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Vector error correction models to measure connectedness of Bitcoin exchange markets
Author(s) -
Giudici Paolo,
Pagnottoni Paolo
Publication year - 2019
Publication title -
applied stochastic models in business and industry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.413
H-Index - 40
eISSN - 1526-4025
pISSN - 1524-1904
DOI - 10.1002/asmb.2478
Subject(s) - social connectedness , variance decomposition of forecast errors , measure (data warehouse) , econometrics , vector autoregression , order (exchange) , computer science , error correction model , variance (accounting) , economics , finance , cointegration , data mining , psychology , accounting , psychotherapist
Bitcoins are traded on various exchange platforms and, therefore, prices may differ across trading venues. We aim to investigate return connectedness across eight of the major exchanges of Bitcoin, both from a static and a dynamic viewpoint. To this end, we employ an extension of the order‐invariant forecast error variance decomposition proposed by Diebold and Yilmaz (2012) to a generalized vector error correction framework. Our results suggest that there is strong connectedness among the exchanges, as expected, although some of them behave dissimilarly. We identify Bitfinex and Coinbase as leading exchanges during the considered period, while Kraken as a follower exchange. We also obtain that connectedness across exchanges is strongly dynamic, as it evolves over time.