Department of Pattern Recognition

(eng): During the last years, cryptocurrencies have been increasingly becoming a relevant subject of academic researchers and investors. This paper adopts a novel framework that combines a multivariate Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) and Copula modeling in a two-stage approach to analyze the cryptocurrency volatility dynamics. By combining the aforementioned techniques, on top of showing that price movements in one cryptocurrency can significantly influence others, the use of copulas highlight how these effects can vary across different parts of distributions and thus for different types of events with respect to their extreme nature. The interconnectedness complexity should be taken into consideration when managing risk in portfolio and constructing relevant models.