Detection of outliers in the volatility of Malaysia shariah compliant index return: The impulse indicator saturation approach
Financial time series data often affected by various unexpected events which known as the outliers. The aim of this study is to detect the outliers in high frequency data using Impulse Indicator Saturation approach (IIS). Monte Carlo simulations illustrate the ability of IIS to detect outliers by us...
| Published in: | ASM Science Journal |
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| Main Author: | |
| Format: | Article |
| Language: | English |
| Published: |
Akademi Sains Malaysia
2020
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| Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087351526&doi=10.32802%2fasmscj.2020.sm26%281.7%29&partnerID=40&md5=615550441de7ac42d7620b2c8ea3301b |
| Summary: | Financial time series data often affected by various unexpected events which known as the outliers. The aim of this study is to detect the outliers in high frequency data using Impulse Indicator Saturation approach (IIS). Monte Carlo simulations illustrate the ability of IIS to detect outliers by using data with various simulation settings. For empirical application, we have chosen the Malaysia Shariah compliant index which is the FBM EMAS Shariah (FBMS) index. The result of this study discovered the presence of 47 outliers which related to several global events such as global financial crisis (2008 & 2009), the falling of stock market (2011), the United States debt-ceiling crisis (2013) and the declination of international crude oil prices (2014). © 2020, Akademi Sains Malaysia. |
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| ISSN: | 18236782 |
| DOI: | 10.32802/asmscj.2020.sm26(1.7) |