高频数据驱动的连续时间模型估计

现代金融经济学中连续时间模型能够更方便地描述重要经济变量的动态过程如股价、汇率和利率等。为连续时间模型提出了一种高频数据驱动的二阶段估计方法,增强了连续时间扩展模型的弹性和可操作性。以Vasicek模型为例给出了该方法的应用实例,首先在第一阶段使用实现波动率方法估计出模型的扩散项参数,然后使用实际数据的稳态分布的前向方程估计漂移项参数。此方法对模型初始设定和优化算法依赖程度低,结果较为稳定可靠。

A High-frequency Data-driven Estimation Approach of Continuous Time Series Models

In modern financial economics continuous time-series diffusion models are more convenient to deal with than discrete time models when they are used to depict important economic variables such as stock prices,exchange rates and interest rates.This paper proposes a two-stage method for continuous time-series diffusion models based on high frequency data.Compared to other computation-driven methods,our method is data-driven and very flexible in model specification. A practical example of Vasicek model estimati...