This article attempts to assess the hedging, diversification and safe haven characteristics of gold, Bitcoin and Tether for G7 investors during the political and health crises. For this end, we use the Generalized Autoregressive Conditional Heteroskedasticity-A-Dynamic Conditional Correlation model. The findings prove that gold can be considered as a strong safe haven asset for the G7 investors during the Russia–Ukraine crisis. In contrast, cryptocurrencies fail to retain their safe haven features for Japanese investors during the COVID-19 pandemic. But, they act as diversifier assets for the rest of the G7 stock markets. The computed optimal hedge and hedging effectiveness reveal that Bitcoin displays the best hedging instrument for the United States, British, Japanese and Canadian investors during the Russia–Ukraine crisis whereas gold is considered as the best instrument for German, French and Italian investors.

In this article, we attempt to analyze and compare the safe-haven features of gold, Bitcoin and gold-backed cryptocurrency against the stock and banking indices of G7 countries during the outbreak of adverse events. To do so, we examine dynamic relationships between different assets and we compute optimal hedge ratios for different couples using the corrected Asymmetric Dynamic Conditional Correlation-Exponential Generalized Autoregressive Conditional Heteroscedasticity and corrected Asymmetric Dynamic Conditional Correlation-Generalized Autoregressive Conditional Heteroscedasticity models. We clearly show that gold and gold-backed cryptocurrency maintain higher weights in optimal portfolios compared to Bitcoin. We also report that shocks due to unexpected events increasingly affect dynamic correlations, asset weights and hedge ratios. This underscores the need for regular demand for rebalancing the hedge positions and effective risk management. We thereafter show that the relationship between Bitcoin (gold-backed cryptocurrency) and G7 indices is highly affected by the outbreak of COVID-19 pandemic. Such findings highlight the hedging and safe-haven features of different asset classes against stock markets. They could have insightful implications for investors who want to minimize investment risks and policymakers who are worried about the financial consequences of different unexpected events.

This study investigates hybrid machine learning models combined with wavelet transforms for predicting clean energy market dynamics from 01.04.2014 to 02.05.2024. Models such as support vector regression (SVR), artificial neural networks (ANNs), eXtreme Gradient Boosting (XGBoost), gradient boosting machine (GBM), long short-term memory (LSTM), and convolutional neural network (CNN) are compared to forecast the Nasdaq Clean Edge Green Energy Index (NasdaqClean). Discrete wavelet transform (DWT) and continuous wavelet transform (CWT) are used for feature extraction and visualizations, capturing both short-term fluctuations and long-term trends. Shapley additive explanations (SHAP) and permutation feature importance (PFI) assess feature contributions. Analysis across sub-periods, including the Paris Agreement, COVID-19, and the Russia–Ukraine conflict, reveals that different models perform optimally in different periods. Specifically, Wavelet-SVR emerges as the most accurate model in the entire dataset, before the Paris Agreement and Paris Agreement periods, demonstrating strong predictive power by reducing noise and enhancing feature extraction. LSTM performs best during COVID-19, capturing long-term dependencies and volatile market dynamics. Meanwhile, CNN yields the most accurate predictions during the Russia–Ukraine conflict, effectively identifying spatial patterns in the dataset.

This paper analyzes the connectedness between gold, wheat, and crude oil futures, Bitcoin, carbon emission futures, and international stock markets in the G7, BRICS, and Gulf regions with the outbreak of exogenous and unexpected shocks related to health, banking, and political crises. To this end, we use a wavelet-based method on the returns of different assets during the period 2 January 2019, to 21 April 2023. The empirical findings show that the existence of time-varying linkages between markets is well documented and appears stronger during the COVID-19 pandemic. However, it seems to diminish for some associations with the advent of the Russia-Ukraine War. The empirical results also show that investor risk perceptions measured by the VIX are negatively and substantially linked to stock markets in different regions. Other interesting findings emerge from the connectedness analysis with the outbreak of Silicon Valley bankruptcy. In particular, Bitcoin tends to regain its role as a safe-haven asset against some G7 stock markets during the bank crisis. Such findings can provide valuable insights for investors and policymakers concerning the relationship between different markets during different crises.