Modelling Extremal Events for Insurance and Finance

Embrechts, P., Klüppelberg, C., & Mikosch, T. (1997). Modelling Extremal Events for Insurance and Finance. Springer.

Type

Book

Intellectual base

Review or monograph

Year

1997

DOI

10.1007/978-3-642-33483-2

ISBN

9783642082429

Link

https://link.springer.com/book/10.1007/978-3-642-33483-2

Cited from

futurism_foresight, mathematics, statistics_experimental_design

Cited by

3 citations across 3 artifacts.

Each citation links to the sentence it supports in the citing article.

Primes

• Central Limit Theorem
  • And by characterizing its own boundary — the regimes where the classical conditions fail and a different attractor takes over — the theorem organizes a whole family of aggregation phenomena (sums under finite variance go Gaussian, maxima go to extreme-value laws, products go log-normal, heavy-tailed sums go to stable laws) into one navigable map rather than a list of unrelated results.
• Heavy-Tailed Distributions
  • The prime reframes a sprawling worry about rare disasters into a single diagnostic: where does the mass in the tail live, and how slowly does it decay?

    This sourceCanonical treatment of extreme-value theory and heavy-tailed risk: reframes tail risk as a tail-mass diagnostic (D54-521), establishes which inferences hold under heavy tails (D54-522), and shows that Gaussian claim-size modelling underprices catastrophic insurance losses (D54-524).

• Wild Cards
  • Routine risks live in the tail of well-characterized distributions (operational failures, market downturns within historical range, expected regulatory shifts within normal policy cycles), the territory in which Embrechts, Klüppelberg, and Mikosch (1997) develop extreme-value theory; wild cards lie in territory where the distribution itself is not well-characterized or is thought to be subject to structural change.

    This sourceCanonical treatment of extreme-value theory and heavy-tailed risk: reframes tail risk as a tail-mass diagnostic (D54-521), establishes which inferences hold under heavy tails (D54-522), and shows that Gaussian claim-size modelling underprices catastrophic insurance losses (D54-524).

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