We investigate whether there is a pattern regarding the quality of several models and methods in expected shortfall (ES) estimation, considering distinct asset classes, estimation windows and significance levels. We use unconditional, conditional and quantile/expectile regression-based models....

In this paper we propose an expected shortfall (ES) backtesting approach that uses the dispersion of a truncated distribution by the estimated value-at-risk (VaR) upper limit, does not limit the approach to the Gaussian case and allows us to test if each individual VaR violation is significantly...

In this chapter, we estimate the Expected Shortfall (ES) in conditional autoregressive expectile models by using a nonparametric multiple expectile regression via gradient tree boosting. This approach has the advantages generated by the flexibility of not having to rely on data assumptions and...

In this study, we investigated the impact of international crude oil prices on the different quantiles of the distribution of stock market returns in Argentina, Brazil, Chile, and Mexico, from May 1st, 2015 to January 15th, 2021. For this purpose, quantile regression and ordinary least square...