Dissecting Tether’s Nonlinear Dynamics during Covid-19

Título

Dissecting Tether’s Nonlinear Dynamics during Covid-19

Autor

Moinak Maiti, Zoran Grubisic, Darko B. Vukovic

Descripción

The present study is on the five cryptocurrency daily mean return time series linearity dynamics during the Covid-19 period. These cryptocurrencies were chosen based on their influence on the market, primarily driven by its market capitalisation. Tether is included as the most important stable coin on the market, nominally pegged to the U.S. dollar (USD). The reason to investigate it is that there are some inconsistencies in its behaviour as opposed to the other four cryptocurrencies. This study found that the behaviour of Tether cryptocurrency daily average return time series pattern is highly nonlinear and chaotic in nature, whereas the other four cryptocurrencies (namely Bitcoin, Ethereum, XRP and Bitcoin Cash) daily average return time series were found to be linear in nature. To further study Tether’s nonlinear time series rich dynamics, this study deployed one category of the regime switching models popularly known as the threshold regressions. The study estimates fairly suggest that both the threshold autoregression (TAR) and smooth transition autoregressive (STAR) models with lag 1 are adequate to capture the rich nonlinear and chaotic dynamics of Tether’s daily average return time series.

Fecha

2020

Materia

covid-19, cryptocurrency, nonlinear dynamics, tether, threshold autoregression, smooth transition autoregressive model

Identificador

10.3390/joitmc6040161

Fuente

Epidemiology and Health

Editor

Korean Society of Epidemiology

Cobertura

Business, Management. Industrial management

Archivos

https://socictopen.socict.org/files/to_import/pdfs/3b85839e366e70b058752602dfb6d1f9.pdf

Colección

Citación

Moinak Maiti, Zoran Grubisic, Darko B. Vukovic, “Dissecting Tether’s Nonlinear Dynamics during Covid-19,” SOCICT Open, consulta 21 de abril de 2026, https://socictopen.socict.org/items/show/7390.

Formatos de Salida

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