1 FORECASTING STOCK MARKET VOLATILITY: EVOLUTION AND DETERMINANTS By

The time to time studies enclosed, delved into the contrasting and diverging substantiation and endorsement on the comparative volatility forecasting methodology and quality in the stock market. Each different research espouses its forecasting ascendancy over a variety of forecasting models. The apparent dissonance and contretemps of opinions among researchers ignited this research paper because forecasting had been relied upon and being employed in the evaluation and appraisal of key economic decision making, and the pricing of assets or options. This study correlates the past, evaluates the present and hopes to predict the future by reviewing the evolution of the techniques such as the random walk model, historical mean model, moving average model and simple regression model among others. Mean error statistic, mean absolute error statistic, root mean squared error statistic, mean absolute percentage error statistic, under-predictions mean mixed error, and over-prediction mean mixed error were employed to examine the error statistics in this study. 2 The findings proposed that the simple regression model is the pre-eminent model to be utilised in forecasting stock market volatility. Nevertheless, different models rankings had been indicated in connection to models sensitive toward the error statistic employed in order to evaluate the precision of the forecasts. The forecasting results were affected by technology advancement, increasing knowledge of investors, improvement of rules and regulations, the liberalising policies of government, and the overall improvement in accounting systems. This paper recommends that the latest and short term historical data should be used as-samples in forecasting