Modeling long-term volatility memory dynamics in the Colombo Stock Exchange
Year: 2025
Type: article
Abstract: Purpose This study examines the long-term volatility memory dynamics of the Colombo Stock Exchange by comparing the behaviors of the All Share Price Index (ASPI) and the S&P SL20 Index under recent economic scenarios. Design/methodology/approach The paper uses daily stock index data for the ASPI and the S&P SL20 between January 2012 and April 2024 and divides it into three periods: normal, COVID-19 and economic crisis. The ARMA-FIGARCH model captures the long-memory properties of volatility. Findings The analysis finds extensive clustering and persistence of return volatility in the ASPI, even after the subsidence of COVID-19, with strong long-memory dependence in all periods. There is, however, significant volatility clustering in the S&P SL20 during the COVID-19 period, although it retains weaker long-memory effects. Volatility shocks have lasting impacts on the market, with the ASPI being more susceptible to economic fluctuations than the S&P SL20. Practical implications The findings have practical implications that dictate considering long-term volatility patterns in risk management to optimize portfolio performance. This will further help policymakers improve regulatory systems, which, in effect, look into risk management to ensure market stability. Originality/value This is the first study that applied the FIGARCH model to explain the volatility dynamics of the Sri Lankan stock market over a long period. The subperiod analysis gives out real-world market behavior under different economic conditions, which could be used for contributions toward academic literature and practical financial market strategies.
Author: Mohamed Ismail Mohamed Riyath
Institution: South Eastern University of Sri Lanka
Language: English
Subfield: Finance
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W4408872054