Modeling Oil shocks–Green Investments Nexus: A Global Evidence Based on Wavelet Coherence Technique

This study investigates the interdependence between oil shocks and green investments over time and frequency domains. Using the wavelet coherence approach, our results show evidence of bidirectional causality between all the variants of oil shocks and green investments around the global financial crisis and the 2014-2016 oil crisis. Economic activity shocks significantly Granger-cause green investments during the COVID-19 pandemic. © 2023, Asia-Pacific Applied Economics Association. All rights reserved.

Connectedness of green investments and uncertainties: new evidence from emerging markets

Purpose>This study aims to investigate the response of green investments of emerging countries to own-market uncertainty, oil-market uncertainty and COVID-19 effect/geo-political risks (GPRs), using the tail risks of corresponding markets as measures of uncertainty.Design/methodology/approach>This study employs Westerlund and Narayan (2015) (WN)-type distributed lag model that simultaneously accounts for persistence, endogeneity and conditional heteroscedasticity, within a single model framework. The tail risks are obtained using conditional standard deviation of the residuals from an asymmetric autoregressive moving average – ARMA(1,1) – generalized autoregressive conditional heteroscedasticity – GARCH(1,1) model framework with Gaussian innovation. For out-of-sample forecast evaluation, the study employs root mean square error (RMSE), and Clark and West (2007) (CW) test for pairwise comparison of nested models, under three forecast horizons;providing statistical justification for incorporating oil tail risks and COVID-19 effects or GPRs in the predictive model.Findings>Green returns responds significantly to own-market uncertainty (mostly positively), oil-market uncertainty (mostly positively) as well as the COVID-19 effect (mostly negatively), with some evidence of hedging potential against uncertainties that are external to the green investments market. Also, incorporating external uncertainties improves the in-sample predictability and out-of-sample forecasts, and yields some economic gains.Originality/value>This study contributes originally to the green market-uncertainty literature in four ways. First, it generates daily tail risks (a more realistic measure of uncertainty) for emerging countries' green returns and global oil prices. Second, it employs WN-type distributed lag model that is well suited to account for conditional heteroscedasticity, endogeneity and persistence effects;which characterizes financial series. Third, it presents both in-sample predictability and out-of-sample forecast performances. Fourth, it provides the economic gains of incorporating own-market, oil-market and COVID-19 uncertainty.