The relevance is determined by the need to investigate the 2010 eurozone sovereign debt crisis and the factors that led to it. These factors range from a combination of international trade imbalances, the impact of the global crisis from 2007 to 2012, the failure of European governments' bailout approaches that created barriers for bondholders in both banking and the private sector, and high-risk lending policies and loans, forced by unregulated credit requirements between 2002 and 2008 and fiscal policy choices related to government revenues and expenditures. The aim of the study is to model data to identify a set of primary risk factors and identify economic variables and their impact on both the local economy of Greece and the financial markets associated with it. As a result of the study, a set of primary risk factors and their impact on the local economy of Greece, the domestic financial market based on external sources, was identified to validate the analysis. Methods of statistical analysis and macroeconomic modeling of data were used for this purpose. Generalised models of autoregressive conditional heteroskedasticity based on data provided by the World Bank Data Portal have become a method of modeling. As a result, the autoregressive conditioned heteroskedastic (ARCH) and the generalised autoregressive conditioned heteroskedastic (GARCH) models, built and trained on the data of 2006-2009, were obtained, predicting volatility from 2010. It was found that the model of the autoregressive integrated moving average is not suitable for the task because there was no noticeable autocorrelation. It was found that volatility has a tendency to fade. This observation coincides with reality. Systemic risk indicators, mainly used to forecast national risk, are usually based on insider data from rating agencies or financial institutions. This article provides results close to the composite indicator of systemic stress provided by the European Central Bank (ECB) using the ARCH and GARCH models on publicly available data. Practical significance refers to the principle of model formation, which allows creating risk indicators based on publicly available state financial data.