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Statistical Arbitrage Trading Strategy in Commodity Futures Market with the Use of Nanoseconds Historical Data

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Book cover Information and Software Technologies (ICIST 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 756))

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Abstract

This paper confirms the existence of statistical arbitrage opportunities by employing the nanosecond historical data in high frequency trading (HFT). When considering the possible options, the Daniel Herlemont pairs trading strategy has been selected. In order pairs trading could operate, the pair selection algorithm had to be developed. Herlemont pairs trading strategy has not been tested before by using the nanosecond information and the proposed pair selection algorithm. The main objective of the given research is to test the pairs trading strategy in HFT by calculating the returnability in commodity futures market. The statistical arbitrage strategy attempts to achieve profit by exploiting price differences of the futures contracts. The strategy takes long/short positions when the spread between the prices widens with an expectation that the prices will converge in the future. In the given paper, the nanosecond historical data was provided by the Nanotick Company. The applied strategy has been subsequently tested with MatLab software.

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Acknowledgements

We would also like to show our gratitude to the NANOTICK for providing with high frequency data in nanoseconds of 5 futures commodity contracts.

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Authors and Affiliations

  1. Vilnius University, Kaunas Faculty, Muitines 8, 44280, Kaunas, Lithuania

    Mantas Vaitonis & Saulius Masteika

Authors
  1. Mantas Vaitonis
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  2. Saulius Masteika
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Correspondence to Mantas Vaitonis .

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Editors and Affiliations

  1. Kaunas University of Technology, Kaunas, Lithuania

    Robertas Damaševičius

  2. Kaunas University of Technology, Kaunas, Lithuania

    Vilma Mikašytė

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Vaitonis, M., Masteika, S. (2017). Statistical Arbitrage Trading Strategy in Commodity Futures Market with the Use of Nanoseconds Historical Data. In: Damaševičius, R., Mikašytė, V. (eds) Information and Software Technologies. ICIST 2017. Communications in Computer and Information Science, vol 756. Springer, Cham. https://doi.org/10.1007/978-3-319-67642-5_25

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  • DOI: https://doi.org/10.1007/978-3-319-67642-5_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67641-8

  • Online ISBN: 978-3-319-67642-5

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