Comparing Predictive Accuracy of Nonlinear Asymmetric Volatility Models: Evidence from the Nigerian Bank Share Prices.

This present work is motivated by the articles titled "Answering the skeptics: yes, standard volatility models do provide accurate forecasts" (Andersen and Bollerslev, 1998) and `A forecast comparison of volatility models: Does anything beat a GARCH(1, 1)?" (Hansen and Lunde, 2005). In the latter paper, the authors could not obtain a single winner amongst different volatility models considered, as it was different models that emerged as best in forecasting the volatility of the asset prices, this implying that the best models did not perform better than GARCH(1,1) model on forecasts. We were motivated by this assertion.   We presented four types of nonlinear asymmetric volatility models, and applied these in predicting the volatility of 12 bank share prices in Nigeria.  The pairwise, forecasts comparison was investigated using the Diebold and Mariano (DM) test.  The initial estimation disproved linear GARCH model since it failed to satisfy stationarity and regularity conditions for the model and we proceeded to estimating the asymmetric types.  The Asymmetric Power ARCH (A PARCH) model emerged the best model in terms of fitness in 9 out of the 12 cases, and other models that could not fit the data well were suggested as the best models in making volatility forecasting

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