A pairs trading strategy based on linear state space models and the Kalman filter

Moura, Carlos Eduardo de; Pizzinga, Adrian; Zubelli, Jorge P.

doi:10.1080/14697688.2016.1164886

Cited by 27 publications

(14 citation statements)

References 39 publications

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“…These applications identify cointegrated pairs in a formation period and trade the cointegrating process in a subsequent trading period. Further studies describe the spread in state space, but still as fully mean-reverting model -see, among others, Elliott et al (2005); Do et al (2006); Triantafyllopoulos and Montana (2011);de Moura et al (2016). The results are convincing.…”

Section: Introductionsupporting

confidence: 72%

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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Section: Introductionsupporting

confidence: 72%

Pairs trading with partial cointegration

Clegg

Krauß

2017

Quantitative Finance

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“…(2005), Triantafyllopoulos and Montana (2009) and Moura et al . (2016) employ state‐space methods to carry out pairs trading. For instance, Moura et al .…”

Section: Examplesmentioning

confidence: 99%

“…For instance, Moura et al . (2016) propose the following trading algorithm. If the spread S t between assets A and B is minimally below zero and p up = Pr[( S t + 1 > 0) ∪ ( S t + 2 > 0) ∪ … ∪ ( S t + k > 0) | S 1 , … , S t ] is sufficiently large, take a long position on the spread (i.e.…”

Section: Examplesmentioning

confidence: 99%

“…In quantitative finance, applying state‐space methods for pairs‐trading strategies (cf. Vidyamurthy, 2004; Elliott et al, 2005; Triantafyllopoulos and Montana, 2009; Moura et al, 2016) demands the covariance matrix of future state vectors, which is difficult to obtain using traditional Kalman filtering algorithms. In macroeconomics, using a Kalman smoother to estimate confidence bands for time‐varying measures of exchange rate pass‐through (cf.…”

Section: Introductionmentioning

confidence: 99%

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Extensions to the invariance property of maximum likelihood estimation for affine‐transformed state‐space models

Pizzinga¹,

Fernandes²

2020

Journal Time Series Analysis

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Replacing the state vector of a linear state‐space model by any one‐to‐one linear transformation does not alter maximum likelihood estimation. We extend this invariance property to more general settings, with possibly diffuse initialization of the Kalman filter and injective affine transformations of the state vector. Our results hold for both direct maximization of the likelihood function and the EM algorithm. We offer two real examples that illustrate how one may employ our results to handle a variety of affine‐transformed state‐space models in the literature.

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“…Linear state space (LSS) model is a mathematical model and has numerous applications in signal processing, control etc. [12–15]. The solutions for LSS include Kalman filter (KF), auto covariance least‐squares (ALS) [16–18], modified Bryson–Frazier smoother [19], minimum‐variance smoother [20, 21], and so on.…”

Section: Introductionmentioning

confidence: 99%