monetary conditions index

Granular residual is created in the banking network when there are a few large banks with many small banks. In this case, the effect of the shocks on each bank not lost in total that can lead to macro-economic consequences. The transmission of monetary policy through change in bank facilities is one of the key mechanisms affecting monetary policies. The power of transmission of monetary policy mechanism is highly dependent on the specific characteristics of the banking network. Thus, the importance of granular residual and considering it on transmission of monetary policy in the banking network is important. The purpose of the present study is the effect of granular residual in Iran's banking network on the transmission of monetary policy through lending channel. Indeed, the granular residual was imported in this study as a independent variable that can effect on bank facilities and lending channel. Therefore, in the present study, the balance sheet data of 32 banks from the banking network for the period 2001-2014 were used.Themodel used in this study was estimated through the Generalized Method of Moments (GMM). The results showed that in the first, according to available theories in relation to the granular residual and conducting relevant empirical analyses, the presence of granular residual in Iran's banking network is confirmed. Also as expected, granular residual has a positive and significant effect on Iran's banking network facilities and the transmission of monetary policy through lending channel is weakened by increasing the granular impacts.

Stock return predictability has been extensively considered as a stylized reality. Theories indicate that returns should change along the time, and various studies have presented evidence on this point. On the other hand, there is an optimal portfolio in each regime, and one cannot claim that a specific portfolio can minimize risk and returns in each regime. On the other hand, the financial conditions index (FCI) is an important index to specify monetary policy conditions. Regarding the importance of the issue, this research aims to present a comprehensive index, including all monetary transmission mechanisms. In this regard, it is attempted to improve the efficiency of stock return predictability in Iran's economy by incorporating an FCI and identifying relationships between FCI and stock returns using the TVP-DMA model, which can resolve shortcomings of traditional models. The study is applied research in terms of purpose. Seasonal data over the period of April 1991 to July 2019 is used. The results based on TPV, DMS, and DMA models indicate that liquidity growth rate, economic growth rate, unemployment rate, exchange rate, financial condition index, oil revenues, misery index, and budget deficit, has significantly affected factors of stock returns in 30, 50, 11, 49, 66, 54, 7, and 84 periods of 104 periods, respectively. Accordingly, budget deficit, financial condition index, oil revenues, and economic growth are the most effective factors of stock returns predictability in Iran. Further, the incorporation of flexibility in coefficients of the financial development index leads to higher forecast accuracy.