Abstract
This study examines the impact of financial crimes and terrorism on banks' financial stability, using a quantitative approach supported by verified international datasets. The quantitative approach is appropriate because the study seeks to statistically estimate the relationships between dependent and independent variables using verifiable macro-level datasets. The study employs the Panel Autoregressive Distributed Lag (P-ARDL) model to analyse both the short-run and long-run dynamics between variables. The P-ARDL technique is suitable because it accommodates explanatory variables with different orders of integration, and effectively handles heterogeneous panel structures involving multiple countries across time. Financial crimes are measured using three validated proxies: the Basel AML Index, the Corruption Perceptions Index (CPI), and Control of Corruption (CC) in the World Governance Indicators (WGI). Terrorism risks are measured using the Global Terrorism Index (GTI) and the WGI-Political Stability and Absence of Violence/Terrorism (PV). Bank financial stability is the dependent variable, which is measured using Bank Z-score. In addition, the study used Regulatory Quality (RQ), financial- sector indicators such as bank concentration and as well as macroeconomic variables such as GDP growth, and trade openness as control variables. Data was collected over a minimum time frame of 10 to 12 years (2012-2023) for a sample of 62 countries derived based on data availability. The long-run P-ARDL results indicate that financial crimes, measured through the Basel AML Index, Corruption Perceptions Index (CPI, inverted), and the World Governance Indicators' Control of Corruption (CC), significantly reduce the stability of banks. The short-run dynamics reveal that immediate changes in financial-crime and terrorism indicators also significantly affect bank stability, although the magnitude of these effects is smaller than in the long run. This study concludes that financial c
References
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