• 论文 •

### 交易信息、跳跃发现与波动率估计

• 出版日期:2017-08-15 发布日期:2017-08-25

### Trading Data, Jump Detection and Estimation of Integrated Volatility

Wu Ben & Zhang Bo

• Online:2017-08-15 Published:2017-08-25

Abstract: When estimating integrated volatility of a financial asset, the impacts of market microstructure noise and jumps should be taken into consideration in the research of high-frequency financial data. This paper proposes a Gaussian mixture model based on the market microstructure noise partially expressed as a parametric function of trading data and the jump characteristics of the asset returns series. A new estimator of integrated volatility is put forward after the jumps of the assets prices are identified while EM algorithm is applied to estimate the parameters of noise. The model put forward in this paper could be regarded as an improvement of Li et al. (2016), with a better result in simulation study, and is able to perform well in detecting the jumps even when the distribution of jump range was set by mistake. At the end, in a practical example, In comparison with Lee and Mykland (2008), the model has been justified in terms of its reliability in detecting the jumps.