Search Results (12)

Energy use remains central to Colombia’s economic growth, yet its composition shapes the scale and direction of environmental outcomes. This study investigates how coal, oil, and hydroelectricity influence ecological degradation within the context of economic growth. The study applies cross-quantilogram and bootstrap Fourier Granger causality techniques to capture directional dependence and predictive causality across different quantiles, respectively. The findings show that the relationships are heterogeneous rather than uniform across the distribution. Economic growth exhibits a predominantly negative dependence on ecological footprint, suggesting that higher output is associated with lower ecological pressure under several environmental states. Hydroelectricity also shows a largely negative dependence, indicating its general contribution to environmental sustainability, although this effect weakens under extreme conditions. By contrast, the effects of coal and oil are more conditional and vary across quantiles, reflecting the complex role of fossil fuels in Colombia’s environmental dynamics. The bootstrap Fourier Granger causality results further reveal that causality is not constant across the distribution, but emerges only at specific quantiles. The central policy implication from this result lies in adopting an adaptive environmental strategy in which preventive measures dominate under low degradation, green-supportive policies are emphasized under moderate degradation, and stronger corrective interventions are implemented under high ecological stress. Full article

Financial markets have the potential to magnify the adverse impacts of carbon-intensive assets, mainly in the case of a swift and unforeseen shift toward a low-carbon economy. Given that green bonds are already in the process of standardization and actively support the funding of environmental goals, this paper aims to explore their relationship with the European banking system. To achieve this objective, we utilize a cross-quantilogram approach, analyzing daily data gathered from July 2014 to January 2021 and examining bi-directional dependence. Our unique contribution lies in revealing the relationships between the green bond index and the stock market dynamics of European banks compared to their relationships with conventional stock market indices, which is a novel endeavor to the best of our knowledge. The results are consistent with prior research findings regarding the relationships between the green bond index and various companies and financial assets. These results confirm that other financial instruments impact green bonds, whereas the influence exerted by green bonds on other assets is minimal. Additionally, our study provides evidence indicating that the COVID-19 pandemic has altered the connections between these financial assets. Full article

We examine the daily dependence and directional predictability between the returns of crude oil and the Crude Oil Volatility Index (OVX). Unlike previous studies, we apply a battery of quantile-based techniques, namely the quantile unit root test, the causality-in-quantiles test, and the cross-quantilogram approach. Our main results show evidence of significant bi-directional predictability that is quantile-dependent and asymmetric. A significant positive Granger causality runs from oil (OVX) returns to OVX (oil) returns when both series are in similar lower (upper) quantiles, as well as in opposite quantiles. The Granger causality from OVX returns to oil returns is only significant during periods of high volatility, although it is not always positive. The findings imply that the forward-looking estimate of oil volatility, reflecting the sentiment of oil market participants, should be considered when studying price variations in the oil market, and that crude oil returns can be used to predict oil implied volatility during bearish market conditions. Therefore, the findings have implications regarding predictability under various conditions for oil market participants. Full article

Mineral resources are essential raw materials to generate electricity, fuel vehicles, and heat homes and workplaces. Besides, the global agenda of clean energy deployment, including solar photovoltaics (PV), wind turbines, electric vehicles (EV), and storage facilities, calls for a considerable volume of critical minerals, which elevates their respective import demands. This highly concentrated source of those minerals poses a significant concern triggered by the augmented geopolitical tensions and economic policy uncertainties. In light of this context, our objective is to estimate the response of mineral import demand to global geopolitical risk events and economic policy uncertainty covering monthly data from January 1996 to December 2020. In doing so, we apply the cross-quantilogram (CQ) and the quantile-on-quantile (QQ) regression approaches due to the fat-tailed nature of the data property. Besides, these quantile-based data analysis procedures are appropriate for non-normal data sets and show the co-movement of the variables of interest under a bi-variate modelling approach. More importantly, these two techniques also exhibit the quantile connectedness among the variables in the bearish and bullish conditions. Moreover, our findings show that mineral import demand responds negatively to the USA’s (own) and global geopolitical risk events at the high quantiles under long memory. In addition, this demand reacts positively to the USA’s (own) and global economic policy uncertainty in entire quantiles under long memory. Therefore, our policy suggestions are concerned with tackling geopolitical tensions and economic policy uncertainty by adopting pre-emptive measures within a viable institutional mechanism to continue impressive mineral trade flows. Full article

