This review paper surveys recent development in software implementations for extreme
value analyses since the publication of Stephenson and Gilleland (Extremes 8: 87–109,) …

The article contributes to the ongoing search for a market risk measure that is both coherent
and elicitable. We compare two traditional measures, namely Value-at-Risk and the …

We use the daily data of 45 listed financial institutions between January 1, 2008 and
January 31, 2022 to conduct a functional data analysis (FDA) to measure the systemic risk of …

Joint inference on extreme expectiles for multivariate heavy-tailed distributions Page 1 Bernoulli
28(2), 2022, 1021–1048 https://doi.org/10.3150/21-BEJ1375 Joint inference on extreme …

The value at risk based on expectile (EVaR) is a very useful method to measure financial
risk, especially in measuring extreme financial risk. The double-threshold autoregressive …

The Supplementary Material [12] contains further details about our technical conditions and
an expanded discussion of the rates of pointwise convergence of our estimators. We then …

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 …

Expectile-based value-at-risk (EVaR) is a more sensitive measure of the magnitude of
extreme losses compared to the conventional quantile-based value-at-risk (VaR). Besides …

As a least squares analogue of quantiles, extremiles define a coherent risk measure
determined by weighted expectations instead of tail probabilities. Estimating extremiles of …

Marginal expected shortfall is unquestionably one of the most popular systemic risk
measures. Studying its extreme behaviour is particularly relevant for risk protection against …