Stock Returns, Volatility and Mean Reversion in Emerging and Developed Financial Markets

Abstract

Abstract. 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.

A Two-Regime Markov-Switching GARCH Active Trading Algorithm for Coffee, Cocoa, and Sugar Futures

“…Other papers test the spillover effect between stock and currency markets of developed and developing countries. Examples of these are the works of Mouratidis et al [43], Miles and Vijverberg [44], Lopes and Nunes [45], Kanas [46], Álvarez-Plata and Schrooten [47], Parikakis and Merika [48], Girdzijauskas [49], Dubinskas and Stungurienė [50], Kutty [51], Ahmed et al [52], and Sosa, Ortiz, and Cabello [11].…”

Investigating Abnormal Volatility Transmission Patterns Between Emerging and Developed Stock Markets: A Case Study

“…EGARCH and TGARCH models in order to estimate volatility of BSE 500 stock returns and concluded that selected financial time series exhibit leverage effect, volatility clustering and leptokurtosis. Ahmed et al (2018) investigated stock returns, volatility and mean reversion in emerging and developed financial markets and the empirical findings 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. Fasanya and Akinde (2019) conducted an empirical study of return and volatility spillovers in the Nigerian emerging financial markets and concluded that the stock market returns are more susceptible to fluctuations in financial market, but also more vulnerable and risky due to the fact that it can be easily influenced by the fluctuations in both the currency and money markets.…”

The effects of volatility and changes in conditional correlations in the stock markets of Russia and developed countries

“…Various interdependencies are revealed. Abdelkefi (2015) demonstrates the existence of unilateral and bilateral relations between the US stock market and other developed markets; Panda and Nanda (2018) establish that emerging markets are less related to developed market in terms of profitability; Kutlar and Torun (2014) show that while the markets of developed countries show a strong spread of volatility, in developed countries there is a weak spread of volatility to developing countries; Seth and Singhania (2019) show that selective border markets are intertwined with developed markets; Guesmi et al (2014) show that most European stock markets are closely related to the US market; Ahmed et al (2018) use correlation analysis to show a significant positive correlation between developed markets but a relatively insignificant correlation between developing and developed markets; Wang et al(2018) highlight the presence of a strong VOLATILITY AND CORRELATIONS BETWEEN STOCK MARKETS spread of volatility from the USA to five major stock markets; Serletis and Azad (2018) reveal statistically significant secondary effects of volatility from emerging economies on the United States; Hung (2019) demonstrates that the correlation between Central European markets is especially significant; and Mitra et al (2015) find that the transfer of volatility between stock markets is predictable because they follow a certain pattern, and therefore they were modelled using appropriate theoretical distributions. The above articles establish that the process of the spread of volatility affects the flow of financial assets between countries and has led to significant changes in terms of stock market returns, the volume of transactions, and market value.…”