A GARCH Modelling of Volatility and M-GARCH Approach of Stock Market Linkages of North America

Abstract

The present study attempts to capture the return volatility and the extent of dynamic conditional correlation between the stock markets of North America region. The data contain weekly stock market returns spanning from the second week of 1995 to the fourth week of June 2016. Using univariate ARCH and GARCH approaches, the study finds evidence of return volatility and its persistence within the region. Mexican stock market neither reacts intensely to immediate market fluctuations nor the part of the realized past volatility spill over to the current period, whereas the stock markets of Canada and USA experience high persistence of return volatility and Bermuda stock market returns are highly sensitive to the immediate market fluctuations. Using MGARCH-DCC, this article finds that emerging markets are less linked to the developed market in terms of return and that there also exists a weak co-movement between the stock markets. There is no evidence of market integration throughout the sample period. Correlations tend to spread out equally throughout the sample period, but the co-variances were found to be more volatile during 2008–2010. This article reveals that changes in co-movement are not due to a change in the correlations between markets but is simply due to volatility.

An analysis of CEE equity market integration and their volatility spillover effects

“…Indeed, these markets have a long memory and are strongly integrated, which can be a reason for international diversifications. Our main results do not confirm previous studies (Majdoub and Mansour, 2014;Panda and Nanda, 2018). In this scenario, the strong conditional correlations over time puts forward that the CEE stock markets are tightly integrated and the volatility transmissions among them are significant as well.…”

“…Finally, the integration of stock markets should be mentioned in CEE financial markets in particular, European countries in general. Our findings are consistent with Patev et al (2006), Vo and Ellis (2018) and Jebran et al (2017) and opposite to Panda and Nanda (2018). These results are intimately connected with some features of CEE finance industry: the screening of the CEE equity index prohibiting sectors in terms of a cause of volatility; imposing stringent restrictions on leverage ratios and interest-related dealings; and preventing purely speculative investments.…”

“…More importantly, openness of financial markets not only makes substantial contribution to economic development but also makes developing countries more vulnerable to financial disruptions (Levine and Schmukler, 2007). The properties of volatilities commonly seen in equity returns consist of volatility clusters, varying over time, infinite non-divergence, varying according to price movements (Panda and Nanda, 2018). These determinants play a prominent role in the development of volatility models.…”

“…It is a limitation of this investigation. In this regard, we have read through the number of relevant articles, which are employed MGARCH models to estimate volatility across markets without diagnostic test (Vo and Ellis, 2018;Kim et al, 2015;Majdoub and Mansour, 2014;Kumar, 2013;Panda and Nanda, 2018). Yet, their results had been confirmed when measuring the dynamic correlation of the economic indicators as well as its noteworthy implications.…”

Dynamic Connectedness among BRICS and Major Countries Stock Markets

“…Dimitriou et al (2013), Mensi et al (2017), and Jin and An (2016) find that the dependency between the U.S. and BRICS stock markets is more during the bullish period than in the bearish period. Panda and Nanda (2018) depict the equity returns carry time‐varying volatility clustering. Panda et al (2019) find that low volatility spillover exists across equity markets.…”