Pairs trading: optimal thresholds and profitability

Zhengqin, Zeng; Lee, Chi-Guhn

doi:10.1080/14697688.2014.917806

Cited by 43 publications

(53 citation statements)

References 12 publications

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“…In the literature, most pairs trading studies use daily data and a daily trading frequency (Gatev et al 2006, Lin et al 2006, Avellaneda and Lee 2010, Cummins and Bucca 2012, Bogomolov 2013, Zeng and Lee 2014. Bowen et al (2010), which use 60 minute return series, is one of the very few that uses data with frequency higher than daily.…”

Section: Sb(t) Sb(0)mentioning

confidence: 99%

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

Liu

Chang

Geman

2016

Quantitative Finance

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This paper introduces novel 'doubly mean-reverting' processes based on conditional modeling to 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.

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Section: Sb(t) Sb(0)mentioning

confidence: 99%

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

Liu

Chang

Geman

2016

Quantitative Finance

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“…Kim (2011) provides an empirical implementation in a high-frequency setting in the Korean stock market. Zeng and Lee (2014) develop an extension of Bertram (2010). 11.…”

Section: Pairs Trading In the Light Of Market Frictionsmentioning

confidence: 99%

Statistical Arbitrage Pairs Trading Strategies: Review and Outlook

Krauß

2016

Journal of Economic Surveys

163

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. This survey reviews the growing literature on pairs trading frameworks, i.e., relative-value arbitrage strategies involving two or more securities. The available research is categorized into five groups: The distance approach uses nonparametric distance metrics to identify pairs trading opportunities. The cointegration approach relies on formal cointegration testing to unveil stationary spread time series. The time series approach focuses on finding optimal trading rules for mean-reverting spreads. The stochastic control approach aims at identifying optimal portfolio holdings in the legs of a pairs trade relative to other available securities. The category "other approaches" contains further relevant pairs trading frameworks with only a limited set of supporting literature. Drawing from this large set of research consisting of more than 90 papers, an in-depth assessment of each approach is performed, ultimately revealing strengths and weaknesses relevant for further research and for implementation. Terms of use: Documents in

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“…One can ignore the term r f t and the constant term

prefix- l n ((), S_{0}^{A}) + γ l n ((), S_{0}^{B})

. Therefore, in empirical applications, we simply use

X_{t} = l n ((), S_{t}^{A}) - γ l n ((), S_{t}^{B})

for generating the buy and sell signals (e.g., Zeng and Lee ()).Remark Note that if one of the stocks is expected to outperform the other stock significantly, which implies an increasing spread, then this situation can be considered in the framework of statistical arbitrage discussed in Göncü ().…”

Section: Spread Modelmentioning

confidence: 99%

“…In our empirical examples, we consider the adjusted closing prices of the same pairs of stocks as given in Zeng and Lee (), and these are, namely, Coca‐Cola (KO)/Pepsi (PEP), Target (TGT)/Walmart (WMT), Dell (DELL)/Hewlet‐Packard (HPQ), RWE AG (RWE.DE)/E.OnSe (EOAN.DE). Compared with Zeng and Lee (), we utilize a larger dataset that consists of 2000 trading days for the period of January 2007 to March 2014.…”

Section: Empirical Implementationmentioning

confidence: 99%

“…In this study, we utilize a mean‐reverting stochastic process to model the spread of two stocks, where the examples of this approach can be found in Zeng and Lee (), Elliot et al (), Cummins and Bucca (), and Bertram (). In Bertram (), optimal buy/sell thresholds are derived by maximizing the expected return and Sharpe ratio of a synthetic asset that follows a mean‐reverting Ornstein–Uhlenbeck (OU) process, whereas in Zeng and Lee (), optimal thresholds for pairs trading are derived by maximizing the expected profits per unit of time. In this study, optimal thresholds are derived from the maximization of the probability of successful termination of the pairs trading strategy.…”

Section: Introductionmentioning

confidence: 99%

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Statistical Arbitrage with Pairs Trading

Göncü

Akyıldırım

2016

Int Rev Finance

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We analyze statistical arbitrage with pairs trading assuming that the spread of two assets follows a mean‐reverting Ornstein–Uhlenbeck process around a long‐term equilibrium level. Within this framework, we prove the existence of statistical arbitrage and derive optimality conditions for trading the spread portfolio. In the existence of uncertainty in the long‐term mean and the volatility of the spread, statistical arbitrage is no longer guaranteed. However, the asymptotic probability of loss can be bounded as a function of the standard error of the model parameters. The proposed framework provides a new filtering technique for identifying best pairs in the market. Backtesting results are given for some of the pairs of stocks that are studied in the literature.

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Pairs trading: optimal thresholds and profitability

Cited by 43 publications

References 12 publications

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

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

Statistical Arbitrage Pairs Trading Strategies: Review and Outlook

Statistical Arbitrage with Pairs Trading

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