General Management

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Author: search.filters.author.Peiris, T.U.I.Has files: No

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    Measuring Stock Market Volatility in an Emerging Economy
    (2011) Peiris, T.U.I.; Peiris, T.S.G.
    The pattern of volatility in a given time series is due to various micro and macro economic factors attached to that security. An understanding of volatility and its causes is important in determining the cost of capital of the security and in assessing investment and leverage decisions in case of emerging economies especially where the market consists of risk?averse investor. This study thus examines the volatility of different sectors in Colombo Stock Exchange (CSE) and how the macro economic factors affect on the volatility by fitting Autoregressive Conditional Heteroskedasticity (ARCH) and the Generalized ARCH (GARCH) using monthly time series data of 20 sectors in CSE for the period 2005-2010. Results found that sixteen out of twenty sectors in CSE has a significance volatile (p<0.05) and both ARCH and GARCH terms on the fitted models for individual sectors were significant (p<0.05). The volatility of composite stock returns of volatile sectors was then regressed against Narrow Money Supply (M1), Broad Money Supply (M2), Inflation (I) and Interest Rate (IR). It was found that inflation and interest rate are the two significantly influencing macro economic factors (p<0.05) on the stock market volatility of emerging economies like Sri Lanka.
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    Measuring Volatility Co-Movement: An Empirical Investigation in North America, Europe and Asia Capital Markets
    (2011) Peiris, T.U.I.; Dayaratne, D.A.I.
    The interdependence of global economy now extends across a far broader range of countries than ever before. An understanding of global volatility co-movement in between stock exchanges is important hence substantial changes in volatility of financial markets are capable of having significant negative effects on risk-averse investors. Thus, this study attempts to identify the co-movement of stock market volatility among the regions North America, Europe and Asia. The econometric models of Autoregressive Conditional Heteroskedasticity (ARCH), Generalized ARCH (GARCH), Johansen Cointegration Test, Vector Autoregression and VAR Variance Decompositions are employed. Daily returns of the market portfolio of these countries are used for the investigation. The period of study is 2000 to 2010 which represents current crisis movements of these markets. The empirical results indicate that the volatility co-movement is not that momentous since in all the markets more than 97% of the forecast error variance is explained by the market itself. However, the volatility co-movement between Sri Lanka stock market with that of India, Japan, Hong Kong, Singapore, UK and US stood in declining order of volatility co-movement respectively. Singapore and India are found to be the most endogenous markets with almost 03% of their forecast error variance is explained by the other markets under study.