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Krill-Herd Support Vector Regression and heterogeneous autoregressive leverage: evidence from forecasting and trading commodities.

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Název: Krill-Herd Support Vector Regression and heterogeneous autoregressive leverage: evidence from forecasting and trading commodities.
Autoři: Stasinakis, Charalampos, Sermpinis, Georgios, Psaradellis, Ioannis, Verousis, Thanos
Zdroj: Quantitative Finance; Dec2016, Vol. 16 Issue 12, p1901-1915, 15p
Témata: SUPPORT vector machines, METAHEURISTIC algorithms, COMMODITY exchanges, AUTOREGRESSIVE models, FINANCIAL leverage
Abstrakt: 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. [ABSTRACT FROM PUBLISHER]
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Databáze: Complementary Index
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Abstrakt: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. [ABSTRACT FROM PUBLISHER]
ISSN:14697688
DOI:10.1080/14697688.2016.1211800