leverage: evidence from forecasting and trading commodities
Stasinakis, Charalampos Sermpinis, Georgios Psaradellis, Ioannis Verousis, Thanos
Stasinakis, Charalampos
Sermpinis, Georgios
Psaradellis, Ioannis
Verousis, Thanos
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Publication Type
Journal article with impact factor
Editor
Supervisor
Publication Year
2016
Journal
Quantitative Finance
Book
Publication Volume
16
Publication Issue
12
Publication Begin page
1901
Publication End page
1915
Publication NUmber of pages
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Abstract
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.
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Keywords
Krill Herd, Support Vector Regression, Commodities, ETF, Leverage