Outlier Detection in the Lognormal Logarithmic Conditional Autoregressive Range Model

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.