Abstract:
Heteroscedasticity arises when the error term of a regression equation does not have a constant variance. Financial markets are known to be very uncertain a phenomenon called volatility which is a key variable used in many financial applications such as investment, portfolio construction, option pricing and hedging as well as market risk management. This study models the heteroscedasticity of volatility of stock returns in Nairobi Stock Exchange(NSE) of Safaricom and Kenya Commercial Bank(KCB) using daily return series from 9th June 2008, to 31st December, 2010, using ARIMA- GARCH models. All the return series exhibit, leptokurtosis, volatility clustering and negative skewness. The estimation results reveal that ARIMA (1, 0, 0)-GARCH (1, 1) and ARIMA (0, 0, 2)-GARCH (1, 1) best fits Safaricom and KCB respectively. Investors who wish to avoid large, erratic swings in portfolio returns may wish to structure their investments to produce a leptokurtic distribution. Further, researches should focus on the calculation of value-at-risk (VaR) in the markets.
