Volatility persistence in cryptocurrency markets under structural breaks

Abakah, Emmanuel Joel Aikins; Gil-Alana, Luís A.; Madigu, Godfrey; Romero-Rojo, Fatima

doi:10.1016/j.iref.2020.06.035

Cited by 59 publications

(31 citation statements)

References 85 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These results were similar to those of Soylu et al (2020), Palamalai et al (2020), Abakah et al (2020), and Sensoy et al (2020). Actually, the characteristics of clustering, spillover, and long memory in volatility were the same features.…”

Section: Ar(1) and Garch(11) Modelssupporting

confidence: 90%

“…Second, from the empirical results of the GARCH(1,1) models, we proved that, for all of the 10 GARCH(1,1) models, the values of the coefficient β i were greater than 0.641374, which means that these 10 cryptocurrencies' return indices had features of volatility clustering or memory persistence in the long run. This result was similar to those of Soylu et al (2020), Palamalai et al (2020), Abakah et al (2020), andSensoy et al (2020). Tether had the lowest GARCH values, but the other nine cryptocurrencies had higher GARCH values than Tether; all of the 10 cryptocurrencies' GARCH values decreased from the pre-COVID-19 period to the COVID-19 period.…”

Section: Summary and Further Studiessupporting

confidence: 89%

“…The characteristics of long memory or persistence in volatility have also been discussed by some researchers. Abakah et al (2020) analyzed the volatility persistence in 12 main cryptocurrencies, including Bitcoin, Bitshare, Bytecoin, Dash, Ethereum, Litecoin, Monero, Nem, Ripple, Siacoin, Stellar, and Tether, by considering the possibility of structural breaks and found that the volatilities represented in both absolute and squared returns display long memory features, but after accounting for structural breaks, the degree of persistence in the cryptocurrency market is reduced.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Firstly, although many researchers have studied the varying volatilities of cryptocurrencies (Palamalai et al 2020;Abakah et al 2020;Enoksen et al 2020) and the impacts of COVID-19 on the cryptocurrencies' volatility (Ardia et al 2019;García-Medina and Hernández C 2020;James et al 2021), the average decreasing features from the pre-COVID-19 period to the COVID-19 period have not been summarized by anyone, and we will discuss this issue. Actually, volatility clustering is a basic in-sample characteristic of cryptocurrencies (Ardia et al 2019); based on a GARCH(1,1) model, the characteristics of clustering, spillover, asymmetry, and long memory in volatility share the same feature, which is dependent on the coefficient of GARCH.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Generally, if a time series is a partial autocorrelation, it is good to choose the AR(p) model; the residual item can be used in the GARCH model. Inversely, if a time series is not an autocorrelation, some researchers prefer to directly use both the absolute and the squared values of the returns in the GARCH model (Abakah et al 2020). If a time series is not an autocorrelation but the AR(p) model is chosen and the residual item is used in the GARCH model, it does not matter for the GARCH model.…”

Section: Ar(1)-garch(11) Modelmentioning

confidence: 99%

See 4 more Smart Citations

Are GARCH and DCC Values of 10 Cryptocurrencies Affected by COVID-19?

Yan

Huqin

Gupta

2022

JRFM

View full text Add to dashboard Cite

This paper examines the dynamic conditional correlations among 10 cryptocurrencies and the possibility of hedging investment strategies among multiple cryptocurrencies over the period affected by COVID-19 from 2017 to 2022. After studying the relationship between Bitcoin, Ethereum, and the other eight cryptocurrencies, four main results were obtained in this paper: first, from the pre-COVID-19 period to the COVID-19 period, almost all of the cryptocurrencies’ return growth rates increased, and COVID-19 had a positive effect on the returns of cryptocurrencies. Second, all of the cryptocurrencies’ return indices had features of volatility clustering and memory persistence in the long run; from pre-COVID-19 to COVID-19, these cryptocurrencies’ GARCH values decreased, but the correlations among the varying GARCH values increased. Third, the varying correlations between the return indices of Bitcoin, Ethereum, and the other cryptocurrencies were very strong; from pre-COVID-19 to COVID-19, the average dynamic correlations between Bitcoin and the others increased. Fourth, Tether can be used as a hedge cryptocurrency against the other cryptocurrencies as COVID-19 enhanced its hedging feature.

