Research Repository

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

Stasinakis, Charalampos and Sermpinis, Georgios and Psaradellis, Ioannis and Verousis, Thanos (2016) 'Krill-Herd Support Vector Regression and heterogeneous autoregressive leverage: evidence from forecasting and trading commodities.' Quantitative Finance, 16 (12). 1901 - 1915. ISSN 1469-7688

[img]
Preview
Text
122457.pdf - Accepted Version

Download (725kB) | Preview

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.

Item Type: Article
Uncontrolled Keywords: Krill Herd, Support vector regression, Commodities, ETF, Leverage
Subjects: H Social Sciences > HG Finance
Divisions: Faculty of Social Sciences > Essex Business School
Faculty of Social Sciences > Essex Business School > Essex Finance Centre
Depositing User: Elements
Date Deposited: 11 Mar 2019 16:11
Last Modified: 11 Mar 2019 17:15
URI: http://repository.essex.ac.uk/id/eprint/24195

Actions (login required)

View Item View Item