Value-at-risk estimation and the PORT mean-of-order-p methodology (2017)
- Authors:
- Autor USP: RODRIGUES, LIGIA CARLA PINTO HENRIQUES JORGE - IME
- Unidade: IME
- Assunto: ESTATÍSTICA
- Keywords: asymptotic behaviour; heavy tails; high quantiles; mean-of-order-p estimation; MonteCarlo simulation; PORT methodology; semi-parametric methods; statistics of extremes; value-at-risk
- Language: Inglês
- Imprenta:
- Source:
- Título do periódico: Revstat Statistical Journal
- ISSN: 1645-6726
- Volume/Número/Paginação/Ano: v. 15, n. 2, p. 187-204, 2017
-
ABNT
FIGUEIREDO, Fernanda e GOMES, M. Ivette e HENRIQUES-RODRIGUES, Lígia. Value-at-risk estimation and the PORT mean-of-order-p methodology. Revstat Statistical Journal, v. 15, n. 2, p. 187-204, 2017Tradução . . Disponível em: https://www.ine.pt/revstat/pdf/rs170202.pdf. Acesso em: 19 abr. 2024. -
APA
Figueiredo, F., Gomes, M. I., & Henriques-Rodrigues, L. (2017). Value-at-risk estimation and the PORT mean-of-order-p methodology. Revstat Statistical Journal, 15( 2), 187-204. Recuperado de https://www.ine.pt/revstat/pdf/rs170202.pdf -
NLM
Figueiredo F, Gomes MI, Henriques-Rodrigues L. Value-at-risk estimation and the PORT mean-of-order-p methodology [Internet]. Revstat Statistical Journal. 2017 ; 15( 2): 187-204.[citado 2024 abr. 19 ] Available from: https://www.ine.pt/revstat/pdf/rs170202.pdf -
Vancouver
Figueiredo F, Gomes MI, Henriques-Rodrigues L. Value-at-risk estimation and the PORT mean-of-order-p methodology [Internet]. Revstat Statistical Journal. 2017 ; 15( 2): 187-204.[citado 2024 abr. 19 ] Available from: https://www.ine.pt/revstat/pdf/rs170202.pdf - Location-invariant reduced-bias tail index estimation under a third-order framework
- Bootstrap methods in statistics of extremes
- Swimming performance index based on extreme value theory
- Resampling-based methodologies in statistics of extremes: environmental and financial applications
- Adaptive estimation for light-tailed models
- Corrected-Hill versus partially reduced-bias value-at-risk estimation
- Competitive estimation of the extreme value index
- Mean-of-order-p value-at-risk estimation: a Monte-Carlo comparison
- Reduced‐bias kernel estimators of a positive extreme value index
- PORT estimation of parameters of extreme events through generalized means
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