A sliding blocks estimator for the extremal index

By Christian-Yann Robert, Johan Segers, C. Ferro. 2008

53 citations recorded by the source corpus.

In extreme value statistics for stationary sequences, blocks estimators are usually constructed by using disjoint blocks because exceedances over high thresholds of different blocks can be assumed asymptotically independent. In this paper we focus on the estimation of the extremal index which measures the degree of clustering of extremes. We consider disjoint and sliding blocks estimators and compare their asymptotic properties. In particular we show that the sliding blocks estimator is more efficient than the disjoint version and has a smaller asymptotic bias. Moreover we propose a method to reduce its bias when considering sufficiently large block sizes.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/117c89e7e4dac07977137cb3320047ca150bc43d

How researchers see this paper

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

In Economics and Econometrics

In Finance

Citations in the corpus

Measures of serial extremal dependence and their estimation (2013, background): “[58], in particular [60], give historical accounts of estimation of θ and some new technology for the estimation of θ and the cluster size distribution P (ξ = k), k ≥ 1.”
MIXED CAUSAL-NONCAUSAL AR PROCESSES AND THE MODELLING OF EXPLOSIVE BUBBLES (2017, unknown): “…the choice of clustering scheme, i.e., how the sequence (ξ̂k,h (x))k is constructed, can have an impact on the estimated excess clustering: more elaborate clustering schemes could be considered (see for instance Ferro and Segers (2003) and Robert, Segers, and Ferro (2009)). using the LAD estimator.”