High Frequency Price Change Spillovers in Bitcoin Markets

Abstract

The study of connectedness is key to assess spillover effects and identify lead-lagrelationships among market exchanges trading the same asset. By means of an extension of Dieboldand Yilmaz (2012) econometric connectedness measures, we examined the relationships of five majorBitcoin exchange platforms during two periods of main interest: the 2017 surge in prices and the 2018decline. We concluded that Bitfinex and Gemini are leading exchanges in terms of return spillovertransmission during the analyzed time-frame, while Bittrexs act as a follower. We also found thatconnectedness of overall returns fell substantially right before the Bitcoin price hype, whereas itleveled out during the period the down market period. We confirmed that the results are robust withregards to the modeling strategies.

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

“…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).…”

Bitcoin and Fiat Currency Interactions: Surprising Results from Asian Giants

“…Another recent study suggests that during a financial and economic disruption, such as the covid-19 crash, Bitcoin and cryptocurrencies act more as amplifiers of contagion rather than hedge or safe havens [2]. Giudici and Pagnottoni [54] study high frequency price change spillovers in Bitcoin exchanges to specifically assess spillover effects and address lead-lag relationships among market exchanges via an extension of Diebold and Yilmaz [55] econometric connectedness measures. They also observe that connectedness of overall returns falls significantly immediately before Bitcoin price hype events.…”

Lead Behaviour in Bitcoin Markets

“…In this context, many of the stylized facts that are valid for traditional financial time series apply, to some extent, also in the context of these alternative currencies Elendner et al (2017). A large stream of papers consider the dynamics of crypto prices, using VAR models (Bianchi (2019); Catania et al (2019); Bohte and Rossini (2019); Giudici and Abu-Hashish (2019)), VECM models (Giudici and Pagnottoni (2019a), (2019b)), similarity networks Giudici and Polinesi (2019) and Generalized Autoregressive Conditional Hetheroskedasticity (GARCH) models Bouoiyour et al (2016). The results from the different papers, however, seem far from consistent.…”