Climate risks and green stocks in Vietnam

Abstract

PURPOSE : Vietnam is among the countries most vulnerable to tropical cyclone risks, and its carbon-intensive production model influences its climate change trajectory. Nevertheless, various initiatives have been undertaken to tap into the country’s green economy potential, and I advance the related literature by exploring the connection between climate risks and green assets in Vietnam. DESIGN/METHODOLOGY/APPROACH : Firstly, by employing the predictive models, I examine the predictive power of climate risks for the returns of green assets in Vietnam between 2010 and 2023 using monthly data. Secondly, to address endogeneity and heteroscedasticity, I employ the feasible quasi-generalized least squares estimator, evaluating both in-sample and out-of-sample connections between climate risks and green assets in Vietnam. FINDINGS : My findings include the following: (1) Green stocks in Vietnam do effectively hedge against climate risk in recent samples that coincide with commitments to international climate agreements, suggesting the importance of data frames and the government’s commitment to modelling climatic outcomes. (2) Classification of assets based on the Vietnam Sustainability Index (VNSI) provides more theoretically compelling results, highlighting the need for robust measures of sustainability. (3) Controlling for key fundamentals, such as oil prices and exchange rate fluctuations, is essential to avoid model misspecification and potential overestimation of climate risk effects on green investment returns. (4) My findings show that incorporating climate risk into the predictive model for green asset returns significantly enhances forecast accuracy of the asset returns compared to benchmark models, such as the historical average and random walk, which overlook this risk factor. ORIGINALITY/VALUE : I provide two major contributions to the literature. (1) I investigate the predictive power of climate risks for the returns of green assets in Vietnam. (2) I conduct both the in-sample and out-of-sample predictability of the connections, as significant in-sample predictability outcomes do not necessarily translate into improved out-of-sample forecasts.