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Volume 7, Issue 3 - September 30, 2012
JAQM Volume 7, Issue 3 - September 30, 2012
Credit Accelerator, CDS Rate and Long Term Yields: Empirical Evidences from the CEE Economies (p )
The study aims to investigate the mechanism by which lending to private sector may induce risks to the long-term interest rates convergence process in the new EU Member States. The added value of this approach consists of three elements. First of all, the analysis provides a quantitative mechanism for assessing the fundamental dependence of the bank portfolio quality to the dynamics of the credit accelerator, econometric results showing that about 30 percent of the squared change in the private sector credit flow is reflected in the jump of the rate of non-performing loans. Secondly, the study shows that sovereign risk premium is dependent on the stability of the banking system, considering that about 20 percent of the changes in the rate of non-performing loans are reflected in the level of the CDS rate. Third, empirical assessment highlights the importance of the sovereign risk premium transmission channel related to long-term interest rate, with approximately two thirds of the CDS rate contributing to the level of government bonds long-term yields. In this context, promoting a mix of macroeconomic policies oriented also to limiting the volatility of credit demand accompanied by poor multiplier effects in the economy becomes a fundamental requirement for ensuring a sustainable cost of financing long term public debt.
Nowcasting Economic Time Series: Real Versus Financial Common Factors (p )
In this paper we want to assess the impact of real and financial variables in nowcasting smoothed GDP. We implement the generalized dynamic factor model, on which Eurocoin indicator is based. We can assess that, during the structural break in 2008, the impact of real variables in estimating smoothed GDP becomes particularly relevant in relation to that concerning financial data as money supply, spreads.
Applying Fuzzy C-Means and Artificial Neural Networks for Analyzing the Non-Banking Financial Institutions’ Sector in Romania (p )
In this paper we apply a neural approach to develop classification models in order to assess the performance of non-banking financial institutions (NFIs) in Romania. Our objective is twofold: to empirically validate our methodology and understand how different financial factors can and do contribute to the NFIs’ movements from one performance class to another.