Outlier Detection in the Lognormal Logarithmic Conditional Autoregressive Range Model
提出了一种在对数正态对数条件自回归极差模型中检测异常值的方法，蒙特卡洛模拟显示该方法具有良好的检验水平和功效，且能减少多个异常值导致的掩蔽效应，实证表明可有效检测波动率异常值并提高预测精度。
Abstract An outlier detection procedure in the lognormal logarithmic conditional autoregressive range (lognormal Log‐CARR) model is proposed. The proposed test statistic is demonstrated to be well‐sized and to have good power using Monte Carlo simulations. Furthermore, the outlier detection procedure suffers less from the masking effect caused by multiple outliers. The results of an empirical investigation show that the proposed method can effectively detect volatility outliers and improve forecasting accuracy.