show abstract

Section: Ar(1) and Garch(11) Modelssupporting

confidence: 90%

Section: Summary and Further Studiessupporting

confidence: 89%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Ar(1)-garch(11) Modelmentioning

confidence: 99%

See 3 more Smart Citations

Are GARCH and DCC Values of 10 Cryptocurrencies Affected by COVID-19?

Yan

Huqin

Gupta

2022

JRFM

View full text Add to dashboard Cite

show abstract

Which factors drive Bitcoin volatility: Macroeconomic, technical, or both?

Wang

Bouri

et al. 2022

Journal of Forecasting

View full text Add to dashboard Cite

Academic research relies heavily on exogenous drivers to improve the forecasting accuracy of Bitcoin volatility. The present study provides additional insight into the role of both macroeconomic and technical indicators in forecasting the realized volatility of Bitcoin. Using 17 famous macroeconomic variables and 18 technical indicators between December 2011 and April 2021, the results reveal that the shrinkage methods, including elastic net and LASSO, can powerfully extract predictive information from macroeconomic and technical indicators. We further investigate the forecasting power of macroeconomic factors and technical indicators in terms of variable selection, business cycle, and volatility levels, and the results show strong evidence that the macroeconomic indicators (namely, S&P 500 realized volatility, global real economic activity index, and trade‐weighted USD index return) are the most frequently selected by shrinkage method, suggesting that their ability to forecast Bitcoin volatility is stronger than that of technical indicators. However, technical indicators are more powerful in forecasting Bitcoin volatility during the low volatility state.

show abstract

The isotropy of cryptocurrency volatility

Hairudin

Mohamad

2023

Int. J Fin Econ

View full text Add to dashboard Cite

We examine the fractal volatility and long‐range dependence of Bitcoin, Ethereum, Tether and USD Coin by employing the continuous wavelet transform, maximal overlap discrete wavelet transform and rescaled range. Our dataset consists of daily prices spanning from January 2017 through to October 2022, encapsulating pre‐ and post‐epidemic eras. Generally, our findings suggest that Tether presents the least overall volatility throughout the time‐frequency spectrum. USD Coin demonstrates ephemeral turbulence, contrary to Tether's maturity in influencing market equilibrium through token issuance and trade responses. In the post‐epidemic sample, both stablecoins indicate mean reversion, with USD Coin showing marginally better efficiency. Conversely, investment tokens display persistent clusters due to retail traders and long‐term fundamental institutions. Although both tokens illustrate multifractal volatility, Ethereum unveils more essence of self‐similarity than Bitcoin. Hence, there is no evidence that Ethereum truly duplicates Bitcoin since policy‐related events differ between them, as both return series move incongruously. Conditional dynamics signify that all cryptocurrencies, except Tether, were affected by the pandemic transition of COVID‐19 and subsequent macroeconomic news. The unconditional volatility of stablecoins evinces zero‐mean errors, antithetical to investment tokens exhibiting annual cycles. The fractal geometry suggests that investment tokens simulate one‐dimensional lines, whereas stablecoins mimic two‐dimensional planes.

show abstract

Volatility persistence in cryptocurrency markets under structural breaks

Cited by 59 publications

References 85 publications

Are GARCH and DCC Values of 10 Cryptocurrencies Affected by COVID-19?

Are GARCH and DCC Values of 10 Cryptocurrencies Affected by COVID-19?

Which factors drive Bitcoin volatility: Macroeconomic, technical, or both?

The isotropy of cryptocurrency volatility

Contact Info

Product

Resources

About