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    Intraday pair trading strategies on high frequency data: the case of oil companies

    Geman, Hélyette and Chang, L. and Liu, B. (2016) Intraday pair trading strategies on high frequency data: the case of oil companies. Quantitative Finance 17 (1), pp. 87-100. ISSN 1469-7688.

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    This paper introduces novel ‘doubly mean-reverting’ processes based on conditional modelling of model spreads between pairs of stocks. Intraday trading strategies using high frequency data are proposed based on the model. This model framework and the strategies are designed to capture ‘local’ market inefficiencies that are elusive for traditional pairs trading strategies with daily data. Results from real data back-testing for two periods show remarkable returns, even accounting for transaction costs, with annualized Sharpe ratios of 3.9 and 7.2 over the periods June 2013–April 2015 and 2008, respectively. By choosing the particular sector of oil companies, we also confirm the observation that the commodity price is the main driver of the share prices of commodity-producing companies at times of spikes in the related commodity market.


    Item Type: Article
    Additional Information: This is an Accepted Manuscript of an article published by Taylor & Francis, available online at the link above.
    Keyword(s) / Subject(s): Pairs trading, Quantitative trading strategies, Conditional modelling, Doubly mean-reverting model, High frequency data, Transaction costs
    School: School of Business, Economics & Informatics > Economics, Mathematics and Statistics
    Research Centres and Institutes: Commodities Finance Centre
    Depositing User: Helyette Geman
    Date Deposited: 07 Jul 2016 14:50
    Last Modified: 09 Jun 2021 18:29


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