Tail Risk in the Tail: High Quantile Estimation for Systemic Risk

Natalia Nolde's research article and supplementary materials address high quantile estimation when a related variable is extreme. The work includes a real data example and simulation studies to illustrate the performance of a proposed estimator for conditional value-at-risk (CoVaR). Supplementary materials for reproducing the work are available online.

Use Cases

• Estimating conditional value-at-risk (CoVaR) for systemic financial risk assessment based on the described methodology.
• Simulating bivariate tail dependence structures to test high quantile estimators.
• Applying the proposed adjustment factor in conjunction with univariate high quantile estimation techniques.

Strengths

• Includes supplementary materials for reproducing the work, as stated in the description.
• The proposed estimator's asymptotic behavior is established under relatively weak assumptions.
• File size is 2.6 MB, indicating a manageable download.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The primary content is a research article; the accompanying data's exact structure and scale are not detailed.

Provenance

Source

Natalia Nolde via figshare

Collection Method

Likely contains data generated from simulation studies and a real data example as part of methodological research.

Freshness

Last updated 2026-05-21 17:49:53; freshness should be verified.