Stochastic modelling in Financial markets: case study of the Nigerian Stock Market

This research uses suitable stochastic models typically encountered in empirical and quantitative financial economics to analyse stock market data from the Nigerian Stock Market (NSM), in light of a) possible changes in the policy environments as result of the 2004 financial reforms by the then Governor of Central Bank of Nigeria, b) effects or otherwise of the 2008-09 global financial crises on the Nigerian financial system, and c) more technical issues underpinning performance of financial markets for example market efficiency, anomalies, bubbles, volatilities and their implications for investment decisions, stock market development and financial policy. There are substantial differences in the operation and characteristics of developed, emerging and pre-emerging (African) financial markets in terms of the above mentioned issues. Sometimes as part of general discussion of results we comment on the extent to which the characteristics of the NSM differ from known results in developed markets. A wide range of financial econometric methods and models including multivariate regression, Goodness of fit tests, Runs, Autocorrelation Function, Variance Ratio, Autoregressive tests, and discrete log logistic and GARCH-type models are applied. Both the All Share index and return data for 2000 to 2010 are used in this study. The time series data are divided into two periods namely pre-reforms (2000-2004) and postreforms (2005-2010). This study provides both investors and researchers in emerging African markets with a clear understanding of key financial characteristics of the NSM. Some useful results were obtained. Key characteristics of the NSM analysed in terms of market index prices and returns reveal evidence of market inefficiency and volatility. The data do not provide evidence of bubbles and anomalies in the NSM. This study, according to the author’s best knowledge, is possibly the most comprehensive combined study of crucial issues affecting the NSM including volatility, anomalies, bubbles and market efficiency. However, some other issues are excluded from the study because of the limitations of the data for example valuations and predictability, which are more suitably studied within specific companies and market sectors.