Comparing value-at-risk methodologies

• Autor(a) principal: Lima, Luiz Renato Regis de Oliveira
• Data de Publicação: 2006
• Outros Autores: Neri, Breno de Andrade Pinheiro
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional do FGV (FGV Repositório Digital)
• Texto Completo: http://hdl.handle.net/10438/906
• topic: Time series; Value-at-risk; Quantile regression; Economia; Economia; Administração de risco; Mercados financeiros futuros

In this paper, we compare four different Value-at-Risk (V aR) methodologies through Monte Carlo experiments. Our results indicate that the method based on quantile regression with ARCH effect dominates other methods that require distributional assumption. In particular, we show that the non-robust methodologies have higher probability to predict V aRs with too many violations. We illustrate our findings with an empirical exercise in which we estimate V aR for returns of S˜ao Paulo stock exchange index, IBOVESPA, during periods of market turmoil. Our results indicate that the robust method based on quantile regression presents the least number of violations.