Modelling Extremal Events for Insurance and Finance¶
Embrechts, P., Klüppelberg, C., & Mikosch, T. (1997). Modelling Extremal Events for Insurance and Finance. Springer.
Cited by¶
4 citations across 4 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.
Supported in partVerified against the publisher's abstract
The abstract identifies the work as a theory-and-applications reference on extremal events (the maxima branch); it says nothing of stable laws for heavy-tailed sums, log-normal products, or a reasoner being 'primed'.
“Both in insurance and in finance applications, questions involving extremal events (such as large insurance claims, large fluctuations in financial data, stock market shocks, risk management, ...) play an increasingly important role. This book sets out to bridge the gap between the existing theory and practical applications both from a probabilistic as well as from a statistical point of view.”
- 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).
- 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?
- 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).
Supported in partVerified against the publisher's abstract
The book's own abstract identifies it as the theory-and-applications treatment of extremal events in insurance and finance, which backs only the clause naming it as where extreme-value theory is developed.
“Both in insurance and in finance applications, questions involving extremal events (such as large insurance claims, large fluctuations in financial data, stock market shocks, risk management, ...) play an increasingly important role. This book sets out to bridge the gap between the existing theory and practical applications both from a probabilistic as well as from a statistical point of view.”
- 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.
Domain-specific¶
Verification¶
Does it exist? Confirmed. This work's DOI resolves to a registered record, which fixes its identity. That is all it fixes.
Does it back the claim? Read against the text for 2 of 4 citations: 2 supported in part. Each verdict is shown under its citation below, with what in the work backs the sentence.
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