In the last two decades, several methods for estimating Value at Risk have been proposed in the literature. Four of the most successful approaches are conditional autoregressive Value at Risk, extreme value theory, filtered historical simulation and time‐varying higher order conditional moments. In this paper, we compare their performances under both an empirical investigation using 80 assets and a large Monte Carlo simulation. From our analysis, we conclude that most of the methods seem not to imply huge numerical difficulties and, according to usual backtests and performance measurements, extreme value theory presents the best results most of the times, followed by filtered historical simulation.

中文翻译：

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某些公认的风险价值方法的广泛比较

在过去的二十年中，文献中提出了几种估算风险价值的方法。四种最成功的方法是有条件的自回归风险价值，极值理论，经过过滤的历史模拟以及随时间变化的高阶条件矩。在本文中，我们在使用80种资产进行的实证研究和大型Monte Carlo模拟下，比较了它们的表现。根据我们的分析，我们得出的结论是，大多数方法似乎并不意味着存在巨大的数值困难，并且根据通常的回测和性能测量，极值理论在大多数情况下都表现出最好的结果，然后是经过过滤的历史模拟。