The Saudi economy ought to maintain a significant amount of foreign exchange reserves due to the pegged exchange rate regime. As a hydrocarbon economy, we measure the dynamic response of external assets and liabilities of banks to the international oil price in Saudi Arabia. In the presence of extreme observations, we apply sophisticated frameworks, including cross-quantilograms, quantile-on-quantile and TVP-VAR approaches, to analyze weekly time-series data from 1993 to 2021. Our results from the cross-quantilogram and quantile-on-quantile frameworks demonstrate that foreign assets and liabilities responded asymmetrically to the volatilities of international oil prices under the bullish and bearish states of the market over different memories. The TVP-VAR results indicate that, during the COVID-19 pandemic, the Saudi economy encountered negative net foreign assets, which occurred mainly as a significant plague of international oil prices. Our findings are robust under different estimators. Full article

Environmental, social, and governance (ESG) factors are increasingly analysed to identify the risks and opportunities in contemporary economies. The banking sector influences the whole economy through the credit channel and balances its stability. The interplay of these elements motivated our main question, whether ESG scores impact European financial stability, measured for the banking sector. To this aim, we employ the cross-quantilogram methodology, which explores dependences at all levels of the distributions of two random variables. To determine the quantile dependence, we resort to methods of measuring systemic risk (Marginal Expected Shortfall—MES, CoVaR, and ΔCoVaR) for all commercial banks listed on European stock exchanges. While our approach provides a dashboard for analysis of the dependence of financial stability on ESG pillars, our findings indicate that such a connection is valid and cannot be identified with standard approaches that explore average distribution levels. We also document the differences in these impacts across the ESG pillars. Full article

This study investigates the interconnectedness among the stocks of the publicly listed vaccine-producing companies before and after vaccine releases in 2020/21. In doing so, the study utilizes the daily frequency equity returns of the major vaccine producers, including Moderna, Pfizer, Johnson & Johnson, Sinopharm and AstraZeneca. First, the investigation applies the TVP-VAR Dynamic Connectedness approach to explore the time–frequency connectedness between the stocks of those vaccine producers. The empirical findings demonstrate that Moderna performs as the most prominent net volatility contributor, whereas Sinopharm is the highest net volatility receiver. Interestingly, the vaccine release significantly increases the stock market connectedness among our sampled vaccine companies. Second, the cross-quantile dependency framework allows for the observation of the interconnectedness under the bearish and bullish stock market conditions by splitting any paired variables into 19 quantiles when considering short-, medium- and long-memories. The results also show that a high level of connectivity among the vaccine producers exists under bullish stock market conditions. Notably, Moderna transmits significant volatility spillovers to Sinopharm, Johnson & Johnson and AstraZeneca under both the bearish and bullish conditions, though the volatility transmission from Moderna to Pfizer is less pronounced. The policy implication proposes that the vaccine release allows companies to increase their stock returns and induce substantial volatility spillovers from company to company. Full article

Rising concerns over climate change have increased investors’ and policymakers’ interests in environmentally friendly investments, which have led to the rapid expansion of the green equity market recently. Previous studies have focused on analyzing the green equity market at the aggregate level, thereby overlooking the heterogeneity across green equity sub-sectors. This paper contributes to the literature by investigating how interdependence between green equity markets and other financial assets varies across regions, market conditions, and investment horizons. To this end, the paper employs the recently developed cross-quantilogram framework, which measures the cross-quantile dependence across time series without any moment condition requirement. The results show that within the green equity market, movements in the U.S. market can predict movements in the Asian and European markets during all market conditions. In contrast, the Asian and European green equity markets only predict movements in the U.S. market during bearish periods. The paper also finds that regional green equity markets respond differently to movements in other financial assets, such as energy commodity and general stock returns. In addition, the interdependence among regional green equity and other assets varies across market conditions and investment horizons. These results have important implications for environmentally friendly investors and policymakers. Full article

