Browsing by Author "Islam, Ali S."

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    Efficiency Evaluation When Modelling Nairobi Security Exchange Data Using Bilinear and Bilinear-Garch (Bl-Garch) Models
    (2012-06) Wagala, Adolphus; Islam, Ali S.; Nassiuma, Dankit K.
    In this paper, the weekly returns of the Nairobi Securities Market (NSE) are modelled using bilinear models and the bilinear-GARCH models so as to determine the most efficient and adequate model for forecasting of the Nairobi Equity market. The data used was obtained from the Nairobi Stock Exchange (NSE) for the period between 3rd June 1996 to 31st 30th October 2011for the company share prices while for the NSE 20-share index was for period between 2nd March 1998 to 30th October 2011.The share prices for three companies; Bamburi Cement, National Bank of Kenya and Kenya Airways which were selected at random from each of the three main sectors as categorized in the Nairobi Stock Exchange were used. The results indicate that the combination of bilinear-GARCH model is more adequate and efficient in modelling the weekly returns of the Nairobi Securities Exchange.
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    Volatility Modelling of the Nairobi Securities Exchang
    (2012-03) Wagala, Adolphus; Nassiuma, Dankit K.; Islam, Ali S.; Mwangi, Jesse W.
    In this paper we identify the most efficient ARCH-type model that can be applied to the Nairobi stock exchange data for forecasting and prediction of volatility which in turn is important in pricing financial derivatives, selecting portfolios, measuring and managing risks more accurately. The establishment of an efficient stock market is indispensable for an economy that is keen on utilizing scarce capital resources to achieve its economic growth. The purpose of this study was to determine the most efficient model from the symmetric and the asymmetric GARCH models. The models were evaluated by use of the Shwartz Bayesian Criteria (SBC), Akaike Information Criteria (AIC) and the Mean Squared Error (MSE). The results show that the AR-Integrated GARCH (IGARCH) models with student’s t-distribution are the best models for modelling volatility in the Nairobi Stock Market data.