Methodology for Estimating Extreme Probabilities Beyond Observed Data

A 210.7 KB PDF authored by Joan del Castillo, last updated in April 2026, presents model-free methods for estimating extreme probabilities. The methodology is grounded in order statistics theory and requires only the assumption of a finite expectation. It is applicable to almost any sample size and its results are comparable to those from extreme value theory.

Use Cases

• Estimating financial market risk beyond observed extremes based on the described model-free methodology.
• Validating existing risk models by providing a simpler, alternative bound for tail probabilities.
• Applying order statistics theory to assess extreme event likelihood in fields with finite expectation data.

Strengths

• Methodology is model-free, requiring only a finite expectation assumption.
• Applicable to almost any sample size, enhancing flexibility.
• Published under a permissive CC-BY-4.0 license for reuse.

Limitations

• The primary content is a 210.7 KB PDF; the presence of underlying numerical datasets is unknown.
• Column-level documentation and sample data are absent; implementation details must be extracted from the text.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

figshare, authored by Joan del Castillo.

Collection Method

Theoretical methodology presented in an academic article.

Time Range

Methodology publication date is implied by the 2026 update.

Freshness

Last updated 2026-04-13 12:54:04.

Geography

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