Krill-Herd Support Vector Regression and heterogeneous autoregressive leverage: evidence from forecasting and trading commodities
Publication type
Journal article with impact factorPublication Year
2016Journal
Quantitative FinancePublication Volume
16Publication Issue
12Publication Begin page
1901Publication End page
1915
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Show full item recordAbstract
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.Knowledge Domain/Industry
Accounting & Financeae974a485f413a2113503eed53cd6c53
10.1080/14697688.2016.1211800