This study investigates the divergent roles of financial institutions and financial markets in shaping climate resilience, with a focus on lower- and middle-income countries. Using a panel dataset spanning 1996–2021, we employ fixed effects models with Driscoll–Kraay standard errors and instrumental variable (IV-2SLS) techniques to address cross-sectional dependence, heteroskedasticity, and endogeneity. Our findings reveal that well-developed financial institutions, such as commercial and development banks, have a consistently positive and significant impact on climate resilience, especially when supported by strong governance frameworks. Interaction effects show that governance quality, particularly regulatory quality and control of corruption, significantly amplifies the effectiveness of financial institutions. Conversely, the role of financial markets appears more complex and context-dependent: in the absence of robust governance, financial markets can exhibit negative or neutral effects on resilience outcomes. These results underscore the importance of institutional quality in determining whether financial development supports or hinders climate adaptation. The study offers actionable insights for policymakers seeking to leverage finance for climate-resilient development in emerging economies.

Predicting volatility in financial markets is an important task with practical uses in decision-making, regulation, and academic research. This study focuses on forecasting realized volatility in stock indices using advanced machine learning techniques. We examine three key indices: the Shanghai Stock Exchange Composite (SSE), Infosys (INFY), and the National Stock Exchange Index (NIFTY). To achieve this, we propose a hybrid model that combines optimized Variational Mode Decomposition (VMD) with deep learning methods like Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU). Using data from 2015 to 2022, we analyse how well these models predict volatility. Our findings reveal distinct patterns: the SSE shows high unpredictability, INFY is prone to extreme positive volatility, and NIFTY is relatively moderate. Among the models tested, the Q-VMD-ANN-LSTM-GRU hybrid model consistently performs best, providing highly accurate predictions for all three indices. This model has practical benefits for financial institutions. It improves risk management, supports investment decisions, and provides real-time insights for traders and risk managers. Additionally, it can enhance stress testing and inspire innovative trading strategies. Overall, our study highlights the potential of advanced machine learning, especially hybrid models, to address financial market complexities and improve risk management practices.

The functioning of energy markets is essential for global stability and is heavily influenced by geopolitical risks. Understanding these risks is critical for policymakers, market analysts, and nations. This study investigates the impact of geopolitical risks and their components on the futures markets of WTI crude oil and natural gas, utilizing time and frequency connectedness analysis along with impulse response function methods. The analysis is based on a dataset comprising daily prices of spot and futures contracts (across various maturities) as well as treasury yields. Our findings reveal that geopolitical risks have a significant, negative impact on the interest-adjusted spread of WTI crude oil. In contrast, the interest-adjusted spread of natural gas futures (NGF) displays a more complex pattern: while short-term maturities show an insignificant response, long-term maturities exhibit a significant reaction. Spillover effects are more pronounced in the short term but tend to weaken over longer horizons. This study underscores the dynamic influence of geopolitical risks on both key energy markets. Its findings offer a practical framework for risk management, equipping market participants and policymakers with valuable insights to better understand and respond to geopolitical risks in the energy sector.