基于高频极值数据的波动率建模与预测

本文系统性地研究了三种类别的高频极值数据下波动率的建模与预测，即分别基于高频收盘价数据、高频高低价数据、以及高频"开高低收"数据，讨论并完善了相应的连续价格假设下的估计量和跳跃价格假设下的估计量的理论性质，并将基于高频极值数据的各类估计方法统一地扩展为相应的动态预测模型，通过对上证指数及其他几种主要指数的高频数据进行实证分析，揭示出充分地利用高频极值数据信息可以显著地提高波动率的模型拟合效果和样本外动态预测能力.

Volatility modeling and forecasting based on high frequency extreme value data

This paper systematically studies the modeling and forecasting of volatility under three types of high-frequency extreme value data, i.e. high-frequency closing price data, high-frequency high-low price data, and high-frequency OHLC data, based on which the theoretical properties of corresponding estimators under continuous price assumptions and under price jump assumptions are discussed and refined, and these estimation methods are uniformly extended to the corresponding dynamic forecasting models. Through the empirical analysis based on the high-frequency data of the Shanghai Stock Index and other major indexes, it reveals that sufficiently utilizing high-frequency extreme data information can significantly improve the model fitting ability and dynamic forecasting ability of volatility.