Abstract
GARCH models have been commonly used to capture volatility dynamics in financial time series. A key assumption utilized is that the series is stationary as this allows for model identifiability. This however violates the volatility clustering property exhibited by financial returns series. Existing methods attribute this phenomenon to parameter change. However, the assumption of fixed model order is too restrictive for long time series. This paper proposes a change-point estimator based on Manhattan distance. The estimator is applicable to GARCH model order change-point detection. Procedures are based on the sample autocorrelation function of squared series. The asymptotic consistency of the estimator is proven theoretically.
Highlights
Modelling volatility of financial asset returns is an important area in Finance
This paper proposes a change-point estimator based on Manhattan distance
The estimator is applicable to GARCH model order change-point detection
Summary
Modelling volatility of financial asset returns is an important area in Finance. Given the changing pace of the underlying economic mechanism and technological progress, modeling economic processes over a long time horizon, it is possible that structural changes may occur This can cause the time series to deviate from stationarity and result to volatility clustering. A key assumption of the GARCH models used is that the process is stationary as this allows for model identifiability This violates the volatility clustering property exhibited by the financial returns series. This phenomenon is manifested by the fact that the absolute value of returns or their squares display a positive, significant and slowly decaying autocorrelation function despite the fact that the returns are uncorrelated This indicates that modeling financial returns series over long time horizons deviates from the stationarity assumption suggesting the existence of a change-point in the series.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.