Quantifying Information Flows among Developed and Emerging Equity Markets

Abstract

We rely on daily changes in implied volatility indices for the US stock market (VIX), developed markets excluding the US (VXEFA), stock markets in Brazil (VXEWZ), Russia (RVI), India (NIFVIX), China (VXFXI), and the overall emerging market volatility index (VXEEM) to examine the degree of information flows among the markets in the coronavirus pandemic. The study also employs the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to decompose the data into intrinsic mode functions (IMFs). Subsequently, we cluster the IMFs based on their level of frequencies into short-, medium-, and long-term horizons. The analysis draws on the concept of Rényi transfer entropy (RTE) to enable an assessment of linear as well as non-linear and tail-dependence in the markets. The study reports significant information flows from BRIC volatility indices to the overall emerging market volatility index in the short-and medium-terms and vice versa. We also document a mixture of bi-directional and uni-directional flow of high risk information and low risk information emanating from emerging equity markets and from the developed markets. We find that the transmission of high risk information is largely dominated by the developed markets (VIX and VXEFA). In the midst of high degree of contagion, our findings reveal that investors can find minimal benefits by shielding against adverse shocks from the developed markets with a combination of stocks from India and other equities in the emerging markets in the short-term, within 1–15 days. For as low as 1–5 days, the empirical evidence indicates that a portfolio consisting of stocks from Russia and Brazil also offer immunity to shocks from the VXEFA. Our study makes an important empirical contribution to the study of market integration and contagion among emerging markets and developed markets in crisis periods.

Multifrequency-based non-linear approach to analyzing implied volatility transmission across global financial markets

“…Finally, low frequencies are defined as mean periods longer than 50 days. This is consistent with the empirical approach by Adam et al (2022, Owusu Junior et al (2021b, Asafo-Adjei et al (2022c), Asafo-Adjei et al (2022b) and Bossman et al (2022a).…”

“…Further, the time-varying dynamics of the financial markets were amplified as they reported that investors who delayed investment in these markets during the pandemic were likely to minimize risks. Boateng et al (2022b) also employed the CEEMDAN-based RTE framework to quantiify information flows among developed and emerging equity markets. In their findings, they found a mixture of bi-directional and uni-directional flow of both high-and low-risk information.…”

Dynamic Connectedness between Indicators of the Ghana Stock Exchange Returns and Macroeconomic Fundamentals

“…Also, the existence of crises or economic events has given rise to addressing the important role a market-based system plays in responding to or influencing other macroeconomic fundamentals, leading to their interdependent structures. The rise in correlations between the market-based system and other macroeconomic variables during turbulent periods contributes to the contagion literature (Baur 2012;Shahbaz et al 2019;Boateng et al 2022b;Agyei et al 2022b;Bossman et al 2022aBossman et al , 2022bYarovaya et al 2022;Gunay and Can 2022, etc. ).…”

Conditional effects of local and global risk factors on the co-movements between economic growth and inflation: Insights into G8 economies

“…Accordingly, the roles of exchange rate and interest rate can be factored to contribute to the understanding of the macroeconomic modelling. Considering the rapid fluctuations in financial time series leading to their nonlinearities, analyses can also be performed using robust decomposition techniques coupled with the quantification of the amount of information that flows through the macroeconomic variables [ [76] , [77] , [78] , [79] , [80] , [81] , [82] ]. The sample period is limited to August 2021 due to limited data availability for some variables for the G8 nations but enough for the subject matter to be investigated.…”