Modelling of the Nairobi Stock Data using ARCH and Bilinear Models
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The purpose of this study was to determine the most efficient model between the two models namely, ARCH and bilinear models when applied to stock market data. The data was obtained from the Nairobi Stock Exchange (NSE) for the period between 3 rd June 1996 to 31 st December 2007 for the company share prices while for the NSE 20-share index data was for period between 2 nd March 1998 to 31 st December 2007.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 ...
The purpose of this study was to determine the most efficient model between the two models namely, ARCH and bilinear models when applied to stock market data. The data was obtained from the Nairobi Stock Exchange (NSE) for the period between 3 rd June 1996 to 31 st December 2007 for the company share prices while for the NSE 20-share index data was for period between 2 nd March 1998 to 31 st December 2007.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. Specifically, the different extensions of ARCH-type models were utilized with ARMA and bilinear models for modelling the weekly mean of the chosen data set. The model efficiency was determined based on the minimal mean squared error (MSE). The results show that the Bilinear-GARCH model with the normal distribution assumption and the AR-Integrated GARCH (IGARCH) model with student's t-distribution are the best models for modelling volatility in the Nairobi Stock Market data.
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