Smooth and Abrupt Dynamics in Financial Volatility: The MS‐MEM‐MIDAS*
提出一种结合混频数据与马尔可夫动态的乘法误差模型，用于刻画已实现波动率的高频与低频平滑突变特征，在标普500指数数据中表现优于其他模型，尤其有助于预测波动率的快速爆发与冲击吸收。
In this paper, we maintain that the evolution of the realized volatility is characterized by a combination of high‐frequency dynamics and smoother, yet persistent, dynamics evolving at a lower frequency. We suggest a new Multiplicative Error Model which combines the mixed frequency features of a MIDAS at the monthly level with Markovian dynamics at the daily level. When estimated in‐sample on the realized kernel volatility of the S&P500 index, this model dominates other simpler specifications, especially when monthly aggregated realized volatility is used. The same pattern is confirmed in the out‐of‐sample forecasting performance which suggests that adding an abrupt change in the average level of volatility better helps in tracking quick bursts of volatility and a relatively rapid absorption of the shocks.