Modelling the asymmetric effect of COVID-19 on REIT returns: A quantile-on-quantile regression analysis

Asymmetric impacts of geopolitical risk on stock markets: A comparative analysis of the E7 and G7 equities during the Russian-Ukrainian conflict

In a nonparametric quantile-on-quantile regression model, we analyze the asymmetric financial impact of the Russian-Ukrainian conflict-induced geopolitical risk (GPR) on the top-seven emerging (E7) and developed (G7) stock markets. Our findings indicate that the impact of GPR on stock markets is not only market-specific but also asymmetric. Except for Russia and China, all E7 and G7 stocks respond positively to GPR in normal conditions. Among the E7 (G7) countries, stock markets from Brazil, China, Russia, and Turkey (France, Japan, and the US) are resilient to GPR in bearish stages. The portfolio and policy implications of our findings have been highlighted.

Emerging markets equities' response to geopolitical risk: Time-frequency evidence from the Russian-Ukrainian conflict era

This study investigates the asymmetric interdependence between geopolitical risk (GPR) and the stock markets of the top-seven emerging (E7) countries (i.e., Mexico, Russia, Turkey, India, China, Indonesia, and Brazil) in the ongoing geopolitical conflict between Russia and Ukraine. With daily datasets covering the period 01-Feb-2022 to 25-July-2022, the squared wavelet coherence (SWC) and wavelet coherence phase difference (WCPD) techniques are employed. The results underscore heterogeneous and asymmetric market-specific coherence and lead-lag patterns regarding E7 stocks' interdependence with geopolitical risk. The findings imply high comovements between Black Swan events like the Russian-Ukrainian conflict and financial markets' volatility, highlighting the essence of alternative assets or asset classes for hedging geopolitical risks in the ongoing military actions. The heterogeneous and asymmetric responses offered by E7 stocks against GPR render emerging markets equities suitable for diversification and downside hedging strategies against GPR-induced shocks. The findings are robust to the time-varying parameter vector autoregression (TVP-VAR) connectedness approach. The results' implications for portfolio managers, investors, and policymakers are discussed.

COVID-19 and the quantile connectedness between energy and metal markets

This study analyzes the relationship between clean and dirty energy sources and energy metals during the COVID-19 pandemic. We document a sharp increase in connectedness after the COVID-19 pandemic, that is asymmetric at the lower and upper quantiles, with stronger dependence among the variables at the upper quantiles. Among the energy metals, cobalt is the least connected to the energy markets. Finally, our empirical results show a switch in the net connectedness indexes of energy metals and clean energy after January 2021. Our results have implication for investors and policy makers for energy and metal under various market conditions.