We forecast monthly value at risk (VaR) and conditional value at risk (CVaR) using option market data and four different econometric techniques. Independent from the econometric approach used, all models produce quick to estimate forward-looking risk measures that do not depend from the amount of historical data used and that, through the implied moments of options, better reflect the ever-changing market scenario. All proposed option-based approaches outperform or are equally good to different “traditional” forecasts that use historical returns as input. The extensive robustness of our results shows that the real driver of the better forecasts is the use of option market data as inputs for the analysis, more than the type of econometric approach implemented.

中文翻译：

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使用期权市场数据预测风险价值和条件风险价值

我们使用期权市场数据和四种不同的计量经济学技术预测月度风险价值 (VaR) 和条件风险价值 (CVaR)。独立于所使用的计量经济学方法，所有模型都可以快速估计前瞻性风险度量，这些度量不依赖于所使用的历史数据量，并且通过期权的隐含时刻，更好地反映了不断变化的市场情景。所有提议的基于期权的方法都优于或同样适用于使用历史回报作为输入的不同“传统”预测。我们结果的广泛稳健性表明，更好预测的真正驱动力是使用期权市场数据作为分析的输入，而不是所实施的计量经济学方法类型。