This work provides a selective review of the most recent dynamic models based on extreme
value theory, in terms of their ability to forecast financial losses through different risk …

Under the framework of dynamic conditional score, we propose a parametric forecasting
model for Value-at-Risk based on the normal inverse Gaussian distribution (Hereinafter NIG …

Response functions that link regression predictors to properties of the response distribution
are fundamental components in many statistical models. However, the choice of these …

This paper develops a new class of dynamic models for forecasting extreme financial risk.
This class of models is driven by the score of the conditional distribution with respect to both …

Encouraged by the literary fact that high-frequency data such as intraday returns contribute
to estimating the tail risk of daily returns, we propose an intraday-return-based Value-at-Risk …

Modeling panels of extremes Page 1 The Annals of Applied Statistics 2023, Vol. 17, No. 1,
498–517 https://doi.org/10.1214/22-AOAS1639 © Institute of Mathematical Statistics, 2023 …

Understanding and modeling the determinants of extreme hourly rainfall intensity is of
utmost importance for the management of flash-flood risk. Increasing evidence shows that …

An accurate prediction of the future service state of long-span bridges is crucial for the
structural reliability evaluation, maintenance planning, and further life-cycle cost analysis. By …

This paper proposes a new risk measurement model that directly incorporate information
from high‐frequency data to predict daily Value‐at‐Risk and expected shortfall. In this …

We use intraday data to construct measures of the realized volatility of bitcoin returns. We
then construct measures that focus exclusively on relatively large realizations of returns to …