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Return and Risk of Pairs Trading Using a Simulation-Based Bayesian Procedure for Predicting Stable Ratios of Stock Prices

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  • David Ardia

    (Institute of Financial Analysis, University of Neuchatel, Neuchatel, 2000, Switzerland
    Department of Finance, Insurance and Real Estate, Laval University, Quebec City, G1V 0A6, Canada)

  • Lukasz T. Gatarek

    (Institute of Econometrics and Statistics, Faculty of Economics and Sociology, University of Lodz, Lodz, 90-255, Poland)

  • Lennart Hoogerheide

    (Department of Econometrics and Tinbergen Institute, Vrije Universiteit Amsterdam, Amsterdam, 1081 HV, The Netherlands)

  • Herman K. Van Dijk

    (Department of Econometrics and Tinbergen Institute, Vrije Universiteit Amsterdam, Amsterdam, 1081 HV, The Netherlands
    Econometric Institute, Erasmus University Rotterdam, Rotterdam, 3062 PA, The Netherlands)

Abstract

We investigate the direct connection between the uncertainty related to estimated stable ratios of stock prices and risk and return of two pairs trading strategies: a conditional statistical arbitrage method and an implicit arbitrage one. A simulation-based Bayesian procedure is introduced for predicting stable stock price ratios, defined in a cointegration model. Using this class of models and the proposed inferential technique, we are able to connect estimation and model uncertainty with risk and return of stock trading. In terms of methodology, we show the effect that using an encompassing prior, which is shown to be equivalent to a Jeffreys’ prior, has under an orthogonal normalization for the selection of pairs of cointegrated stock prices and further, its effect for the estimation and prediction of the spread between cointegrated stock prices. We distinguish between models with a normal and Student t distribution since the latter typically provides a better description of daily changes of prices on financial markets. As an empirical application, stocks are used that are ingredients of the Dow Jones Composite Average index. The results show that normalization has little effect on the selection of pairs of cointegrated stocks on the basis of Bayes factors. However, the results stress the importance of the orthogonal normalization for the estimation and prediction of the spread—the deviation from the equilibrium relationship—which leads to better results in terms of profit per capital engagement and risk than using a standard linear normalization.

Suggested Citation

  • David Ardia & Lukasz T. Gatarek & Lennart Hoogerheide & Herman K. Van Dijk, 2016. "Return and Risk of Pairs Trading Using a Simulation-Based Bayesian Procedure for Predicting Stable Ratios of Stock Prices," Econometrics, MDPI, vol. 4(1), pages 1-19, March.
  • Handle: RePEc:gam:jecnmx:v:4:y:2016:i:1:p:14-:d:65426
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    Cited by:

    1. Javier Oliver-Muncharaz & Fernando García, 2020. "Leading research trends on trading strategies [Tendencias líderes de investigación sobre estrategias de trading]," Post-Print hal-03149330, HAL.
    2. Krauss, Christopher, 2015. "Statistical arbitrage pairs trading strategies: Review and outlook," FAU Discussion Papers in Economics 09/2015, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    3. Bruno Breyer Caldas & João Frois Caldeira & Guilherme Vale Moura, 2016. "Is Pairs Trading Performance Sensitive To The Methodologies?: A Comparison," Anais do XLII Encontro Nacional de Economia [Proceedings of the 42nd Brazilian Economics Meeting] 130, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
    4. Haican Diao & Guoshan Liu & Zhuangming Zhu, 2020. "Research on a stock-matching trading strategy based on bi-objective optimization," Frontiers of Business Research in China, Springer, vol. 14(1), pages 1-14, December.

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    More about this item

    Keywords

    Bayesian analysis; cointegration; linear normalization; orthogonal normalization; pairs trading; statistical arbitrage;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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