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• W1902154080 abstract "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 of predicting 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." @default.
• W1902154080 created "2016-06-24" @default.
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• W1902154080 date "2007-05-01" @default.
• W1902154080 modified "2023-09-27" @default.
• W1902154080 title "Comparing Value-at-Risk Methodologies" @default.
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• W1902154080 doi "https://doi.org/10.12660/bre.v27n12007.1570" @default.
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