An Examination of Cryptocurrency Volatility: Insights from Skewed Error Innovation Distributions Within GARCH Model Frameworks

Abstract

With escalating public interest in the cryptocurrency market, largely driven by its perceived potential for rapid wealth accumulation and various advantages over traditional currencies, there is an imperative to understand its inherent volatility.This study addresses the dynamic behaviour of cryptocurrencies by utilizing skewed error innovation distributions to model the volatility of five key cryptocurrencies.Data was sourced from Yahoo Finance, encompassing daily closing prices from September 11, 2017, to April 8, 2022.The significance of the skewness parameter in all optimal volatility models (p<.05) substantiates the application of skewed error innovation distributions.Notably, the observed influence of past negative events on volatility was consistently greater than that of positive events across most examined cryptocurrencies.While Value at Risk (VaR) models are frequently used for risk measurement in this domain, this study's findings suggest that their reliability is not universal across all cryptocurrency cases.Consequently, caution is advised when employing VaR models for risk assessment associated with cryptocurrencies.