Commerce and Management
Permanent URI for this communityhttp://repository.kln.ac.lk/handle/123456789/140
Browse
5 results
Search Results
Item The Effect on Stock Market Volatility on Stock Prices By Using The Methods of Arch, Garch, And Egarch in Colombo Stock Exchange, Sri Lanka(Department of Finance, Faculty of Commerce and Management Studies, University of Kelaniya, Sri Lanka., 2025) Shajida, M. S. F.; Kethmi, G. A. P.Introduction: The purpose of the study is to examine the effect on stock market volatility on stock prices by using the methods of ARCH, GARCH, and EGARCH in Colombo Stock Exchange, Sri Lanka. Methodology: The researcher has chosen the S&P SL 20 Index in CSE, Sri Lanka for the study. In this regard, the secondary data was collected from the official website of CSE. Daily historical stock prices were the independent variables and forecasted stock volatility was the dependent variable. The model has been done using EViews 12 SV. 10 years span of data has been analyzed to find the most accurate forecasting model and the deviation between actual volatility and forecasted volatility. To check the accuracy between actual and the forecasted volatility, the error measurements called MAE, RMSE, MAPE, and TU were used. Findings: According to the study's findings, all three models confirmed that there is a significant relationship between actual and forecasted volatility, evident through the model's ability to capture key market patterns, including volatility clustering and persistence. While each model offers unique strengths, the ARCH model emerges as the most balanced option for general use. EGARCH is particularly useful in markets with asymmetrical responses, and GARCH provides reliable short-term forecasting but is less consistent overall. Conclusion: ARCH consistently performs well, making it the most balanced and reliable model overall. EGARCH effectively captures percentage errors and handles market asymmetries, proving useful for conditions requiring specific percentage accuracy. Finally, GARCH performs best in minimizing large deviations but falls short on other metrics, placing it behind ARCH and EGARCH in terms of consistent forecast reliability. The final result ranking emphasizes ARCH as the top choice for balanced accuracy, followed by EGARCH for specialized contexts, with GARCH providing strong but slightly less consistent performance.Item Financial Performance of SBI Mutual Funds: An Analysis(Department of Accountancy, Faculty of Commerce and Management Studies, University of Kelaniya, Sri Lanka, 2016) Baral, S.K.When we talk about mutual funds, putting all our eggs in a single basket is never a wise decision. This is due to the market volatility and the risks involved in it. But one can minimize risk by distributing his investments among various financial instruments, industries and many more options. Here the intent is to maximize returns by investing in diversified areas, where each would react differently to the same event. This not only buffers the impact of a market downturn, but also allows for more potential rewards by offering a broader exposure to various stocks and sectors. A mutual fund is a pool of money from various investors who wish to save or make money. Investing in a mutual fund can be easier than buying and selling individual stocks and bonds on our own. Investors can sell their shares when they want. The main objective of this paper is to analyze the financial performance of State Bank of India (SBI) mutual funds.Item Equity Market Volatility Behavior in Sri Lankan Context(University of Kelaniya, 2015) Morawakage, P.S.; Nimal, P.D.Colombo Stock Exchange (CSE) in Sri Lanka is at its first level of emerging markets. Volatility of emerging markets are considered to be high and characterized by complex features. Therefore, this study focusses on examining the volatility behavior of Colombo Stock Exchange with advanced econometric models. Here GARCH, EGARCH and TGARCH models are used to capture the complex volatility features. It is observed that volatility clustering and leverage effect exists in Colombo Stock Exchange. Further, negative shocks creates more volatility compared to a positive shocks generated in the market. TGARCH model assuming student-t probability distribution function is more suitable to explain the volatility in Colombo Stock Exchange among the models described above according to the Akaike and Schwarz information criteria.Item Equity Market Volatility Behavior in Sri Lankan Context(Faculty of Commerce and Management Studies, University of Kelaniya, 2015) Morawakage, P.S.; Weerasinghe, W.D.J.D.Colombo Stock Exchange (CSE) in Sri Lanka is at its first level of emerging markets. Volatility of emerging markets are considered to be high and characterized by complex features. Therefore, this study focusses on examining the volatility behavior of Colombo Stock Exchange with advanced econometric models. Here GARCH, EGARCH and TGARCH models are used to capture the complex volatility features. It is observed that volatility clustering and leverage effect exist in Colombo Stock Exchange. Further, negative shock creates more volatility compared to a positive shock generated in the market. TGARCH model assuming student-t probability distribution function is more suitable to explain the volatility in Colombo Stock Exchange among the models described above according to the Akaike and Schwarz information criteria.Item Volatility Modeling and its Impact on Risk premium in Emerging markets(2015) Morawakage, P.S.This study examines different volatility models to capture the stock market volatility in two emerging markets Indonesia and Sri Lanka. Further the relationship between volatility and risk premium in both markets are analyzed to test the risk return trade off in those markets. GARCH, EGARCH and TGARCH models are used to capture the volatility and GARCH-M model is used to analyze the risk return relationship. In both markets it is observed that volatility clustering, leverage effect and nonlinear effect are significant by considering daily ASPI return observations from 2004 to 2013. Relationship between volatility and risk premium is not significant according to the GARCH-M model.