We propose a robust semi-parametric framework for persistent time-varying extreme tail behavior, including extreme Value-at-Risk (VaR) and Expected Shortfall (ES). The framework builds on Extreme Value Theory and uses a conditional version of the Generalized Pareto Distribution (GPD) for...

The score-driven framework relies on pre-specified scoring rules tied to assumed conditional densities, making it vulnerable to misspecification under outliers or structural breaks. We embed the flexible Barron loss within the quasi score-driven (QSD) framework, allowing the degree of robustness...

We propose a robust semi-parametric framework for persistent time-varying extreme tail behavior, including extreme Value-at-Risk (VaR) and Expected Shortfall (ES). The framework builds on Extreme Value Theory and uses a conditional version of the Generalized Pareto Distribution (GPD) for...

We develop a data-driven procedure to identify which correlations in high-dimensional dynamic systems should be time-varying, constant, or zero. The method integrates a vine-based multivariate partial correlation model with sequential penalized estimation. Applied to 50 US equities and...