Outlier Detection in the Lognormal Logarithmic Conditional Autoregressive Range Model
19 Pages Posted: 20 Jan 2016
Date Written: February 2016
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.
Suggested Citation: Suggested Citation
Chiang, Min-Hsien and Chou, Ray Y. and Wang, Li‐Min, Outlier Detection in the Lognormal Logarithmic Conditional Autoregressive Range Model (February 2016). Oxford Bulletin of Economics and Statistics, Vol. 78, Issue 1, pp. 126-144, 2016, Available at SSRN: https://ssrn.com/abstract=2718671 or http://dx.doi.org/10.1111/obes.12081
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