Commerce and Management

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    Impact of Exchange Rate Volatility on Sri Lanka’s Trade Growth
    (Faculty of Commerce and Management Studies, University of Kelaniya, Sri Lanka, 2016) AL Soos, M.Y.M.; Madurapperuma, M.W.
    The exchange rate regime and foreign policy is an important measure of the macroeconomic management in endeavoring for economic development through improving the performance of export of the country. Many scholars pay their attention to study the impact of exchange rate volatility on the export growth. However, these studies do not provide a definite result that increased uncertainty has reduced exports, the majority of empirical research have found that there is a negative relationship between exchange rate volatility and export performance. The conclusion drawn from recent empirical literature is insignificant between export and exchange rate volatility. Very few studies found significant relationships between export and exchange rate volatility. Therefore, the purpose of this study is to investigate the impact of exchange rate volatility on exports in Sri Lanka. The research used quarterly data during the period 2000 to 2015. Data of this study was analyzed using cointegration, vector error correction model (VECM) and GARCH techniques. Findings of this study show that the presence of a unique cointegrating vector linking real exports, relative export prices, real exchange rate volatility in the long run. Real exchange rate volatility exerts significant negative effects on exports both in the short run and the long run. Further findings of this study show that real exchange rate impact positively on export. Overall, findings of these results show that trading activities of Sri Lanka can be improved by maintaining a stable competitive real exchange rate.
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    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.
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    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.
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    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.