Integration in Bric Stock Markets: An Empirical Analysis

Abstract

This paper aims to analyse financial integration in the markets of Brazil, China, India and Russia (BRIC’s), from July 2015 to June 2020, being the sample split in pre and during the global pandemic (Covid-19). In order to carry out this analysis, different approaches were undertaken to analyse two issues, namely, whether: (i) the global pandemic has accentuated the interdependencies in the BRIC financial markets? If so, how it has influenced the efficiency of portfolio diversification. The results suggest very significant levels of integration, in the Covid period these evidences diminish the chances of portfolio diversification in the long term. In turn, the analysis of the relationship between markets, in the short term, through the impulse response functions, in a period of global pandemic, shows positive/negative movements, with statistical significance, with persistence exceeding one week. In addition, there was no immediate adjustment in prices between markets, due to the high levels of shocks identified. Regarding the implementation of efficient portfolio diversification strategies, we consider that a good option for investors would be to avoid investments in stock markets. In this sense, one suggestion could be to invest in derivatives, gold and sovereign debt markets, with the purpose of diversifying portfolios and mitigating the risk arising from the global pandemic. The authors consider that the results achieved are of interest to investors seeking opportunities in these exchanges, as well as to policy makers to undertake institutional reforms in order to increase the efficiency of stock markets and promote the sustainable growth of financial markets.

Random Walks and Market Efficiency Tests: Evidence for Us and African Capital Markets

“…These events significantly infected developed economies, however, this significance was not dense in emerging economies. Understanding the synchronism between stock markets, as well as the study on the occurrence of movements in periods of turbulence is important for investors, investment fund managers, academics, in various aspects, particularly when it is to implement strategies for diversifying efficient portfolios (Alexandre, Dias, and Heliodoro, 2020;Alexandre, Heliodoro, and Dias, 2019;Dias, and Pereira, 2020;Dias and Carvalho, 2020;Dias, da Silva, and Dionysus, 2019;Alexandre, 2019, 2020;Dias, Heliodoro, Alexan-dre, Santos, and Farinha, 2021;Vasco, 2020a, 2020b;Dias, Heliodoro, Alexandre, et al, 2020a, 2020aDias, Heliodoro, Teixeira, andGodinho, 2020a, 2020b;Dias, Pardal, Teixeira, & Machová, 2020c;Heliodoro, Dias, Alexandre, and Vasco, 2020;Sparrow, P., Dias, R., Šuleř, P., Teixeira, N., and Krulický, 2020;Santos and Dias, 2020).…”

Stock Market Efficiency in Africa: Evidence From Random Walk Hypothesis

“…If a given stock market is strongly linked to another country's stock market, the financial stability of the former depends in part on the financial stability of the second. For this reason, a close or strong link between markets increases the levels of vulnerability to external shocks and, consequently, influences the economic conditions and welfare levels of their respective countries, as well as the efficiency of the market itself (Alexandre, Heliodoro and Dias, 2019;Dias, da Silva and Dionysus, 2019;Alexandre, 2019, 2020;Alexandre, Dias and Heliodoro, 2020;Heliodoro et al , 2020Heliodoro et al , , 2020Heliodoro, Dias and Alexandre, 2020;Heliodoro, 2020, 2020;, 2020a, 2020bDias, Sparrow, et al , 2020) .…”

Risk Diversification in Asean-5 Financial Markets: An Empirical Analysis in the Context of the Global Pandemic (Covid-19)

“…The DFA has the following interpretation: 0 < < 0,5: anti-persistent series; = 0,5 series has a random walk; 0,5 < < 1 persistent series. The function of this technique is to examine the relationship between the and + values at different times (Guedes et al, 2018;Dionísio, 2019 Alexandre, Dias, andDias, Heliodoro, Alexandre, Santos, and Farinha, 2021;Dias, Heliodoro, and Alexandre, 2020;Dias, Heliodoro, Alexandre, and Vasco, 2020;Dias, Pardal, Teixeira, and Machová, 2020 Zebende ( 2011) non-trend cross-correlation coefficient is a method for quantifying the level of cross-correlation between two nonstationary time series. The coefficient is based on the DFA (Peng et al, 1994) and DCCA (Podobnik and Stanley, 2008) methods.…”

“…If a given stock market is strongly linked to the stock market of another country, the financial stability of the former depends, in part, on the financial stability of the latter. For this reason, a close or strong link between markets increases levels of vulnerability to external shocks and, as a result, influences the economic conditions and welfare levels of the respective countries, as well as the efficiency of the market itself Heliodoro, 2020a, 2020b;Alexandre, Heliodoro, and Dias, 2019;Dias and Carvalho, 2020;Alexandre, 2020a, 2019;Dias, Heliodoro, Alexandre, Santos, and Farinha, 2021;Dias, Heliodoro, Teixeira, and Godinho, 2020;Dias, Pardal, Teixeira, and Machová, 2020;Dias, Heliodoro, Alexandre, and Vasco, 2020b;Dias and Pereira, 2021; Heliodoro, P., Dias, R., Alexandre, P., and Vasco, 2020;Pardal, P., Dias, R., Šuleř, P., Teixeira, N., and Krulický, 2020;Santos and Dias, 2020).…”