عباس فدایی

The present study aims at developing an aggregate financial stability index by using banking sector indices to assess financial stability and examine if the variable of credit-to-GDP gap corresponds to its long-term trend which represents the macro-prudential indicator has co-movement with the built financial stability index? To this end, monthly banking balance sheet data were collected from the Central Bank of the Islamic Republic of Iran from March 2007 to March 2017. Co-movement of two time series was assessed at two dimensions of time and frequency through wavelet analysis. It can be observed that there is a greater relationship between the two variables at short-term and medium-term. In the short-term, there is a negative correlation between financial stability and the representative of the macro-prudential variable. The increase of the credit-to-GDP gap results in a decrease in financial stability while these variables are positively correlated at medium-term. An increase in the credit-to-GDP gap increases financial stability, whereas such a relationship cannot be observed for the long-term. Thus, it seems necessary to adopt a macro-prudential policy more at medium term.

The aims of the present study are developing a financial stability index (FSI) using banking indices to measure financial stability in Iran, and examining the relationship between financial stability and macroeconomic variables for policymaking. To these ends, we have employed principal-component analysis, out of sample forecasting, Autoregressive Integrated Moving Average (ARIMA) method, and Vector Error Correction Model (VECM). The monthly data period is spanning 2007:3 through 2017:2. We find evidence of one cointegrating vector. According to the cointegration test, there is a long-run relationship running from inflation, Gross Domestic Product (GDP) growth rate, and unemployment to FSI. Also, the results of the Engle-Granger test indicate bidirectional causality between FSI and unemployment. Forecast evaluation shows that VECM-based FSI prediction is more accurate than the ARIMA model.