A computational study of a quasi-PORT methodology for VaR based on second-order reduced-bias estimation

• Tipo: Artigo em Revista Científica Internacional
• Data: 2012

Autores

figueiredo, f (Autor) FEP; gomes, mi (Autor) Outra; henriques-rodrigues, l (Autor) Outra; miranda, mc (Autor) Outra

Revista

Título: Journal of Statistical Computation and Simulation

Vol. 82 Nº 4

Páginas: 587-602

ISSN: 0094-9655

Editora: Taylor & Francis

Indexação

ISI Web of Knowledge - 8 Citações

ISI Web of Science

Scopus - 11 Citações

Classificação Científica

FOS: Ciências exactas e naturais > Ciências da computação e da informação

Outras Informações

ID Authenticus: P-002-GJ6

DOI: 10.1080/00949655.2010.547196

Abstract (EN): In this paper, we deal with the estimation, under a semi-parametric framework, of the Value-at-Risk (VaR) at a level p, the size of the loss occurred with a small probability p. Under such a context, the classical VaR estimators are the Weissman-Hill estimators, based on any intermediate number k of top-order statistics. But these VaR estimators do not enjoy the adequate linear property of quantiles, contrarily to the PORT VaR estimators, which depend on an extra tuning parameter q, with 0 <= q < 1. We shall here consider 'quasi-PORT' reduced-bias VaR estimators, for which such a linear property is obtained approximately. They are based on a partially shifted version of a minimum-variance reduced-bias (MVRB) estimator of the extreme value index (EVI), the primary parameter in Statistics of Extremes. Due to the stability on k of the MVRB EVI and associated VaR estimates, we propose the use of a heuristic stability criterion for the choice of k and q, providing applications of the methodology to simulated data and to log-returns of financial stocks.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 16