An efficient semiparametric maxima estimator of the extremal index

By Paul Northrop. 2015

47 citations recorded by the source corpus.

The extremal index θ, a measure of the degree of local dependence in the extremes of a stationary process, plays an important role in extreme value analyses. We estimate θ semiparametrically, using the relationship between the distribution of block maxima and the marginal distribution of a process to define a semiparametric model. We show that these semiparametric estimators are simpler and substantially more efficient than their parametric counterparts. We seek to improve efficiency further using maxima over sliding blocks. A simulation study shows that the semiparametric estimators are competitive with the leading estimators. An application to sea-surge heights combines inferences about θ with a standard extreme value analysis of block maxima to estimate marginal quantiles.

Subfields: Finance, Global and Planetary Change, Oceanography

View Paper: https://www.semanticscholar.org/paper/05804e37407060366f0f1fed33539555871ad6b7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Finance

1. Chen Zhou
2. Axel Bucher
3. Johan Segers
4. A. C. Davison
5. Jean-David Fermanian

In Global and Planetary Change

1. Chen Zhou
2. Axel Bucher
3. A. C. Davison

In Oceanography

1. Chen Zhou
2. Axel Bucher

Citations in the corpus

• A Horse Race between the Block Maxima Method and the Peak–over–Threshold Approach (2018, background): “Regarding the estimation of the extremal index, a large variety of estimators has been proposed, which may itself be grouped into four categories: 1) BM-like estimators based on “blocking” techniques (Northrop, 2015; Berghaus and Bücher, 2017), 2) POT-like estimators that rely on threshold exceedanc”