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This consumer behavior is especially seen in cosmetics, cars, fashion, and cigarettes. The presumption underlying gender-based marketing segmentation is that: men and women prefer gender signaling products in spite of the fact that the ingredients and the USPs (unique selling propositions) of these products are almost the same. In addition to these aspects, it is seen that gender is also used as a marketing segmentation parameter and this trend seems to be rising especially in recent years. Market segmentation is a widely used process of dividing customers into groups based on different aspects like SES (socioeconomic status) groups, locations, interests, etc. The modelling and forecasting of the volatility of the stock markets has been a topic of great interest in the world of finance.The motivation of the study was threefold: (1) we employ elev- en GARCH models such as, sGARCH, EGARCH, GJR-GARCH, APGARCH, IGARCH, csGARCH, FIGARCH, AVGARCH, TGARCH, NGARCH and NAGARCH to produce better forecasts, (2) we use ten different distributions, such as normal (norm), gener- alized error distribution (ged), student t-distribution (std), skewed normal distribution (snorm), skewed generalized error distribution (sged), skewed student t-distribution (sstd), generalized hyperbolic distribution (ghyp), normal inverse gaussian distribution (nig), gen- eralized hyperbolic student t-distribution (ghst) and Johnson’s Su distribution (jsu), and (3) we propose a process for the model and the distribution by using a fair selection platform which is named ‘model confidence set’.In this study, we aim to provide an empirical foundation for a better understanding of the ability of different GARCH models with different distributions in terms of out-of-sample forecasting performace.

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