Comparison of forecasting performances: Does normalization and variance stabilization method beat GARCH(1,1)-type models? Empirical evidence from the stock markets

What is it about?

In this paper, we present a comparison between the forecasting performances of the normalization and variance stabilization method (NoVaS) and the GARCH(1,1), EGARCH(1,1) and GJR-GARCH(1,1) models.

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Why is it important?

The findings show that NoVaS method performs better out-of-sample forecasting performance than GARCH(1,1)-type family. Furthermore, the study emphasizes that researchers or practitioners must be very careful when determining the sample size for the training set, selecting a reasonable predictor and using a model under several distributions for forecasting volatility in different datasets. In addition, The result can offer useful guidance in model building for out-of-sample forecasting purposes, aimed at improving forecasting accuracy.