Asymmetric connectedness among S&P 500, crude oil, gold and Bitcoin

Abstract

PurposeThis paper investigates the dynamic intercorrelation among cryptocurrency (Bitcoin) and conventional financial assets (gold, oil and S&P 500).Design/methodology/approachThe dynamic contemporaneous nexus has been analyzed using spillover index developed and extended by Diebold and Yilmaz (2012, 2014) and Kyrtsou-Labys (2006) nonlinear causality tests. This study is implemented using the daily data spanning from January 2013 to December 2021.FindingsFirst, using the spillover index, the authors find evidence that the S&P 500 was a net transmitter of volatility from oil and gold markets, but a net receiver of volatility from Bitcoin. Return spillovers from crude oil were transmitted first to gold, and Bitcoin markets and return spillovers from gold were transmitted to Bitcoin. Second, Kyrtsou-Labys nonlinear causality tests provide us further insights into the lead-lag interconnections among the four key considered variables from the economic perspective. Specifically, a close inspection of these empirical results, the integration of the four key assets is significant. Similarly, price fluctuation dependency among Bitcoin, stock, gold and oil markets is generally minimal, but it strengthens throughout the COVID-19 period.Originality/valueThis paper is the first study employing the spillover index Diebold-Yilmaz alongside with Kyrtsou-Labys nonlinear causality tests not only to capture the directional return spillover effects but also to highlight the potential presence of asymmetric causality relationships, nonlinear effects among assets under investigation that the previous studies have been ignored in these relations. Therefore, the main contribution of this article to the related literature in this field is significant.

Safe flight to which haven when Russia invades Ukraine? A 48-hour story

“…Thus, to answer the question of 'who moves first', Brent moves first, 24 h before the invasion, whereas bitcoin moves first against other assets within the 24 h after the invasion. Our results are generally in line with Hung (2022) and Mensi et al (2019), who find that bitcoin serves as a strong transmitter of shocks to other assets. Meanwhile, our Brent analysis is consistent with Ji et al (2018) and Suleman et al (2021), who uncover that Brent contributes the most to the positive volatilities of other markets.…”

Asymmetric causality between Bitcoin and tech stocks in the US market using mixed frequency data

“…Umar et al (2021) investigated the relationship between the technology sector and cryptocurrency markets and discovered that while there is substantial interconnectedness among global technology sectors, the cryptocurrency market has less integration and lower exposure to systemic risk. Hung (2022) argues that investors, fund managers and policymakers should consider return spillover effects between assets to implement targeted strategies and assess asset risk effectively.…”

“…Most previous studies have used traditional linear tests or aggregated data to examine the interactions between Bitcoin prices and other financial assets (Hung, 2022). Maghyereh and Abdoh (2021) analyzed the relationship between Bitcoin and stock market returns using a quantile cross-spectral dependence approach.…”

“…Lahiani and Jlassi (2021) explored the connection between cryptocurrencies and stock market returns, finding that Bitcoin had limited predictive power except for Nasdaq. These results align with other recent studies (Elsayed et al ., 2022; Hung, 2022) indicating the presence of asymmetric causality and nonlinear effects between Bitcoin and equity markets. The present study's findings are broadly consistent with these studies, highlighting that the causal relationship predominantly runs from Bitcoin to certain segments of the equity market and not the other way around.…”

“…It is not surprising why a number of previous studies concluded that the causality runs typically from Bitcoin to the equity market studies (e.g. Elsayed et al ., 2022; Hung, 2022; Lahiani and Jlassi, 2021). However, no previous study used an AMIDAS regression model to examine the Granger causality running from high-frequency data (i.e.…”