Purpose
The purpose of this research is to analyze the Bitcoin (BTC) and Ether (ETH) long memory and conditional volatility.
Design/methodology/approach
The empirical approach includes ARFIMA-HYGARCH and ARFIMA-FIGARCH, both models under Student‘s t-distribution, during the period (ETH: November 9, 2017 to November 25, 2021 and BTC: September 17, 2014 to November 25, 2021).
Findings
Findings suggest that ARFIMA-HYGARCH is the best model to analyze BTC volatility, and ARFIMA-FIGARCH is the best approach to model ETH volatility. Empirical evidence also confirms the existence of long memory on returns and on BTC volatility parameters. Results evidence that the models proposed are not as suitable for modeling ETH volatility as they are for the BTC.
Originality/value
Findings allow to confirm the fractal market hypothesis in BTC market. The data confirm that, despite the impact of the Covid-19 crisis, the dynamics of BTC returns, and volatility maintained their patterns, i.e. the way in which they evolve, in relation to the prepandemic era, did not change, but it is rather reaffirmed. Yet, ETH conditional volatility was more affected, as it is apparently higher during Covid-19. The originality of the research lies in the focus of the analysis, the proposed methodology and the variables and periods of study.
We analyze volatility contagion between the U.S. and Chinese stock markets and international capital markets. The volatility is modeled using: GARCH, TARCH, EGARCH, APARCH, IGARCH, FIGARCH, ACGARCH and GAS models under Gaussian, GED and t-Student distributions. 21,000 intraday observations of thirteen markets from January/1st to June/25th 2020 are employed. Once volatility is modeled, the incidence of Chinese and American markets on the rest of the bourses is tested employing Vector Autoregressive Markov Switching Models. Evidence confirms incidence of the Chinese and American capital markets volatility in other markets volatility; common breakpoints and Intermarket incidence in high volatility periods stand out.
Se analiza el impacto de la Incertidumbre de la Política Económica (EPU) local y estadounidense en la actividad económica y financiera en México, empleando datos mensuales sobre el periodo enero 1996 a septiembre 2019. Para lograr dicho objetivo, se propone un análisis de Correlación Condicional Dinámica. Los reultados evidencian que la Incertidumbre local tiene un impacto significativo en las variables financieras (mercado accionario y de divisas), pero no en los indicadores económicos (actividad económica y producción industrial). Por el contrario, la incertidumbre estadounidense no tiene efectos significativos en las variables reales, ni financieras
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