Reduced-bias and partially reduced-bias mean-of-order-p value-at-risk estimation: a Monte-Carlo comparison and an application

• Autor(a) principal: Gomes, M. Ivette
• Data de Publicação: 2020
• Outros Autores: Caeiro, Frederico, Figueiredo, Fernanda, Hneriques-Rodrigues, Lígia, Pestana, Dinis
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10174/32928; https://doi.org/10.1080/00949655.2020.1746787
• topic: Bias reduction; heuristic methods; heavy right-tails; semi-parametric estimation; statistics of extremes; value-at-risk estimation; Monte-Carlo simulation

On the basis of a sample of either independent, identically distributed or possibly weakly dependent and stationary random variables from an unknown model F with a heavy right-tail function, and for any small level q, the value-at-risk (VaR) at the level q, i.e. the size of the loss that occurs with a probability q, is estimated by new semi-parametric reduced-bias procedures based on the mean-of-order-p of a set of k quotients of upper order statistics, with p an adequate real number. After a brief reference to the asymptotic properties of these new VaR-estimators, we proceed to an overall comparison of alternative VaR-estimators, for finite samples, through large-scale Monte-Carlo simulation techniques. Possible algorithms for an adaptive VaR-estimation, an application to financial data and concluding remarks are also provided.