Unified discrete-time and continuous-time models and statistical inferences for merged low-frequency and high-frequency financial data
Unified discrete-time and continuous-time models and statistical inferences for merged low-frequency and high-frequency financial data
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DOI：
10.1016/j.jeconom.2016.05.003
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发表时间：
2016-10
影响因子：
6.3
通讯作者：
Donggyu Kim;Yazhen Wang
中科院分区：
文献类型：
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作者：
Donggyu Kim;Yazhen Wang
本文通过在连续时间的瞬时波动率中嵌入离散时间的Gestival波动率，建立了一个统一的模型，该模型可以同时适应用于高频股票价格建模的连续时间Itô过程和用于低频股票价格建模的Gestival过程。这个模型被称为统一的GARCH-Itô模型。我们采用基于高频金融数据的已实现波动率估计和低频GARCH结构的拟似然函数来发展高频和低频组合数据的参数估计方法。我们建立了渐近理论的估计和进行模拟研究，以检查有限样本的估计性能。我们应用所提出的估计方法，美国银行的股票价格数据。
This paper introduces a unified model, which can accommodate both continuous-time Itô processes used to model high-frequency stock prices and GARCH processes employed to model low-frequency stock prices, by embedding a discrete-time GARCH volatility in its continuous-time instantaneous volatility. This model is called a unified GARCH-Itô model. We adopt realized volatility estimators based on high-frequency financial data and the quasi-likelihood function for the low-frequency GARCH structure to develop parameter estimation methods for the combined high-frequency and low-frequency data. We establish asymptotic theory for the proposed estimators and conduct a simulation study to check finite sample performances of the estimators. We apply the proposed estimation approach to Bank of America stock price data.