Outlier Detection in the Lognormal Logarithmic Conditional Autoregressive Range Model

19 Pages Posted: 20 Jan 2016

See all articles by Min-Hsien Chiang

Min-Hsien Chiang

National Cheng Kung University - Institute of International Business

Ray Y. Chou

Academia Sinica

Li‐Min Wang

affiliation not provided to SSRN

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

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

Min-Hsien Chiang (Contact Author)

National Cheng Kung University - Institute of International Business ( email )

1 University Rd.
Tainan, 70101
Taiwan

Ray Y. Chou

Academia Sinica ( email )

128 Academia Road, Section 2
Nankang
Taipei, 11529
Taiwan

Li‐Min Wang

affiliation not provided to SSRN

No Address Available

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