Dynamic stability of the financial monitoring system: Intellectual analysis

Introduction: Although there is a growing number of scientific publications on financial monitoring, combating money laundering, the shadow economy, and the impact of corruption on economic development, further research needs to determine the stability of the national financial system in dynamics. The dynamic stability of the national financial monitoring system subjects will allow to adequately assess the effectiveness of the existing national financial monitoring system in each country and determine the influential factors. Materials and methods: The article investigates an approach to identifying the dynamic stability of the national financial monitoring system subjects based on the calculation of the integrated indicator of the country’s financial system propensity to ALM, vector autoregression (VAR) model taking into account time lag. The proposed integrated indicator allowed to adequately assess the existing financial monitoring systems of the countries (15 countries of the European Union for 2000–2020: Austria, Belgium, Cyprus, Estonia, Finland, France, Greece, Ireland, Italy, Latvia, Malta, Netherlands, Portugal, Slovak Republic, Spain). In addition, vector autoregression models (VAR) of the dependence of the country’s financial system propensity to ALM on the regressors Government Integrity, Index of economic freedom, Monetary Sector credit to the private sector (% GDP), were built, taking into account time lags in general and for each studied country. Results: According to the modeling results, the national financial monitoring systems in Austria, Belgium, Estonia, Finland, France, Ireland, Netherlands, Slovak Republic, Spain were resistant to money laundering. It is vice versa in Malta, Greece, Cyprus, Portugal, Italy, Latvia. These conclusions are also confirmed based on a binary approach. Such exogenous variables as Government Integrity (with a lag of 2 years) and the Index of economic freedom (taking into account the time delays of the regression reflection under the influence of this regressor for 1 and 2 years) have a statistically significant effect on the country’s financial system. Conclusion: The general vector autoregression (VAR) model shows that the current value of the country’s financial system propensity to ALM by 92.78% is determined by its previous value. With an increase of Government Integrity by 1%, the country’s financial system’s propensity to ALM will decrease by 0.000616 units with a lag of two years. The nature of the impact made by the Index of economic freedom on the performance feature was specific—when this indicator increases by 1% for a lag delay in one year, the PFSALM value will decrease by 0.001997 units, and for a lag delay of two years it will change the trend and increase by 0.003076 units per unit, respectively.