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In times of crisis, such as global pandemics or conflicts, investors’ preference between green and conventional bonds may lean towards the latter due to increased risk aversion and a focus on short-term stability. However, some investors motivated by increased awareness of sustainability issues may maintain or increase their allocation to green bonds, seeing them as an opportunity for long-term resilience and sustainable investing. We use multifractal detrended cross-correlation analysis, wavelet coherence, and copula-based dependence analysis to examine the complex relationship between the S&PGreen Bond Index and the S&P500 Bond Index. The results indicate the presence of multifractal cross-correlations, the strength of which is most pronounced in times of crisis, especially in the post-COVID-19 period. The wavelet-based analysis also detects the COVID-19 break and shows significant interdependence at all frequency levels after the RU–UA conflict. The copula-based correlation values exhibit a distinct oscillating pattern over time, characterized by an initial break coinciding with the impact of COVID-19. In light of these findings on the impact of COVID-19 and the RU–UA conflict, we have included the Geopolitical Risk Index in our analysis to better understand how geopolitical tensions and conflicts influence the observed interdependence and to gain insight into how changes in the global risk environment affect both bond market dynamics. Overall, the results of this study provide insights into the interconnectedness between conventional and green bond markets and highlight potential spillover effects and systemic risks in an increasingly complex financial landscape.

The relationship between environmental degradation and economic growth has become a prominent topic in recent decades, increasing multidisciplinary approaches across a wide range of studies. Analyzing the economy-environment nexus is significant because most countries attempt to limit environmental deterioration while pursuing economic growth. This paper studies the non-linear cross-correlations between economic growth and environmental degradation on a sample of five developed European countries - France, Germany, Spain, Norway, and the United Kingdom. The study is performed using two approaches: multifractal detrended cross-correlation analysis and wavelet-based analysis. According to the empirical tests, there are non-linear cross-correlations between economic growth and environmental degradation in all five countries. For each country, multifractal metrics such as Hurst exponents, scaling exponents, and multifractal spectra are computed to show the significance of all time-series pairs. The strongest multifractal features have been observed in France, Norway, and Spain. Shuffling the series suggests that multifractality in the cross-correlations arises both from a broad probability density function and from different long-term correlations. Wavelets are applied to detrend all data sets before computing the cross-correlation. Moreover, the wavelet coherence is computed as a measure for analyzing interdependencies across time and frequencies. Except for France and Spain, Wavelet-detrended cross-correlation values are considerably lower than cross-correlation values of the raw data sets, which indicates that the interdependence between growth and environmental degradation is rather long-term. In terms of wavelet coherence, results vary among the countries, with France and Spain showing the highest and most persistent coherence values. [ABSTRACT FROM AUTHOR]