Detecting Structural Breaks and Outliers for Volatility Data via Impulse Indicator Saturation

• Published in: Studies in Systems, Decision and Control
• Main Author: Nasi I.N.M.; Ismail M.T.; Karim S.A.A.
• Format: Book chapter
• Language: English
• Published: Springer Science and Business Media Deutschland GmbH 2022
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140216176&doi=10.1007%2f978-3-031-04028-3_42&partnerID=40&md5=3890f5e93a6135734d3bf7f15d30e2eb

The existence of structural breaks and outliers are two components commonly appear in volatility data, especially in financial data. However, their presence is often treated separately using different methods. This led to inconsistent results due to different assumptions and procedures involved. The study presents simulation results based on the GARCH model to simultaneously detect structural breaks and outliers in high-frequency volatility data using the recent procedure developed by a group of researchers at Oxford University. The procedure is named impulse indicator saturation (IIS). In this study, the simulation procedure demonstrates the ability of the proposed method to capture these two components. This study is expected to eliminate the masking effects in outliers’ detection, commonly quantified as the loss of power due to more than the anticipated number of discordant observations in the sample. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.