Publicação
High quantiles estimation with Quasi-PORT and DPOT:an application to value-at-risk for financial variables
| Resumo: | 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. |
|---|---|
| Autores principais: | Santos, Paulo Araújo |
| Outros Autores: | Alves, Isabel Fraga; Hammoudeh, Shawkat |
| Assunto: | Financial time series High quantiles Quantitative risk management Statistics of extremes |
| Ano: | 2013 |
| País: | Portugal |
| Tipo de documento: | artigo |
| Tipo de acesso: | acesso restrito |
| Instituição associada: | Instituto Politécnico de Santarém |
| Idioma: | inglês |
| Origem: | Repositório Científico do Instituto Politécnico de Santarém |
| Resumo: | 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. |
|---|