随着波动率衍生品市场的快速发展，如何准确有效地对其进行定价成为研究者和市场参与者广泛关注的重要问题。针对既有研究在模型选择上集中于连续时间模型、在数据频率上集中于以"日数据"为代表的低频数据的局限，本项目应用最新的波动率建模成果，从经典离散时间模型与已实现测度相结合这一新的角度，研究高频数据信息应用于波动率衍生品定价的理论和应用问题。主要研究内容涉及基于高频数据信息的波动率建模方法、风险中性化方式与模型的风险中性动态性质、代表性波动率衍生品的解析定价公式、参数的稳健估计方法等，并通过实际数据对理论结果进行实证。除了解决传统定价模型中难以对标的物(物理测度)动态性质和衍生品(风险中性)动态性质同时建模的问题、为波动率衍生品提供新的定价方法以外，本项目还为对风险溢价的理解和建模、考察高频数据信息的经济价值等金融和计量理论问题提供新的途径，并最终为我国相关市场和产品的推出提供技术基础。

With the rapid development of derivatives' market, how to accurately and effectively pricing volatility derivatives becomes an important issue to researchers as well as market participates. Current researches focused on continuous time model with daily level low frequency data.In view of those limitations and utilizing the latest innovation in volatility modeling, this project investigates the theoretical and practical issues in pricing volatility derivatives with information from high frequency data by combining realized measures with classical discrete time model.From this new perspective, main research topics in this project are: modeling volatility with high frequency data in a pricing-friendly model, risk neutralization and model's dynamic properties under risk neutral probability, analytical pricing formula for typical volatility derivatives, robust parameter estimation methods and empirical tests of main theorical results with market data. Besides a new pricing method for volatility derivatives and possible solution to reconcile the underling's (physical measure) dynamics with derivatives' (risk neutral measure) dynamics, this project also casts new light on issues in finance and econometrics such as modeling and understanding variance risk premia, evaluating the economic value of information from high frequency data. Results also provide technical fundations upon which the related markets and products can be opend and launched in China.