Dynamic spillovers between the term structure of interest rates, bitcoin, and safe-haven currencies

Abstract

This study examines the connectedness between the US yield curve components (i.e., level, slope, and curvature), exchange rates, and the historical volatility of the exchange rates of the main safe-haven fiat currencies (Canada, Switzerland, EURO, Japan, and the UK) and the leading cryptocurrency, the Bitcoin. Results of the static analysis show that the level and slope of the yield curve are net transmitters of shocks to both the exchange rate and its volatility. The exchange rate of the Euro and the volatility of the Euro and the Canadian dollar exchange rate are net transmitters of shocks. Meanwhile, the curvature of the yield curve and the Japanese Yen, Swiss Franc, and British Pound act mainly as net receivers. Our static connectedness analysis shows that Bitcoin is mainly independent of shocks from the yield curve’s level, slope, and curvature, and from any main currency investigated. These findings hint that Bitcoin might provide hedging benefits. However, similar to the static analysis, our dynamic analysis shows that during different periods and particularly in stressful times, Bitcoin is far from being isolated from other currencies or the yield curve components. The dynamic analysis allows us to observe Bitcoin’s connectedness in times of stress. Evidence supporting this contention is the substantially increased connectedness due to policy shocks, political uncertainty, and systemic crisis, implying no empirical support for Bitcoin’s safe-haven property during stress times. The increased connectedness in the dynamic analysis compared with the static approach implies that in normal times and especially in stressful times, Bitcoin has the property of a diversifier. The results may have important implications for investors and policymakers regarding their risk monitoring and their assets allocation and investment strategies.

Time–frequency co-movement and risk connectedness among cryptocurrencies: new evidence from the higher-order moments before and during the COVID-19 pandemic

“…Besides, many fund managers have also considered currencies a desirable asset class and pinned their hopes on them to gain high returns despite their extreme volatility (Ji et al 2019). Furthermore, cryptocurrencies have been regarded as a 'safe haven' due to their low correlations with traditional assets and favorable performance during turbulent times (Kumar et al 2022;Aharon et al 2021). Cryptocurrencies have shown tremendous potential, and their trading volumes are skyrocketing (Hasan et al 2022).…”

“…To boost the global economy under the COVID-19 pandemic, the quantitative easing (QE) monetary policy has been commonly implemented by worldwide central banks thus leading to a widespread fear that fiat currencies will most likely lose value (Shahzad et al 2021). Bitcoin has been regarded as a 'safe haven' during the COVID-19 pandemic (Kumar et al 2022;Aharon et al 2021).…”

Impact of COVID-19 effective reproductive rate on cryptocurrency

“…In this study, we extend the existing literature by adding the influence of fear and uncertainty by introducing a pandemic-related period to the evaluation. This is particularly important because, as Aharon et al ( 2021 ) noted, CCs, such as bitcoin, are not independent of other currencies during market stress. In the current study, we use the effective reproductive rate (Spread), which is an epidemiological indicator of the spread of COVID-19, to conduct the analysis.…”

Oil price shocks and the term structure of the US yield curve: a time–frequency analysis of spillovers and risk transmission

“…In particular, it is worth mentioning that over the two last decades, a vast body of scientific research addressing the interrelations of the US yield curve and the most relevant economic parameters has been produced. For deeper insights, we recommend consulting (Estrella, 2005 ; Diebold & Li, 2006 ; Rudebusch & Williams, 2009 ; Aguiar-Conraria et al, 2012 ; Diebold & Rudebusch, 2013 ; Fernandes & Vieira, 2019 ; Gubareva & Keddad, 2020 ; Gupta et al, 2021 ; Aharon et al, 2021 ) among many others. Since the yield curve prediction tool was pioneered by Harvey ( 1986 ) (see also Harvey, 1988 ), proposing the gap between the US Treasury short-end and long-end yields as an alert on a possible nosediving of the US economy, investors worldwide are closely monitoring the US interest rates for signs of eventual recessions (Gupta et al, 2020 ; Hillerbrand et al, 2018 ).…”