Symmetric and Asymmetric Volatility Clustering Via GARCH Family Models: An Evidence from Religion Dominant Countries

Volatility clustering and asymmetry are considered as an essential element in time series data analysis for portfolio managers. This study is conducted to analyze the volatility clustering and asymmetry occurrence by employing different GARCH models. Data is collected from 11 Religion Dominant Countries (RDCs) based on daily stock returns from 2011 to 2017. The findings of the study show that volatility clustering increases the asymmetric comportment of daily stock market returns. We estimated the analytical competence of GARCH models and found that GJR-GARCH and EGARCH executed better results than GARCH (p, q) in RDCs stock markets. It also shows that GJR-GARCH and EGAECH explain the asymmetric behavior along with an accurate assessment of volatility clustering for the selected 11 RDCs stock markets. This study helps managers, investors, and corporations to make investment-related decisions.