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The issue related to the quantification of the tail risk of cryptocurrenciesis considered in this paper. The statistical methods used in the study arethose concerning recent developments in Extreme Value Theory (EVT) for weaklydependent data. This research proposes an expectile-based approach forassessing the tail risk of dependent data. Expectile is a summary statisticthat generalizes the concept of mean, as the quantile generalizes the conceptof the median. We present the empirical findings for a dataset ofcryptocurrencies. We propose a method for dynamically evaluating the level ofthe expectiles by estimating the level of the expectiles of the residuals of aheteroscedastic regression, such as a GARCH model. Finally, we introduce theMarginal Expected Shortfall (MES) as a tool for measuring the marginal impactof single assets on systemic shortfalls. In our case of interest, we arefocused on the impact of a single cryptocurrency on the systemic risk of thewhole cryptocurrency market. In particular, we present an expectile-based MESfor dependent data.

Tail Risk and Systemic Risk Estimation of Cryptocurrencies: an Expectiles and Marginal Expected Shortfall based approach