Outlier Detection in the Lognormal Logarithmic Conditional Autoregressive Range Model

Min Hsien Chiang; Ray Yeutien Chou; Li Min Wang

doi:10.1111/obes.12081

Outlier Detection in the Lognormal Logarithmic Conditional Autoregressive Range Model

Min Hsien Chiang, Ray Yeutien Chou, Li Min Wang

Institute of International Business

Research output: Contribution to journal › Article › peer-review

8 Citations (Scopus)

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.

Original language	English
Pages (from-to)	126-144
Number of pages	19
Journal	Oxford Bulletin of Economics and Statistics
Volume	78
Issue number	1
DOIs	https://doi.org/10.1111/obes.12081
Publication status	Published - 2016 Feb 1

All Science Journal Classification (ASJC) codes

Statistics and Probability
Social Sciences (miscellaneous)
Economics and Econometrics
Statistics, Probability and Uncertainty

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10.1111/obes.12081

Cite this

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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.",

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Outlier Detection in the Lognormal Logarithmic Conditional Autoregressive Range Model

Abstract

All Science Journal Classification (ASJC) codes

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