Outlier Detection Based on Discrete Wavelet Transform with Application to Saudi Stock Market Closed Price Series

• 발행기관: 한국유통과학회
• 저자명: Khudhayr A. RASHEDI Mohd T. ISMAIL S. Al WADI Abdeslam SERROUKH
• 간행물 정보: 『The Journal of Asian Finance, Economics and Business(JAFEB)』Vol. 7 No.12, 1~10쪽, 전체 10쪽
• 주제분류: 경제경영 > 경제학
• 파일형태: PDF
• 발행일자: 2020.12.30

영문 초록

This study investigates the problem of outlier detection based on discrete wavelet transform in the context of time series data where the identification and treatment of outliers constitute an important component. An outlier is defined as a data point that deviates so much from the rest of observations within a data sample. In this work we focus on the application of the traditional method suggested by Tukey (1977) for detecting outliers in the closed price series of the Saudi Arabia stock market (Tadawul) between Oct. 2011 and Dec. 2019. The method is applied to the details obtained from the MODWT (Maximal-Overlap Discrete Wavelet Transform) of the original series. The result show that the suggested methodology was successful in detecting all of the outliers in the series. The findings of this study suggest that we can model and forecast the volatility of returns from the reconstructed series without outliers using GARCH models. The estimated GARCH volatility model was compared to other asymmetric GARCH models using standard forecast error metrics. It is found that the performance of the standard GARCH model were as good as that of the gjrGARCH model over the out-of-sample forecasts for returns among other GARCH specifications.

목차

1. Introduction

2. Wavelets and Outlier Detections

3. The MODWT Transform

4. Model Fitting

5. Forecasting

6. Conclusion

References

키워드

MODWT Wavelets Transform Saudi Arabia Stock Market Outlier Detections GARCH Models