The Profitability of Pairs Trading Strategies: Distance, Cointegration, and Copula Methods

Rad, Hossein; Low, Rand Kwong Yew; Faff, Robert W.

doi:10.2139/ssrn.2614233

Cited by 34 publications

(76 citation statements)

References 52 publications

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“…Key representatives are Liew and Wu (2013); Xie and Wu (2013); Stander et al (2013); Xie et al (2014); Krauss and Stübinger (2015); Rad et al (2016). These studies use bivariate copulas to model the dependence structure between two stock return time series, and to identify mispricings that can potentially be exploited in a pairs trading application.…”

Section: Introductionmentioning

confidence: 99%

Statistical arbitrage with vine copulas

Stübinger

Mangold

Krauß

2018

Quantitative Finance

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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. Terms of use: Documents in AbstractWe develop a multivariate statistical arbitrage strategy based on vine copulas -a highly flexible instrument for linear and nonlinear multivariate dependence modeling. In an empirical application on the S&P 500, we find statistically and economically significant returns of 9.25 percent p.a. and a Sharpe ratio of 1.12 after transaction costs for the period from 1992 until 2015. Tail risk is limited, with maximum drawdown at 6.57 percent. The high returns can only partially be explained by common sources of systematic risk. We benchmark the vine copula strategy against other variants relying on the multivariate Gaussian and t-distribution and we find its results to be superior in terms of risk and return characteristics. The multivariate dependence structure of the vine copulas is time-varying, and we see that the share of copulas capable of modeling upper and lower tail dependence increases well over 90 percent at times of high market turmoil.

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Section: Introductionmentioning

confidence: 99%

Statistical arbitrage with vine copulas

Stübinger

Mangold

Krauß

2018

Quantitative Finance

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“…For the GGR approach, we opt for a 12-month formation, and a 6-month trading period (Gatev et al, 2006). For CI1, we follow Huck (2015) as well as Rad et al (2015) and use the same durations as GGR. In case of the CI2 and the PCI approach, we lengthen the formation period to 48 months (approximately 1000 days) in order to increase the power of the tests for cointegration and partial cointegration -see tables 5 and 6 as well as Clegg (2014); .…”

Section: Datamentioning

confidence: 99%

“…These tests are recommended in Huck (2015) and Rad et al (2015). Caldeira and Moura (2013) use this exact combination.…”

Section: Formation Periodmentioning

confidence: 99%

“…Recently, Krauss (2015) has surveyed the literature, cataloging more than 90 papers in five different categories -among them the cointegration approach. Representative studies are by Caldeira and Moura (2013);Huck (2015); Rad et al (2015). These applications identify cointegrated pairs in a formation period and trade the cointegrating process in a subsequent trading period.…”

Section: Introductionmentioning

confidence: 99%

“…The results are convincing. For example, Rad et al (2015), the most comprehensive study, report annualized excess returns of approximately 10 percent from 1962 until 2014 on U.S. CRSP data, prior to transaction costs.…”

Section: Introductionmentioning

confidence: 99%

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Pairs trading with partial cointegration

Clegg

Krauß

2017

Quantitative Finance

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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. Terms of use: Documents in AbstractPartial cointegration is a weakening of cointegration that allows for the "cointegrating" process to contain a random walk and a mean-reverting component. We derive its representation in state space, provide a maximum likelihood based estimation routine, and a suitable likelihood ratio test. Then, we explore the use of partial cointegration as a means for identifying promising pairs and for generating buy and sell signals. Specifically, we benchmark partial cointegration against several classical pairs trading variants from 1990 until 2015, on a survivor bias free data set of the S&P 500 constituents. We find annualized returns of more than 12 percent after transaction costs. These results can only partially be explained by common sources of systematic risk and are well superior to classical distance-based or cointegration-based pairs trading variants on our data set.

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Pairs trading across Mainland China and Hong Kong stock markets

Zhang

Urquhart

2018

Int. J Fin Econ

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Motivated by the rationale that market inefficiency arises from a combination of less than fully rational demand and limits to arbitrage, this paper investigates the profitability of pairs trading across Mainland China and Hong Kong on highly liquid large‐cap and midcap stocks from January 1996 to July 2017. We have three main findings. First, we find that pairs trading constrained within each market generates no significant abnormal returns. However, if investors can trade across Mainland China and Hong Kong, pairs trading is profitable after adjusting for risk and transaction costs, where the annualized abnormal return is 9% over the full sample. Second, by using a rolling‐window regression, we find that the profitability of the strategy is time‐varying. The bootstrap simulations suggest that the decline in profitability of the strategy since 2012 is due to random chance rather than poor ability of identifying mispriced stocks. However, the vast majority of profitable periods reflect the strategy's ability to choose profitable stocks rather than random chance. Third, the profitability of the strategy is somewhat sensitive to market conditions, most notably, the strategy is more profitable during longer term market turbulence. Overall, our empirical findings are consistent with the Adaptive Market Hypothesis in that the integration of financial markets and market conditions determine the level of market efficiency.

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The Profitability of Pairs Trading Strategies: Distance, Cointegration, and Copula Methods

Cited by 34 publications

References 52 publications

Statistical arbitrage with vine copulas

Statistical arbitrage with vine copulas

Pairs trading with partial cointegration

Pairs trading across Mainland China and Hong Kong stock markets

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