A Statistical Analysis of the Colombo Stock Returns

Zhang, Zili; Nadarajah, Saralees

doi:10.1177/0972150918797196

Cited by 1 publication

(2 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(2022), Jahufer (2021), and Jegajeevan (2012. Study findings further contribute to the findings of Zhang and Nadarajah (2018) in expanding the empirical evidence on ARMA-GARCH applications in market index volatility determination.…”

Section: Modelsupporting

confidence: 53%

“…Kamal and Haque (2016) modeled the volatility of the Indian, Bangladesh and Sri Lankan stock markets in ARMA-GARCH to determine the fact that in the South Asian markets' volatility subsided promptly post crisis. Locally, Zhang and Nadarajah (2018) analysed CSE returns from 1985 to 2017 to conclude that the ARMA-APARCH modelling is efficient in forecasting volatility of the Sri Lankan market based on its log likelihood. Consequently, a limited ARMA-GARCH approach on predicting stock market volatility can be identified in the Sri Lankan context.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Sri Lankan Stock Market Volatility Analysis: An Arma- Garch Approach

Samarawickrama I.D.W.,

Pallegedara A.

2023

SLJBE

View full text Add to dashboard Cite

Beyond its role in capital generation, a stock market is emulated as a facet in economic development indication. Sri Lankan stock market, the Colombo Stock Exchange (CSE) is a languidly developing market known for instability and periodical fluctuations increasing the volatility risk for the investors. Toward market development, it is imperative in attracting and retaining long term investors. Thus, the study aimed to identify the dynamics of the CSE through volatility estimation of the Sri Lankan stock market during a high volatile period. Further, the use of ARMA-GARCH models aims to contribute to the local empirical studies on the applicability of ARMA-GARCH models in the Sri Lankan context. The study used the daily closing prices of the All-Share-Price Index (ASPI) from January 2018 to December 2022 in log return volatility. Owing to the non-normality and serial dependence conditions inherent in the data, the study developed an ARMA (2,2) mean equation and separate volatility equation applying symmetric models of GARCH, and TGARCH and asymmetric GARCH models of EGARCH, and GJR-GARCH. The study findings identified that asymmetric GARCH models are more reliable in volatility estimation and forecasting. Further, ASPI indicated a leverage effect where negative information caused idiosyncratic volatility. Keywords: ARMA-GARCH, GARCH, Leverage Effect, Stock Markets, Volatility

show abstract

Section: Modelsupporting

confidence: 53%

Section: Literature Reviewmentioning

confidence: 99%