论文标题
用于离散价格变化和不规则间隔观测的GARCH模型
An Intraday GARCH Model for Discrete Price Changes and Irregularly Spaced Observations
论文作者
论文摘要
我们为高频价格开发了一种新颖的观察驱动模型。我们考虑了不规则的观察，同时交易，价格离散性和市场微观结构噪声。贸易持续时间和价格波动之间的关系以及贸易持续时间和价格波动的盘中模式，使用平滑花键捕获。动态模型基于零充气的Skellam分布，在得分驱动的框架中，随时间变化的波动率。通过包括移动平均成分来过滤市场微观结构噪声。该模型是通过最大似然法估计的。在对IBM股票的实证研究中，我们证明了该模型非常适合数据。除了建模日内波动率外，还可以用于测量每日实现的波动率。
We develop a novel observation-driven model for high-frequency prices. We account for irregularly spaced observations, simultaneous transactions, discreteness of prices, and market microstructure noise. The relation between trade durations and price volatility, as well as intraday patterns of trade durations and price volatility, is captured using smoothing splines. The dynamic model is based on the zero-inflated Skellam distribution with time-varying volatility in a score-driven framework. Market microstructure noise is filtered by including a moving average component. The model is estimated by the maximum likelihood method. In an empirical study of the IBM stock, we demonstrate that the model provides a good fit to the data. Besides modeling intraday volatility, it can also be used to measure daily realized volatility.
