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

Charalambos Stasinakis, Georgios Sermpinis, Ioannis Psaradellis, Thanos Verousis

    Research output: Contribution to journalArticlepeer-review

    12 Citations (Scopus)

    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.
    Original languageEnglish
    JournalQuantitative Finance
    Volume16
    Issue number12
    Early online date14 Sept 2016
    DOIs
    Publication statusPublished - 2016

    Keywords

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

    Fingerprint

    Dive into the research topics of 'Krill-Herd support vector regression and heterogeneous autoregressive leverage: evidence from forecasting and trading commodities'. Together they form a unique fingerprint.

    Cite this