Abstract: Background and Objective: Error distributions were found to be very useful in volatility modeling financial time series. To this end, several error distribution innovation were proposed for estimating the true parameters of volatility models in term of fitness and forecast as a results of excess leptokurtic presence in financial stock market trigger by economic crises and wars etc. The aim of this study was to propose new class of error distributions using class of exponentiated distribution method for fitness and forecasting performance of volatility models. Materials and Methods: A daily returns data from standard and poor 500 (S and P500) index return from the period of 2007-2017 were used to validate the new error distribution and Jarque-Bera (JB), ADF test, ARCH effect test were used to validate the assumption of volatility models, maximum likelihood (ML) methods were used to estimate the parameters of the volatility models under five error innovation distribution. Results: From the obtained results, it was observed that TGARCH and APARCH model outperformed in term of best fitness and forecasting performance under the proposed error innovation distribution. Conclusion: This study will enable a better understanding of error innovation distribution in improvement of volatility models.