波动率可以衡量市场风险，对其准确预测在衍生品定价、风险管理和资产配置等方面有重要意义，是政府、资本市场及投资者共同关心的热点问题。本文在介绍了现有典型的各种波动率预测模型的基础上，将表示隐含波动率的市场波动率指数（CVX）作为影响因子引入高频数据HAR模型，构成HAR-CVX模型，该模型既利用了股票交易的高频数据，又利用了期权模拟交易的信息，最大程度地综合了可以利用的信息，所以预测效果更佳。以沪深 300 指数为研究对象，将几种常用波动率预测模型（GARCH模型、SV模型和HAR模型）与所提出的HAR-CVX模型进行滚动时间窗口样本外预测，并采用4种损失函数和SPA检验，对这几种模型的预测效果进行了评估，发现基于高频数据的HAR模型表现优于基于日收益率数据的GARCH模型和SV模型，并且加入了隐含波动率的HAR-CVX模型的预测效果更好。
Volatility is used to measure the market risk, and its accuracy prediction has an direct significance on derivative pricing, risk management and asset allocation. How to understand and measure the market risk accurately is related to investment decision efficiency and economic operation, which is the hot problem of the government department, investors and security market. Currently, the option is in simulated trading in China, and various options will soon list orderly. To research the volatility of correspondence stock and predict it accurately is very important to option pricing and option trading. The research has great theoretical and practical significance for regulation of the financial risk and will promote the long-term health development of Chinese options market. In this paper, detailed descriptions of the characteristics of different volatilities, the theoretical of various volatility forecasting models are given. The Realized Volatility, which is based on high-frequency data, is chosen as a representative of the future real volatility. The HS300 index is chosen as the research object, and then the preliminary analysis of the basic characteristics of volatility is carried on. Combined with the stock market implied volatility, the market volatility index-CVX is chosen as the impact factor and added to HAR model. Then are used a new model called HAR-CVX model is gotten. Then several common volatility forecasting models, such as GARCH model, SV model, HAR model and the HAR-CVX model, to forecast the volatility of HS300 index.The prediction method we used is called the out-of-sample rolling time window forecast method. Finally, four loss functions and SPA test are used to evaluate the prediction results. It is found that prediction effect of realized volatility models which is based on high frequency trading data is better than the SV model and GARCH model which are based on the daily price data. In addition, Considering the CVX index, which contains the implied volatility, improves the prediction effect of the HAR model.
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