利用期权市场数据预测风险价值和条件风险价值

Annalisa Molino, Carlo Sala
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引用次数: 5

摘要

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,所有模型都产生快速估计前瞻性风险的措施,而不依赖于所使用的历史数据的数量,并且通过隐含的期权时刻,更好地反映不断变化的市场情景。所有提出的基于期权的方法都优于或同样优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,更好的预测的真正驱动因素是使用期权市场数据作为分析的输入,而不是采用计量经济学方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Forecasting Value at Risk and Conditional Value at Risk using Option Market Data
We forecast monthly Value at Risk (VaR) and Conditional Value at Risk (CVaR) using option market data and four different econometric techniques. Independently 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.
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