Selecting superior GARCH model with backtesting approach in First Bank of Nigeria stock returns

The evaluation of financial risk models or backtesting is an important part of the internal model’s approach to market risk management. Unfortunately, the backtesting approach is not popular among financial analysts in Nigeria. Backtesting is a statistical procedure where actual profits and losses are systematically compared to corresponding VaR estimates. This study investigates the volatility in daily stock returns of First Bank of Nigeria using nine variants of GARCH models: sGARCH (1,1), girGARCH (1,1), eGARCH(1,1), iGARCH(1,1), apARCH(1,1), TGARCH(1,1), NGARCH(1,1), NAGARCH (1,1), and AVGARCH (1,1) along with value-at-risk estimation through backtesting approach using student t and skewed student t innovations. We use daily data for First Bank of Nigeria returns for the period, January 2, 2001 to May 8, 2017 obtained from a secondary source. Most of the models were promising in terms of information criteria and ARCH test after estimation but failed the backtesting analysis. With the backtesting approach, eGARCH (1,1) model with student t distribution emerged as the superior GARCH model among the competing GARCH models for modeling First Bank returns in Nigeria. This study recommends that backtesting approach can enhance modeling selection and reliable inferences among financial analysts and practitioners.

 

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