High quantiles estimation with Quasi-PORT and DPOT:an application to value-at-risk for financial variables

• Autor(a) principal: Santos, Paulo Araújo
• Data de Publicação: 2013
• Outros Autores: Alves, Isabel Fraga, Hammoudeh, Shawkat
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10400.15/2978
• topic: Financial time series; High quantiles; Quantitative risk management; Statistics of extremes

Recurrent “black swans” financial events are a major concern for both investors and regulators because of the extreme price changes they cause, despite their very low probability of occurrence. In this paper, we use unconditional and conditional methods, such as the recently proposed high quantile (HQ) extreme value theory (EVT) models of DPOT (Duration-based Peak Over Threshold) and quasi-PORT (peaks over random threshold), to estimate the Value-at-Risk with very small probability values for an adequately long and major financial time series to obtain a reasonable number of violations for backtesting. We also compare these models and other alternative strategies through an out-of-sample accuracy investigation to determine their relative performance within the HQ context. Policy implications relevant to estimation of risk for extreme events are also provided.