Vector error correction models to measure connectedness of Bitcoin exchange markets

Abstract

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.

Investigating the relationship between volatilities of cryptocurrencies and other financial assets

“…Gold has some intrinsic values but most likely it does not justify its current market value (Dyhrberg 2016b ). Bitcoin is defined as a highly volatile asset (Brière et al 2015 ; Selmi et al 2018 ; Symitsi and Chalvatzis 2019 ; Agosto and Cafferata 2020 ; Giudici and Pagnottoni 2020 ; Baur and Hoang 2020 ). It has become a main theme in the financial press and academia.…”

Financial Bubbles: A Study of Co-Explosivity in the Cryptocurrency Market

“…The lack of an intrinsic value and the substantial changes in their price gave rise to a debate in the financial literature. As Giudici et al (2019b) pointed out, part of the on-going discussion focuses on the unclear way in which cryptocurrencies may be classified: if as commodities, money or derivatives; some attention is also brought to legal and political issues.…”

“…In particular, Corbet et al (2018a) applied the Diebold and Yilmaz (2014) methodology to study volatility transmission between cryptoassets, finding high correlations and evidence of a spillover between Bitcoin and Ripple, and Yi et al (2018) studied co-movements in a set of cryptocurrencies using a LASSO-VAR approach. The VAR methodology was also applied by Giudici and Pagnottoni (2019a) and Giudici and Pagnottoni (2019b) to study spillovers in the Bitcoin exchanges, while Giudici and Abu Hashish (2019) used a network VAR model to investigate interconnectedness in the cryptocurrency market. The network methodology to study correlation between cryptocurrencies was also recently applied by Giudici and Polinesi (2019) and Chen et al (2020).…”

High Frequency Price Change Spillovers in Bitcoin Markets

“…On the other hand, Pagnottoni and Dimpfl (2018), who analyzed a subsequent timespan, concluded the decreased role of BTC-e and the increased one of Chinese exchange platforms in the price discovery mechanism by means of the (Hasbrouck 1995;Gonzalo and Granger 1995) techniques. Recently, (Giudici and Abu-Hashish 2018;Giudici and Pagnottoni 2019) have also focused on price discovery, analyzing Bitcoin daily prices, respectively, with a VAR model and a vector error correction model (VECM).…”

“…To summarize our empirical contribution in a nutshell, we are able to shed further light on price discovery among Bitcoin exchange markets. Previous papers, such as (Brandvold et al 2015;Pagnottoni and Dimpfl 2018;Giudici and Pagnottoni 2019), found that the exchange markets with higher traded volumes are typically the ones that drive prices and spillovers. Differently from the previous papers, based on daily price data, we considered high frequency data.…”