Pairs trading with the persistence-based decomposition model

Recently, the persistence-based decomposition (PBD) model has been introduced to the scientific community by Rende et al. (2019). It decomposes a spread time series between two securities into three components capturing infinite, finite, and no shock persistence. The authors provide empirical eviden...

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Main Author: Jonas Rende
Format: Article
Language:English
Published: AGH UNIVERSITY PRESS 2020-05-01
Series:Managerial Economics
Online Access:https://journals.agh.edu.pl/manage/article/view/3801
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author Jonas Rende
author_facet Jonas Rende
author_sort Jonas Rende
collection DOAJ
description Recently, the persistence-based decomposition (PBD) model has been introduced to the scientific community by Rende et al. (2019). It decomposes a spread time series between two securities into three components capturing infinite, finite, and no shock persistence. The authors provide empirical evidence that the model adopts well to noisy high-frequency data in terms of model fitting and prediction. We put the PBD model to test on a large-scale high-frequency pairs trading application, using S&P 500 minute-by-minute data from 1998 to 2016. After accounting for execution limitations (waiting rule, volume constraints, and short-selling fees) the PBD model yields statistically significant and economically meaningful annual returns after transaction costs of 9.16 percent. These returns can only partially be explained by the exposure to common risk. In addition, the model is superior in terms of risk-return metrics. The model performs very well in bear markets. We quantify the impact of execution limitations on risk and return measures by relaxing backtesting restrictions step-by-step. If no restrictions are imposed, we find annual returns after costs of 138.6 percent.
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spelling doaj-art-c4f411b0230b436db89fb094845bccba2025-08-20T01:58:34ZengAGH UNIVERSITY PRESSManagerial Economics1898-11432353-36172020-05-0120210.7494/manage.2019.20.2.151Pairs trading with the persistence-based decomposition modelJonas RendeRecently, the persistence-based decomposition (PBD) model has been introduced to the scientific community by Rende et al. (2019). It decomposes a spread time series between two securities into three components capturing infinite, finite, and no shock persistence. The authors provide empirical evidence that the model adopts well to noisy high-frequency data in terms of model fitting and prediction. We put the PBD model to test on a large-scale high-frequency pairs trading application, using S&P 500 minute-by-minute data from 1998 to 2016. After accounting for execution limitations (waiting rule, volume constraints, and short-selling fees) the PBD model yields statistically significant and economically meaningful annual returns after transaction costs of 9.16 percent. These returns can only partially be explained by the exposure to common risk. In addition, the model is superior in terms of risk-return metrics. The model performs very well in bear markets. We quantify the impact of execution limitations on risk and return measures by relaxing backtesting restrictions step-by-step. If no restrictions are imposed, we find annual returns after costs of 138.6 percent.https://journals.agh.edu.pl/manage/article/view/3801
spellingShingle Jonas Rende
Pairs trading with the persistence-based decomposition model
Managerial Economics
title Pairs trading with the persistence-based decomposition model
title_full Pairs trading with the persistence-based decomposition model
title_fullStr Pairs trading with the persistence-based decomposition model
title_full_unstemmed Pairs trading with the persistence-based decomposition model
title_short Pairs trading with the persistence-based decomposition model
title_sort pairs trading with the persistence based decomposition model
url https://journals.agh.edu.pl/manage/article/view/3801
work_keys_str_mv AT jonasrende pairstradingwiththepersistencebaseddecompositionmodel