Mathematics and Statistics Vol. 7(4A), pp. 41 - 48
DOI: 10.13189/ms.2019.070706
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Outlier Detection in Local Level Model: Impulse Indicator Saturation Approach

F. Z. Che Rose ^1,2,*, M. T. Ismail ², N. A. K. Rosili ^1
1 School of Computing, Faculty of Science and Technology, Quest International University Perak, Malaysia
² School of Mathematical Sciences, Universiti Sains Malaysia, Malaysia

ABSTRACT

The existence of outliers in financial time series may affect the estimation of economic indicators. Detection of outliers in structural time series framework by using indicator saturation approach has become our main interest in this study. The reference model used is local level model. We apply Monte Carlo simulations to assess the performance of impulse indicator saturation for detecting additive outliers in the reference model. It is found that the significance level, α = 0.001 (tiny) outperformed the other target size in detecting various size of additive outliers. Further, we apply the impulse indicator saturation to detection of outliers in FTSE Bursa Malaysia Emas (FBMEMAS) index. We discover that there were 14 outliers identified corresponding to several economic and financial events.

KEYWORDS
Outliers, Local Level, Indicator Saturation, Monte Carlo, Impulse Indicator Saturation, Structural Time Series

Cite This Paper in IEEE or APA Citation Styles
(a). IEEE Format:
[1] F. Z. Che Rose , M. T. Ismail , N. A. K. Rosili , "Outlier Detection in Local Level Model: Impulse Indicator Saturation Approach," Mathematics and Statistics, Vol. 7, No. 4A, pp. 41 - 48, 2019. DOI: 10.13189/ms.2019.070706.

(b). APA Format:
F. Z. Che Rose , M. T. Ismail , N. A. K. Rosili (2019). Outlier Detection in Local Level Model: Impulse Indicator Saturation Approach. Mathematics and Statistics, 7(4A), 41 - 48. DOI: 10.13189/ms.2019.070706.