Over the last decades, the corruption perception has been considered a material topic for economic agents, institutions and policymakers. This paper evaluates the role of the access to civil justice by economic agents for the corruption perception. Using yearly data from the last decade, we analyse the statistical contribution of the access to civil justice and the access, effectiveness, and impartiality of alternative dispute resolution mechanisms indexes to explain changes in the corruption perception. Therefore, we found that: (i) a 1% increase in access to civil justice is associated with a 1.819% decrease in perceived corruption with a highly statistically significant effect; (ii) increased reliance on alternative dispute resolution mechanisms is not necessarily perceived as a substitute for trustworthy formal legal systems; and (iii) there is strong evidence that access to civil justice Granger-causes the corruption perception index (and no statistically significant Granger causality was detected for corruption perception and alternative dispute resolution mechanisms).
Keywords: Alternative resolutions mechanisms, corruption perception, civil justice, integrity.
This study investigates the volatility specification of carbon prices in the European Union Emissions Trading System using seven stochastic volatility (SV, henceforth) models. These models are compared using a Bayesian model comparison technique. Data from March 2005 to October 2023 is analysed, segmented into different phases of market development, including periods such as Covid-19 and the Russia-Ukraine conflict. Empirical results from each phase highlight the varying performance of the SV models, with different models proving optimal fitting performance for different periods. The study finds that the SV with t-distributed innovations model consistently performs well, especially during mature phases of the market, indicating its suitability for capturing volatility dynamics. Additionally, during periods of significant external shocks, such as the Covid-19, the inclusion of moving average innovations model shows superior performance, suggesting its ability to capture volatility changes influenced by past innovations.
Keywords: Bayesian econometrics; EU ETS; Emission allowances; Markov chain Monte Carlo; Stochastic volatility.
This paper empirically studies the volatility behavior and co-movements of the European Union Emissions Trading System and the two main Chinese carbon markets between 2014 and 2024. First, we test a battery of Dynamic Conditional Correlation models, including those with fat tails and asymmetric effects. Different error term specifications were also examined. Second, we investigate the existence of spillover effects between the European and Chinese carbon markets using a Full BEKK model, also under alternative distributional assumptions for the error term. We find that: (i) multivariate GARCH models with fat-tailed distributions outperform the standard multivariate GARCH model; (ii) there is no statistical evidence supporting dynamic conditional correlations in these markets; (iii) incorporating t-distributed innovations in all multivariate GARCH models clearly outperforms the standard normal distribution; (iv) there is statistical evidence of spillover effects between the European and Chinese markets and the between two Chinese carbon markets; and (v) the sub-sample after the COVID-19 and the Russia-Ukraine conflict and the Israel-Hamas conflict corroborate the previous results.
Keywords: Alternative error term assumptions; Carbon markets; Emission allowances; Dynamic Conditional Correlation; Volatility Spillover.
This study examines the impact of climate-related financial policies on banks' risk taking behavior. Based on a dataset of 614 banks across 37 countries from 2006 to 2020, we find that stronger green financial policies lead to a reduction in bank risk from a total risk perspective. Conversely, when examining credit risk, we find an inverse relationship in which higher levels of commitment to green policies induce an increase in non-performing loans, translating into higher credit risk. Finally, we complement this analysis by demonstrating that the dampening effect of green policies on bank risk is stronger in developing countries and during crises. These results stand when using different proxies for bank risk and green financial policies.
Keywords: Banks’ risk, climate-related financial policies, green credit, sustainability.
This study analyses the behaviour of fifteen carbon markets from January 2019 to December 2025, focusing on both price and volatility dynamics. The empirical analysis is developed in five stages. First, return series are constructed and decomposed into trend, seasonal, and irregular components. Second, similarity between emissions trading systems is assessed using the irregular component. Third, volatility is estimated with an Exponentially Weighted Moving Average model. Fourth, standardized volatility series are analysed through factor analysis and Principal Component Analysis, and the retained components are modeled with ARIMA(1,0,1) specifications with t-Student innovations. Finally, an out-of-sample simulation is performed for the second half of 2025. The main contribution of the paper is to show, in the same comparative setting, that ETSs may share a common volatility structure while still presenting important differences in trend dynamics, irregular behaviour, and market-specific out-of-sample volatility shocks. The results also show that carbon markets are heterogeneous in their long-term trends, shortrun irregular behaviour, and volatility structures and that similarity across markets does not depend only on geographical or institutional proximity. By using a harmonized weekly panel and a common volatility framework, the study provides a systematic comparison across carbon markets with different institutional designs.
Keywords: Carbon markets; Emissions trading systems; Carbon price dynamics; Volatility; Principal component analysis; Factor analysis; ETS comparison.