Generalized Autoregressive Conditional Heteroskedasticity Modeling of One-Year Maturity Government Bonds of Greece During Sovereign Debt Crisis of Eurozone in 2010

Numerous factors have led to the Euro Area sovereign debts' crisis of 2010. These factors range from a combination of international trade imbalances, the impact of the global crisis from 2007 to 2012, failure in bailout approaches of European governments that troubled banking industries and private bondholders, high-risk lending and borrowing policies enforced by unrestricted credit requirements during the period from 2002 to 2008 and fiscal policy choices related to government revenues and expenses. The objective is to model the boiling state of the Greek local financial market before the peak of the Sovereign Debt Crisis of Eurozone in 2009, modelling the insights of foreign investors and credit rating organizations. We will identify a set of primary risk factors and their effect on both the local economy and the markets involved to validate the analysis done. In this paper will use both statistical analysis and macroeconomic data modelling techniques to identify a set of primary risk factors or economic variables and their effect on both the local economy of Greece and the markets involved. The selected method of modeling is Generalized autoregressive conditional heteroskedasticity models. The research is based on the data provided by World Bank Data Portal. Results obtained are fitted of 2006-2009 years data Autoregressive Conditional Heteroskedasticity (ARCH) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models, forecasting market volatility in 2010 and on. We have discovered, that the Auto Regressive Integrated Moving Average model is not suitable for this problem as there was no notable autocorrelation. The volatility seems to fade out. This observation coincides with reality, as the crisis is about to peak and descend. Systemic risk indicators, primarily used for forecasting state-wide risk, are usually built on insider data of rating agencies or financial institutions. In this paper we obtain results close to Systemic Stress Indicator provided by European Central Bank (ECB) using ARCH and GARCH models on public data. The practical importance is model generation principle, which allows creating a risk indicator based on public financial data.