Abstract
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.
Muhammad Salman Khan, Kanwal Iqbal Khan, Shahid Mahmood, Muhammad Sheeraz. (2019) Symmetric and Asymmetric Volatility Clustering Via GARCH Family Models: An Evidence from Religion Dominant Countries, Paradigms , Vol 13, Issue 1.
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