Bitcoin has received a lot of attention from both investors and analysts, as it forms the highest market capitalization in the cryptocurrency market. This paper evaluates the volatility of Bitcoin returns using three GARCH models (sGARCH, iGARCH, and tGARCH). The new development allows for the modeling of volatility clustering effects, the leptokurtic and the skewed distribution in the return series of Bitcoin. Comparative to the Students't-distribution and the Generalized error distribution, the Normal Inverse Gaussian (NIG) distribution captured adequately the leptokurtic and skewness in all the GARCH models. The tGARCH model was the best model as it described the asymmetric occurrence of shocks in the Bitcoin market. That is, the response of investors to the same amount of good and bad news are distinct. From the empirical results, it can be concluded that tGARCH-NIG was the best model to estimate the volatility in the return series of Bitcoin. Generally, it would be optimal to use the NIG distribution in GARCH type models since time series of most cryptocurrency are leptokurtic.

In this paper,we investigate the mean and volatility spillover between the price of green bonds and the price of renewable energy stocks using daily price series from 02/11/2011 to 31/08/2021. The unrestricted trivariate VAR-BEKK-GARCH model is employed to examine potential causality,mean,and volatility spillover effects from the green bond market to the renewable energy stock market and vice-versa. The results from the VAR-BEKK-GARCH model indicate that there exists a uni-directional Granger causality from renewable energy stock prices to green bond prices. While the price of green bonds is positively influenced by its own lagged values and the lagged values of renewable energy stock prices,only the past price value of renewable energy stocks has a positive effect on the current price value. We identified a uni-directional volatility spillover from renewable energy stock prices to green bond prices. However,there was no shock spillover from both sides of the market. This research shows that investors in the green bond market should always consider information from the renewable energy stock market because of the causal link between renewable energy stocks and green bonds.

Due to the inherent chaotic and fractal dynamics in the price series of Bitcoin, this paper proposes a two‐stage Bitcoin price prediction model by combining the advantage of variational mode decomposition (VMD) and technical analysis. VMD eliminates the noise signals and stochastic volatility in the price data by decomposing the data into variational mode functions, while technical analysis uses statistical trends obtained from past trading activity and price changes to construct technical indicators. The support vector regression (SVR) accepts input from a hybrid of technical indicators (TI) and reconstructed variational mode functions (rVMF). The model is trained, validated, and tested in a period characterized by unprecedented economic turmoil due to the COVID‐19 pandemic, allowing the evaluation of the model in the presence of the pandemic. The constructed hybrid model outperforms the single SVR model that uses only TI and rVMF as features. The ability to predict a minute intraday Bitcoin price has a huge propensity to reduce investors’ exposure to risk and provides better assurances of annualized returns.

This study utilizes the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework to investigate the interconnectedness of green bond with various financial markets, aiming to clarify their relationship with global economic uncertainty and their impact on returns. After a comprehensive search of pertinent research papers from January 2016 to September 2023, 79 relevant articles were identified. The analysis delves into the evolution of research on green bonds' interactions with economic policy uncertainty considering the financial markets, analytical methodologies, contributions to the field, and the role of green bonds under both normal and extreme market conditions. The study reveals noteworthy findings: firstly, the interplay between green bonds and financial markets is influenced by macroeconomic factors, such as the COVID-19 pandemic and the Russia-Ukraine conflict in 2022, which were significant sources of economic policy uncertainty during the study period. Secondly, during times of global economic uncertainties, green Bonds act as net transmitters of spillovers in the short term but shifts to net receivers in the long term, positioning them as strategic hedging assets rather than safe-havens, particularly against spillovers from crude oil and CO2 emission in times of economic uncertainties. Additionally, the review highlights prevalent methodologies employed to assess the relationship between global economic policy uncertainty and green bonds. Some of which include quantile approaches, the Diebold & Yilmaz 2012 spillover index, as well as various models like VAR models, GARCH models, ARDL models. Notably, certain countries like China, the United Kingdom, and Vietnam emerge as key contributors to this research domain. The review not only consolidates existing knowledge but also provides valuable insights for investors and policymakers regarding green bonds in terms of risk management and asset allocation, while also pointing towards potential avenues for future research in this field.

Climate variability has become one of the most pressing issues of our time, affecting various aspects of the environment, including the agriculture sector. This study examines the impact of climate variability on Ghana’s maize yield for all agro-ecological zones and administrative regions in Ghana using annual data from 1992 to 2019. The study also employs the stacking ensemble learning model (SELM) in predicting the maize yield in the different regions taking random forest (RF), support vector machine (SVM), gradient boosting (GB), decision tree (DT), and linear regression (LR) as base models. The findings of the study reveal that maize production in the regions of Ghana is inconsistent, with some regions having high variability. All the climate variables considered have positive impact on maize yield, with a lesser variability of temperature in the Guinea savanna zones and a higher temperature variability in the Volta Region. Carbon dioxide (CO₂) also plays a significant role in predicting maize yield across all regions of Ghana. Among the machine learning models utilized, the stacking ensemble model consistently performed better in many regions such as in the Western, Upper East, Upper West, and Greater Accra regions. These findings are important in understanding the impact of climate variability on the yield of maize in Ghana, highlighting regional disparities in maize yield in the country, and highlighting the need for advanced techniques for forecasting, which are important for further investigation and interventions for agricultural planning and decision-making on food security in Ghana.