Spillovers between Bitcoin and other assets during bear and bull markets

Abstract

This paper contributes to the embryonic literature on the relations between Bitcoin and conventional investments by studying return and volatility spillovers between this largest cryptocurrency and four asset classes (equities, stocks, commodities, currencies, and bonds) in bear and bull market conditions. We conducted empirical analyses based on a smooth transition VAR GARCH-in-mean model covering daily data from July 19, 2010 to October 31, 2017. We found significant evidence that Bitcoin returns are related quite closely to those of most of the other assets studies, particularly commodities, and therefore, the Bitcoin market is not isolated completely. The significance and sign of the spillovers exhibited some differences in the two market conditions and in the direction of the spillovers, with greater evidence that Bitcoin receives more volatility than it transmits. Our findings have implications for investors and fund managers who are considering Bitcoin as part of their investment strategies and for policymakers concerned about the vulnerability that Bitcoin represents to the stability of the global financial system.

An Analysis of Bitcoin’s Price Dynamics

“…These findings are also supported by Yermack (2013) and studies in which stock markets have an impact on the price of Bitcoin. Interestingly, Bouri et al (2018c) find moderate integration between Bitcoin and most of the asset classes studied, included MSCI World and gold.…”

A Survey on Empirical Findings about Spillovers in Cryptocurrency Markets

“…Autoregressive conditional heteroskedasticity (ARCH) methodologies and especially the exponential GARCH (EGARCH) and the Glosten-Jagannathan-Runkle (GJR)-GARCH specifications have been employed for capturing volatilities and leverage effects as in Bouri et al (2018). Correlation is also investigated by dynamic conditional correlations (DCC)-GARCH and asymmetric DCC-GARCH methods.…”

“…This model takes into consideration the asymmetries of negative shocks on conditional variance as in Symitsi and Chalvatzis (2018). Alternative specifications based on the combination of VAR and GARCH schemes have been employed in Bouri et al (2018) in the form of smooth-transition VAR combined with bivariate GARCH-M (STVAR-BTGARCH-M) specifications. Integrated GARCH (IGARCH) methodologies combined with dynamic conditional correlations have been adopted by Kumar and Anandarao (2019).…”

“…More specifically, Bouri et al (2018) investigate return and volatility spillovers between Bitcoin and stocks, commodities, currencies and bonds in bearish and bullish conditions in markets. They employ a bivariate generalized autoregressive conditional heteroskedasticity (BTGARCH) specification to model volatility as well as the smooth transition vector autoregressive (STVAR) scheme in order to capture the changes between market conditions (STVAR-BTGARCH-M).…”

Spillover Risks on Cryptocurrency Markets: A Look from VAR-SVAR Granger Causality and Student’s-t Copulas

“…With the presence of Bitcoin and the rest of coins in market (referring to Altcoins, which are established for alternative investment after the commencement of Bitcoin), many investors paid close attention to them as a potential market for earning money. Unbelievably, Bitcoin returns used to climb up by 1358% in 2017 alone (Bouri et al 2018a). Afterwards, numerous financial institutions, such as the Chicago Mercantile Exchange (CME) Group and the Chicago Board Options Exchange's (CBOE), accepted this coin as a part of their playground in derivatives.…”