Modeling Time Series with Both Permanent and Transient Components Using the Partially Autoregressive Model

Clegg, Matthew

doi:10.2139/ssrn.2556957

Cited by 4 publications

(11 citation statements)

References 9 publications

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“…Note that the time series X 2 and X 1 are connected by a partially autoregressive (PAR) model Summers (1986) and Poterba and Summers (1988), and further elaborated in Clegg (2015) and the associated R package partialAR. A key statistic of a PAR model is the proportion of variance attributable to mean-reversion, given as,…”

Section: Representationmentioning

confidence: 99%

“…The sample size n is set to 100, 1000, or 10000 and we perform 10000 replications for each setting. is a random walk -see Clegg (2015) for further details. When H R 0 is rejected, we assume that the cointegrating process either follows a partially autoregressive model of order one or autoregressive model of order one.…”

Section: Consistency Of Estimation Routinementioning

confidence: 99%

“…Based upon the fitted values for ρ, σ M , and σ R , the Kalman gain κ is computed. The Kalman gain can be computed either as a closed-form formula (see Clegg (2015)) or it can be approximated through the Kalman filter equations.…”

Section: Trading Periodmentioning

confidence: 99%

“…Also, note that (1 − B)X 2,t = (1 − B)(βX 1,t + M t + R t ) = βD t + (1 − B)(M t + R t ). Having recovered β, we can now compute the sequence W t = X 2,t − βX 1,t = M t + R t , (A.6) which is partially autoregressive, and known to be identifiable (Clegg, 2015). If we take…”

Section: Appendixa Identifiabilitymentioning

confidence: 99%

“…The second one is simulative. Specifically, we evaluate the goodness of our maximum likelihood based estimators and analyze size as well as power properties of a likelihood ratio test for partial cointegration -originally proposed in Clegg (2015). In a further Monte Carlo simulation, we benchmark PCI-based versus CI-based pairs trading on artificially generated pairs, thereby extracting key determinants of profitability for the individual systems.…”

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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Section: Representationmentioning

confidence: 99%

Section: Consistency Of Estimation Routinementioning

confidence: 99%

Section: Trading Periodmentioning

confidence: 99%

Section: Appendixa Identifiabilitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Pairs trading with partial cointegration

Clegg

Krauß

2017

Quantitative Finance

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A cointegration analysis of agricultural, energy and bio-fuel spot, and futures prices

et al. 2017

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This paper features an analysis of the cointegration relationships among agricultural commodity, ethanol and Cushing crude oil spot and futures prices. The use of grains for the creation of bio-fuels has sparked fears that these demands are inating food prices. We analyse approximately 10 years of daily spot and futures prices for corn, wheat, sugar ethanol and oil prices from Datastream for the period 19 July 2006 to 2 July 2015. The analysis, featuring Engle-Granger pairwise cointegration and Markov-switching VECM and Impulse Response Analysis, conrms that these markets have signicant linkages which vary according to whether they are in low or high volatility regimes.

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Measuring price discovery in the European wheat market using the partial cointegration approach

Vollmer

Herwartz

Cramon‐Taubadel

2019

European Review of Agricultural Economics

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Understanding price discovery in agricultural spot and futures markets is important for market participants and policy makers, because it can contribute to better management decisions and more informed policy debates on market regulation. Combining partial cointegration with state space modelling, we generate time-varying price discovery metrics for the European wheat market that allow for shifts in the long-run relationship. We find that the futures market dominates price discovery in terms of efficiency, but that this dominance is reduced in phases of higher price volatility. We find evidence of persistent shocks in the long-run relationship between spot and futures prices that appear to be related to variations in the quality of the wheat harvest, and to the concatenation of the futures prices.

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Modeling Time Series with Both Permanent and Transient Components Using the Partially Autoregressive Model

Cited by 4 publications

References 9 publications

Pairs trading with partial cointegration

Pairs trading with partial cointegration

A cointegration analysis of agricultural, energy and bio-fuel spot, and futures prices

Measuring price discovery in the European wheat market using the partial cointegration approach

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