Second-Order Filter Distribution Approximations for Financial Time Series With Extreme Outliers
指出，对于股票收益等频繁出现极端异常值的序列，基于一阶泰勒展开的辅助粒子滤波容易失效，而基于二阶近似的滤波表现良好，并用模拟数据验证了这一点。
Particle filters are regularly used to obtain the filter distributions associated with state–space financial time series. The most common use today is the auxiliary particle filter (APF) method in conjunction with a first-order Taylor expansion of the log-likelihood. We argue that for series such as stock returns, which exhibit fairly frequent and extreme outliers, filters based on this first-order approximation can easily break down. However, an APF based on the much more rarely used second-order approximation appears to perform well in these circumstances. To detach the issue of algorithm design from problems related to model misspecification and parameter estimation, we demonstrate the lack of robustness of the first-order approximation and the feasibility of a specific second-order approximation using simulated data.