基于期权与高频数据信息的VaR度量研究

传统的市场风险度量模型没有充分利用期权与高频数据包含的信息，且主要基于单因子波动率模型，导致信息的损失以及模型缺乏足够的灵活性.本文基于灵活的双因子随机波动率模型，通过提取期权与高频数据包含的市场前瞻与当前信息，构建相应的市场风险度量波动率模型对在险值（VaR）进行度量.为了估计模型参数，建立基于连续粒子滤波的极大似然估计方法.采用iVX指数与已实现波动率测度（RV）作为上证50ETF期权与高频数据信息的代理，对构建的市场风险度量波动率模型进行了实证检验，结果表明：充分利用了期权与高频数据信息的双因子随机波动率模型能够在快速变化的市场环境中更好地估计波动率，相比其它波动率模型（仅利用了历史数据信息的GARCH模型、利用了高频数据信息的已实现GARCH模型以及利用了期权与高频数据信息的单因子随机波动率模型）具有更为优越的VaR度量精确性，尤其是极端风险情形下的VaR估计精确性改进明显，凸显了期权与高频数据信息以及双因子波动率在市场风险管理中的价值.

Measuring VaR Based on the Information Content of Option and High-frequency Data

Value-at-Risk (VaR), is a widely used measure of financial market risk, which is conceptually simple and intuitive. Accurate measurement of VaR is important for financial risk managers and regulators. Traditionally, the measurement of market risk (VaR) doesn't use the information content of option and high-frequency data fully, and mainly based on the single-factor volatility models such as the GARCH and stochastic volatility models, which leads to the loss of information and the lack of flexibility of the model.With the rapid development of derivative (option) markets, the option data become readily available. The option prices reflect market sentiment and/or investors' expectations about future stock market volatility. And a growing body of research has found that option data contain important (forward-looking) information for volatility forecasting and risk measurement. At the same time, with the advanced computers and communications technology, high-frequency financial data are now widely available, which usually summarized in terms of realized volatility measure, providing much more detailed information about the current level of volatility.Motivated by the above interpretation, by extracting the forward-looking and current information of option and high-frequency data based on a flexible two-factor stochastic volatility model, a market risk measurement volatility model is proposed for measuring VaR. To estimate the model parameters, the continuous particle filters-based maximum likelihood estimation method is developed. Using iVX index and realized volatility measure as the proxies of the Shanghai 50ETF option and high-frequency data, an empirical study for the proposed market risk measurement volatility model is presented. The results show that the two-factor stochastic volatility model incorporated with option and high-frequency information can provide more accurate volatility estimates even in the rapidly changing market environment, which leads to more accurate VaR estimates than other volatility models, including the GARCH model incorporated with only historical information, the realized GARCH model incorporated with high-frequency information and the single-factor stochastic volatility model incorporated with option and high-frequency information. In particular, the proposed model can improve the accuracy of VaR estimates more significantly over others under the extreme risk condition.The empirical results highlight the values of the information content of option and high-frequency data and the two-factor volatility in market risk management. And our model provides an efficient and promising tool to measuring VaR.