The objective of this research is to measure and examine volatilities between important emerging and developed stock markets and to ascertain a relationship between volatilities and stock returns. This research paper also analyses the Mean reversion phenomenon in emerging and developed stock markets. For this purpose, seven emerging markets and five developed markets were considered. Descriptive statistics showed that the emerging markets have higher returns with the higher risk-return trade-off. In contrast, developed markets have low annual returns with a low risk-return trade-off. Correlation analysis indicated the significant positive correlation among the developed markets, but emerging and developed markets have shown relatively insignificant correlation. Results of ARCH and GARCH revealed that the value of likelihood statistics ratio is large, that entails the GARCH (1,1) model is a lucrative depiction of daily return pattern, that effectively and efficiently capturing the orderly reliance of volatility. The findings of the study showed that the estimate ‘β’ coefficients given in conditional variance equation are significantly higher than the ‘α’, this state of affair entails that bigger market surprises tempt comparatively small revision in future volatility. Lastly, the diligence of the conditional variance estimated by α + β is significant and proximate to integrated GARCH (1,1) model, thus, this indicates, the existing evidence is also pertinent in order to forecast the future volatility. The results signified that the sum of GARCH (1,1) coefficients for all the equity returns’ is less than 1 that is an important condition for mean reversion, as the sum gets closer to 1, hence the Mean reversion process gets slower for all the emerging and developed stock markets.
The short-term mean reversion of stock price and the change in trading volume
