Correlation and Dependence in Risk Management

Embrechts, P., McNeil, A. J., & Straumann, D. (2002). Correlation and Dependence in Risk Management: Properties and Pitfalls. Risk Management: Value at Risk and Beyond.

Type

Book chapter

Intellectual base

Review or monograph

Year

2002

Pages cited

176-223

DOI

10.1017/cbo9780511615337.008

ISBN

9780521781800

DOI registrant

Cambridge University Press

Link

https://doi.org/10.1017/CBO9780511615337.008

Cited from

systems_cybernetics

Cited by

3 citations across 3 artifacts.

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

Primes

• Correlated Capacity Demand
  • The diversification illusion: pooling consumers reduces variance under independence, but under correlation the variance reduction collapses and the pooled load can be worse than the sum of marginal loads under certain rationing rules. Tail-correlation versus centre-correlation: two demand processes can be near-uncorrelated under normal conditions and strongly correlated in the tail, and the relevant correlation for capacity planning is the tail correlation, often much higher than linear-correlation estimates suggest — which is why copula models exist to capture it.

    This sourceShows linear correlation understates tail dependence, motivates copulas, and demonstrates that pooling under tail dependence can worsen rather than reduce joint-exceedance risk. (

    Supported in partVerified against the publisher's abstract

    Abstract backs the dependence-beyond-linear-correlation and copula frame; it says nothing about pooling, variance collapse, pooled load vs marginal load, or rationing rules.

    “Linear correlation is a natural dependence measure for multivariate normally, and more generally, elliptically distributed risks but other dependence concepts like comonotonicity and rank correlation should also be understood by the risk management practitioner. Using counterexamples the falsity of some commonly held views on correlation is demonstrated; in general, these fallacies arise from the naive assumption …”

    Read by an automated reader; not a human review. How support was checked

Domain-specific

• Tail dependence
  • Financial or environmental interpretation must not infer causal linkage from a dependence statistic.

Mechanisms

• Scenario or Monte Carlo Joint-State Sampling
  • Its failure mode is garbage-in with a confident face: assume independence where conditions move together, or a thin tail where the real one is fat, and it returns a precise, polished, wrong probability whose 50,000-run histogram hides the guesswork in its priors.

    This sourceShows that misspecified dependence and tail assumptions can yield precise but materially wrong joint-risk probabilities.

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 1 of 3 citations: 1 supported in part. Each verdict is shown under its citation below, with what in the work backs the sentence.

Support is checked per citation rather than per work — the same source can be cited soundly in one article and wrongly in another. Per-citation recording began recently, so a citation with no recorded check is a gap in the record rather than evidence it went unchecked.

See how references were verified.

Registry ID ref:7c0b42f1797a · see in the full table