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

TL;DR

This paper introduces an expectile-based approach combined with Marginal Expected Shortfall to quantify tail and systemic risks in cryptocurrencies, accounting for dependence and heteroscedasticity in the data.

Contribution

It proposes a novel expectile-based method for tail risk assessment and introduces an expectile-based MES for systemic risk measurement in cryptocurrency markets.

Findings

Empirical analysis on cryptocurrency data demonstrates the effectiveness of the proposed methods.

The expectile-based MES provides insights into the marginal impact of individual cryptocurrencies on systemic risk.

The approach captures tail dependencies better than traditional methods.

Abstract

The issue related to the quantification of the tail risk of cryptocurrencies is considered in this paper. The statistical methods used in the study are those concerning recent developments in Extreme Value Theory (EVT) for weakly dependent data. This research proposes an expectile-based approach for assessing the tail risk of dependent data. Expectile is a summary statistic that generalizes the concept of mean, as the quantile generalizes the concept of the median. We present the empirical findings for a dataset of cryptocurrencies. We propose a method for dynamically evaluating the level of the expectiles by estimating the level of the expectiles of the residuals of a heteroscedastic regression, such as a GARCH model. Finally, we introduce the Marginal Expected Shortfall (MES) as a tool for measuring the marginal impact of single assets on systemic shortfalls. In our case of interest, we are focused on the impact of a single cryptocurrency on the systemic risk of the whole cryptocurrency market. In particular, we present an expectile-based MES for dependent data.