The impact of geopolitical risk transmission on banking stability: Evidence from ASEAN

The global economy has fostered a dynamic environment of economic globalization, leading to amplified interconnectedness, integration, and worldwide influence in both commercial transactions and monetary activities. This occurrence emphasizes the vital role of liberalizing capital and international trade in economic discussions, particularly in emerging economies where banking-centric systems wield considerable influence. The objective of this study is to investigate the correlation between trade liberalization and financial inclusivity, specifically concerning the resilience of the banking industry in developing nations throughout the period of 2010–2020. Utilizing the dynamic data model of Arellano-Bond’s Generalized Method of Moment Estimator, this study yields a significant revelation. The interaction between trade openness and financial transparency exerts a noticeable and advantageous impact on banking stability, with each 1% increase in openness resulting in a remarkable improvement of 98.9445 in Net Interest Margin, 116.2575 in Z score, and 119.9189 in Non-Performing Loans. Consequently, this investigation confirms the presence of a diversification effect on stability while concurrently applying the concept of voltage fragility. In essence, trade openness propels the banking sector toward heightened competitiveness due to increased demand from local businesses, while financial openness fosters heightened competition within the credit market.

Type of the article: Research Article

This study aims to investigate how geopolitical risk shocks influence return dynamics, volatility transmission, and hedging properties across sustainable financial assets, financial technology instruments, energy markets, and precious metals during major global crisis episodes over a daily period from June 15, 2018, to September 14, 2024. We examined three major events: the trade conflict between the United States and China from June 15, 2018, to November 30, 2019; the COVID-19 pandemic from December 22, 2019, to February 23, 2023; and the ongoing wars between Russia and Ukraine, as well as Hamas and Israel, from February 24 to September 14, 2024. We found anomalies explained by the volatility of these returns. Using static, dynamic, and fractional QVAR methodology, we concluded that gold and two indicators of green finance can be considered safe-haven assets and hedging instruments, while FinTech plays a stabilizing role during these crises. Spillovers and connectivity networks at the median quantile validate the negative impact of geopolitical risk on non-renewable energy. However, we observed that the geopolitical risk index does not significantly affect green finance indicators, eco-friendly cryptocurrencies, or various measures of FinTech, with a low sensitivity of this index to gold prices.

Type of the article: Research Article

Abstract
Digital financial inclusion (DFI) has become a critical driver of sustainable growth and financial resilience in the digital era, yet its implications for bank stability remain ambiguous, particularly across heterogeneous institutional contexts. This study examines whether and under what conditions DFI fosters bank stability, using data from 65 emerging and advanced economies during 2010–2022. Employing Double Machine Learning (DML) and Causal Forests to address endogeneity and treatment heterogeneity, together with Panel Threshold Regression (PTR) to capture nonlinear dynamics, the paper provides a causal and structural assessment of the DFI–stability nexus. Results reveal that, on average, DFI exerts no statistically significant impact on bank stability across the full sample. However, substantial heterogeneity emerges in financially developed and institutionally strong economies, DFI significantly enhances stability (CATE = +0.0165, p < 0.001), while in underdeveloped systems it weakens it (CATE = –0.0082, p < 0.001). The PTR model identifies a critical DFI threshold (–1.3611), below which DFI undermines stability and above which its effect becomes neutral, confirming nonlinear regime behavior. These findings highlight that DFI alone cannot guarantee stability; its benefits materialize only within robust institutional and financial ecosystems. Methodologically, the integration of causal machine learning and threshold modeling offers a novel framework for examining digital finance policies and contributes to a deeper understanding of conditional digital effectiveness in modern banking systems.