Returns and volatility spillovers among cryptocurrency portfolios

Fasanya, Ismail O.; Oyewole, Oluwatomisin J.; Odudu, Temitope F.

doi:10.1108/ijmf-02-2019-0074

Cited by 23 publications

(11 citation statements)

References 19 publications

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“…While this paper focuses on three different but integrated methods, there are also many studies that use different techniques to show that the volatility spillovers in the cryptocurrency markets are still prevalent. These methods can be classified as follows: rolling sample analysis (Fasanya et al 2021 ), BEKK-MGARCH analysis (Katsiampa et al 2019b ), machine learning techniques (e.g., linear models, random forests, and support vector machines) (Sebastião and Godinho 2021 ), an integrated cluster detection, optimization, and interpretation approach (Li et al 2021 ), Markov regime-switching vector autoregressive with exogenous variables (MS-VARX) model (Shahzad et al 2021 ), generalized VAR framework (Melki 2020 ), bankruptcy prediction model (Kou et al 2021a , b ), a hybrid interval type-2 fuzzy multidimensional decision-making approach (Kou et al 2021a , b ), and multivariate stochastic volatility model (Zhang and He 2021 ). The common point of using these methods is to capture the inherent secular and cyclical movements in digital asset markets.…”

Section: Introductionmentioning

confidence: 99%

Cue the volatility spillover in the cryptocurrency markets during the COVID-19 pandemic: evidence from DCC-GARCH and wavelet analysis

Özdemir

2022

Financ Innov

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This study investigates the dynamic mechanism of financial markets on volatility spillovers across eight major cryptocurrency returns, namely Bitcoin, Ethereum, Stellar, Ripple, Tether, Cardano, Litecoin, and Eos from November 17, 2019, to January 25, 2021. The study captures the financial behavior of investors during the COVID-19 pandemic as a result of national lockdowns and slowdown of production. Three different methods, namely, EGARCH, DCC-GARCH, and wavelet, are used to understand whether cryptocurrency markets have been exposed to extreme volatility. While GARCH family models provide information about asset returns at given time scales, wavelets capture that information across different frequencies without losing inputs from the time horizon. The overall results show that three cryptocurrency markets (i.e., Bitcoin, Ethereum, and Litecoin) are highly volatile and mutually dependent over the sample period. This result means that any kind of shock in one market leads investors to act in the same direction in the other market and thus indirectly causes volatility spillovers in those markets. The results also imply that the volatility spillover across cryptocurrency markets was more influential in the second lockdown that started at the beginning of November 2020. Finally, to calculate the financial risk, two methods—namely, value-at-risk (VaR) and conditional value-at-risk (CVaR)—are used, along with two additional stock indices (the Shanghai Composite Index and S&P 500). Regardless of the confidence level investigated, the selected crypto assets, with the exception of the USDT were found to have substantially greater downside risk than SSE and S&P 500.

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Section: Introductionmentioning

confidence: 99%

Cue the volatility spillover in the cryptocurrency markets during the COVID-19 pandemic: evidence from DCC-GARCH and wavelet analysis

Özdemir

2022

Financ Innov

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“…Several studies examined the cryptocurrency connectedness during the pandemic to give investors and portfolio managers insights into how the pandemic affected the behavior of cryptocurrencies. Fasanya et al ( 2021 ), Shahzad et al ( 2021 ), and Polat and Kabakçı Günay ( 2021 ) find that volatility spillovers experience significant changes during major market crises. Fasanya et al ( 2021 ) show that the volatility spillover index experiences significant bursts during major market crises.…”

Section: Literature Review and Hypotheses Developmentmentioning

confidence: 99%

Volatility Interdependence Between Cryptocurrencies, Equity, and Bond Markets

et al. 2022

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This paper investigates (i) the return-volatility spillover between Bitcoin, Ethereum, Ripple, and Litecoin, (ii) the interdependence between cryptocurrencies’ volatility and the US equity and bond markets’ volatility, and (iii) the impact of the Covid-19 outbreak on the cryptocurrencies’ return-volatility. A two-step estimation approach is considered where Univariate General Autoregressive Conditional Heteroskedastic models are estimated to model the volatility of the four cryptocurrencies then a Simultaneous Equation Model is estimated to model the interconnection between the cryptocurrency volatilities, the US equity and bond markets’ volatility, and Covid-19 outbreak. We show that return-volatility spillovers exist among Bitcoin, Ethereum, and Litecoin while Ripple is the main transmitter of shocks. We find that the cryptocurrency market is detached from the US stock market but not from the US bond market. Finally, we show that a high economic and financial uncertainty in the US stock market due to pandemic outbreaks affects the price of Litecoin, Bitcoin, and Ethereum. However, shocks are short-lived. Our findings have practical implications; as the evidence of volatility spillovers among cryptocurrencies and their relative isolation from the majority of mainstream assets should be factored into the valuation and portfolio diversification strategies of investors. In crisis times such as those induced by Covid-19, investors who seek protection from downward movements in bond markets could benefit from taking a position in Ethereum. Policymakers can also rely on our findings to time their intervention to stabilize markets and control uncertainties inherent to stressful periods.

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“…Several studies examined the dynamic connectedness between cryptocurrencies and other financial assets. Fasanya et al (2020) made various conclusions regarding the dynamic spillovers of cryptocurrencies. For instance, volatility spillover is time-varying and surges rapidly under market turmoil.…”

Section: Related Literaturementioning

confidence: 99%

Spillovers and bivariate portfolios of gold-backed cryptocurrencies and gold during the COVID-19 outbreak

Nugroho

2021

JIABR

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Purpose This paper aims to analyze the time-varying connectedness of gold-backed cryptocurrencies and gold. This study determines the volatility spillovers in these two asset classes and the performance of bivariate portfolios based on net pairwise spillovers. Design/methodology/approach This research uses two Islamic and four conventional gold-backed cryptocurrencies and gold as variables. GJR-GARCH method under corrected DCC (cDCC) of Aielli (2013) evaluates the dynamic connectedness. Additionally, the spillovers are created using the dynamic connectedness of Diebold and Yilmaz (2012). A network-based spillover of Diebold and Yılmaz, (2014) is also made. A dynamic optimal weights strategy optimized with DCC-t-Copula determines bivariate portfolios’ performances. In general, there are 21 bivariate portfolios. Findings The outbreak of COVID-19 increases the dynamic connectedness of gold and gold-backed cryptocurrencies, which indicates a contagion effect. The results show that gold is the net volatility receiver during the COVID-19 pandemic. Moreover, a portfolio composed of gold and gold-backed cryptocurrency provides high profitability performance but zero hedge effectiveness under optimal weights strategy. Practical implications According to bivariate portfolios based on net pairwise spillovers, gold-backed cryptocurrencies' investors should not add gold to their portfolio during the pandemic because it is a net receiver of risk from the cryptocurrencies. Originality/value To the best of the author’s knowledge, this is the first paper to create bivariate portfolios composed of gold-backed cryptocurrencies and their underlying asset using DCC-t-Copula.

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Returns and volatility spillovers among cryptocurrency portfolios

Cited by 23 publications

References 19 publications

Cue the volatility spillover in the cryptocurrency markets during the COVID-19 pandemic: evidence from DCC-GARCH and wavelet analysis

Cue the volatility spillover in the cryptocurrency markets during the COVID-19 pandemic: evidence from DCC-GARCH and wavelet analysis

Volatility Interdependence Between Cryptocurrencies, Equity, and Bond Markets

Spillovers and bivariate portfolios of gold-backed cryptocurrencies and gold during the COVID-19 outbreak

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