This paper examines the role of non-cash flow factors over correlation jumps in financial markets. Utilizing time-varying risk aversion measure as a proxy for investor sentiment and the cross-quantilogram method applied to intraday data, we show that risk aversion captures significant predictive power over realized stock-bond correlation jumps at different quantiles and lags. The predictive relation between correlation jumps and time-varying risk aversion is found to be asymmetric, as we detect a heterogeneous dependence pattern across different quantiles and lag orders. Our findings underline the importance of non-cash flow factors over correlation jumps, highlighting the role of behavioral factors in optimal portfolio allocations and the effectiveness of diversification strategies. Full article

A bulk of literature suggests that geopolitical events such as terrorist attacks dampen tourism demand. However, there is little research on whether this effect helps predict the return of the tourism equity sector. We provide country-level evidence on whether local and global geopolitical risk (GPR) predicts the first and second moments of tourism stocks in emerging economies. This objective was achieved by employing the non-parametric causality-in-quantiles (CiQ) model and a cross-quantilogram (CQ) test, which allowed us to uncover the predictive potential of GPR for the tourism sector equities. Our findings, obtained through the CiQ model, suggest that while both local and global GPRs carry significant potential for predicting the returns and volatility of tourism stocks of most emerging economies under normal market conditions, they seem to play no such role in certain countries. These countries include South Korea, for which only a limited number of tourism stocks trade on the domestic stock market compared to other sectors, and Colombia, for which both the domestic stock market and tourism sectors are at an emerging stage. Further, it turns out that, compared to its local counterpart, global GPR has a more pronounced predictive power for the tourism stocks of emerging economies. Finally, with some exceptions, the results are qualitatively similar, and hence reasonably robust, to those when a directional predictability model is applied. Given that geopolitical shocks are largely unanticipated, our findings underscore the importance of a robust tourism sector that can help the market recover to stability as well as an open economy that allows local investors to diversify country-specific risks in their portfolios. Implications and directions for future research are discussed. Full article

The research investigates the safe-haven, hedging, and diversification function of crude oil for conventional currencies, among which five are major oil exporters, and six are major oil importers. In order to model time-varying dynamic correlations between crude oil and currencies, the study uses the Asymmetric-DCC model. The findings highlight low or negative correlations, especially during the crisis period. Next, we employ a quantile based regression framework and conclude distinct safe-haven and hedge functions of oil for major currencies. We provide additional evidence on the safe-haven, hedging, and diversification function of crude oil using the cross-quantilogram framework. The findings of out of sample analysis illustrate that the hedging effectiveness of oil is greater for oil-exporting countries. In addition, the conditional diversification benefit of oil is higher in the lower quantiles, i.e., when both foreign exchange and oil markets are in a bearish state. Finally, implications for investors, portfolio managers, and policymakers are further discussed. Full article

This paper examines the predictive power of time-varying risk aversion over payoffs to the carry trade strategy via the cross-quantilogram methodology. Our analysis yields significant evidence of directional predictability from risk aversion to daily carry trade returns tracked by the Deutsche Bank G10 Currency Future Harvest Total Return Index. The predictive power of risk aversion is found to be stronger during periods of moderate to high risk aversion and largely concentrated on extreme fluctuations in carry trade returns. While large crashes in carry trade returns are associated with significant rises in investors’ risk aversion, we also found that booms in carry trade returns can be predicted at high quantiles of risk aversion. The results highlight the predictive role of extreme investor sentiment in currency markets and regime specific patterns in carry trade returns that can be captured via quantile-based predictive models. Full article