Liquidity fluctuations and the latent dynamics of price impact
提出一个高频动态线性价格冲击模型，将价格冲击系数分解为日、日内和自回归随机成分，用纳斯达克2016年订单簿数据估计，发现模型能解释82%的价格变化方差，且优于静态估计。
Market liquidity is a latent and dynamic variable. We propose a dynamical linear price impact model at high frequency in which the price impact coefficient is a product of a daily, a diurnal, and an auto-regressive stochastic intraday component. We estimate the model using a Kalman filter on order book data for stocks traded on the NASDAQ in 2016. We show that our price changes estimates, conditional on order flow imbalance, on average 82% of real price changes variance. Evidence is also provided on the fact that the conditioning on filtered information improves the estimate of the LOB liquidity with respect to the one obtained from a static estimation of the price impact. In addition, an out-of-sample analysis shows that our model provides a superior out-of-sample forecast of price impact with respect to historical estimates.