Please use this identifier to cite or link to this item: https://www.um.edu.mt/library/oar/handle/123456789/140040
Title: Stock market volatility and the COVID-19 pandemic in Sri Lanka
Other Titles: VUCA and other analytics in business resilience, part A
Authors: Riyath, Mohamed Ismail Mohamed
Dewasiri, Narayanage Jayantha
Siraju, Mohamed Abdul Majeed Mohamed
Grima, Simon
Mustafa, Abdul Majeed Mohamed
Keywords: Stock exchanges -- Sri Lanka
Stock price forecasting -- Sri Lanka
COVID-19 Pandemic, 2020-2023 -- Economic aspects -- Sri Lanka
Financial crises -- Sri Lanka
Heteroscedasticity
Issue Date: 2024
Publisher: Emerald Publishing Limited
Citation: Riyath, M. I. M., Dewasiri, N. J., Siraju, M. A. M. M., Grima, S., & Mustafa, A. M. M. (2024). Stock market volatility and the COVID-19 pandemic in Sri Lanka. In D. Singh, K. Sood, S. Kautish, & S. Grima (Eds.), VUCA and other analytics in business resilience, part A (pp. 151–168). Leeds: Emerald Publishing.
Abstract: Purpose: This chapter examines the effect of COVID-19 on the stock market volatility (SMV) in the Colombo Stock Exchange (CSE), Sri Lanka.
Need for the Study: The study is necessary to understand investor behaviour, market efficiency, and risk management strategies during a global crisis.
Methodology: Utilising daily All Share Price Index (ASPI) data from 2 January 2018 to 31 August 2021, the data are divided into subsamples corresponding to the pre-pandemic period, the pandemic period, and distinct waves of the pandemic. The impact of the pandemic is investigated using the Mann–Whitney U test, the Kruskal–Wallis test, and the Exponential Generalised Autoregressive Conditional Heteroscedasticity (EGARCH) model.
Findings: The pandemic considerably affected CSE – the Mann–Whitney U test produced different market returns during the pre-COVID and COVID eras. The Kruskal–Wallis test improved performance during COVID-19 but did not continue to do so across COVID-19 waves. The EGARCH model detected increased volatility and risk during the first wave, but the second and third waves outperformed the first. COVID-19 had a minimal overall effect on CSE market results. GARCH and Autoregressive Conditional Heteroskedasticity (ARCH) models identified longterm variance memory and volatility clustering. The News Impact Curve (NIC) showed that negative news had a more significant impact on market return volatility than positive news, even if the asymmetric term was not statistically significant.
Practical Implications: This study offers significant insight into how Sri Lanka’s SMV is affected by COVID-19. The findings help create efficient mitigation strategies to mitigate the negative consequences of future events.
URI: https://www.um.edu.mt/library/oar/handle/123456789/140040
Appears in Collections:Scholarly Works - FacEMAIns

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