great volatility regarding the response of Bitcoin to a shock of STOXX50. The Granger causality test was constructed to …

This study explores the stylized facts, volatility clustering, other highly irregular behaviour, and risk measures of … cryptocurrencies' returns. By analysing bitcoin, ripple, and ethereum daily data we establish evidence of strong dependencies among … analysed cryptocurrencies. This paper provides new insights about cryptocurrency behaviour and the main measures of risk and …

This research examines the correlations between the return volatility of cryptocurrencies, global stock market indices …, and the spillover effects of the COVID-19 pandemic. For this purpose, we employed a two-stage multivariate volatility … and respond well to previous shocks. As a result, financial assets have low unconditional volatility and the lowest risk …

With the exception of Bitcoin, there appears to be little or no literature on GARCH modelling of cryptocurrencies. This … paper provides the first GARCH modelling of the seven most popular cryptocurrencies. Twelve GARCH models are fitted to each …

This paper examines the volatility of cryptocurrencies, with particular attention to their potential long memory … properties. Using daily data for the three major cryptocurrencies, namely Ripple, Ethereum, and Bitcoin, we test for the long …) Model Method. Our findings show that squared returns of three cryptocurrencies have a significant long memory, supporting …

cryptocurrency market is well known as very volatile, mainly for the fact that the cryptocurrencies have not the price to fall back … GARCH-type models dominate as VaR estimators the overall objective of this paper is to perform comprehensive volatility and … (given by Geometric Brownian Motion). We conclude that the best method for value-at-risk estimation for cryptocurrencies is …

Cryptocurrencies have increasingly attracted the attention of several players interested in crypto assets. Their rapid … growth and dynamic nature require robust methods for modeling their volatility. The Generalized Auto Regressive Conditional … Heteroskedasticity (GARCH) model is a well-known mathematical tool for predicting volatility. Nonetheless, the Realized-GARCH model has …

the dynamics of volatility spillover are observed for a span of 7 years – 2013 to 2020; wherein the total number of sample … & Binance coins are more adversely affected due to the bad news in the market, leading to rigorous fluctuations in volatility … model which we used to estimate and quantify the nature of volatility spillovers. Given the overall cryptocurrency bull …

This study investigates the role of probability distribution in forecasting volatility and Value-at-Risk (VaR). We use …; however, a skewed and heavy-tailed distribution does not improve the accuracy of volatility forecasting. For predictive models …