In the last few years, the financial condition index has been an important indicator to determine the situation of each country. The Financial Conditions Index can be defined as the current state of the variables that affect the future state of the economy. The Financial Conditions Index is a summary index of financial variables that is effective in predicting the economic situation of a country.
Financial conditions in any country are one of the most important issues in the economy. The purpose of this article is to determine and calculate the financial condition index for Iran using the basic component analysis method and time series data from 1990-2020. The results of calculating the financial condition index show that the credit channel has more weight than other channels and this shows the importance of the volume of credit in estimating the country's financial condition index during this period in the research. After that, the asset channel, ie the stock price index and the housing index, have the most weight. Among these, the exchange rate and interest rate channels have the lowest weight. Therefore, the central bank can adopt a forward-looking approach in monetary policy, so that by changing its retrospective approach in monetary policy decisions, it can show the most correct reaction.

In recent years, the financial condition index (FCI) has been used in many countries as an important index to determine the state of macroeconomic policies. For this purpose, in the present study, the effects of financial condition index on macroeconomic variables were investigated by applying the time-varying parameter factor-augmented vector autoregressive model (TVP-FAVAR) and using quarterly data during the period (1991-2019). The results indicate that the response type and response rate of macroeconomic variables were different due to the financial condition index shock over time, and this indicates the necessity of employing the parameter - variable approach. According to the obtained results , The unemployment rate and economic growth rate variables in the short and long term showed a Negative and positive response to behavioral changes in the financial condition index variable, respectively; The effects of the financial Conditions Index shock on the inflation rate variable appear after one period; However, the response of this variable to the financial condition index shock in the short and long term has been different according to the conditions prevailing on the economy of the country . Also, the financial conditions index shock in the short run has improved the Gini coefficient variable, but in the long run, especially in the late 2010s has rised the income gap. The response of the budget deficit variable to the financial condition index shock in the whole period under review was positive and the financial condition index shock has increased the government budget deficit.

The central banks typically respond to inflationary fluctuations and production gaps by using monetary policy tools. The financial crisis in 2007 indicated that the monetary policy of central banks and pricing tools used to stabilize the economy have not been effective. Thus central banks utilized unconventional monetary policy to achieve financial stability the index of financial conditions indicates the financial climate affecting firms and households. This paper aims to identify and analyze the channel of monetary policy transmission and estimate the effect of the index of financial conditions on Iran's economic activity during the 2005-2017 period. Then, using the forward and backward distribution in the Bayesian VAR model, the impulse variables and immediate response to the financial condition indicator in the period under review are estimated. The findings of the paper indicate that financial conditions have negatively affected GDP and private sector investment, and credit growth has played a significant role in the financial condition index.

Today, the financial condition index is introduced as a tool for forecasting macroeconomic variables in many countries of the world. The aims of the present study are, first, to construct the financial condition index in different patterns of time-varying parameters and then, to analyze the performance of the financial conditions index in order to forecast the macroeconomic variables using the mean squared forecasting error approach (MSFE) and the cumulative sum of log-predictive likelihoods.The results show that the constructed financial conditions index can adapt to different states of Iran's economy and its fluctuations increase over time. Based on the obtained results, the time-varying parameter factor-augmented vector autoregressive model (TVP-FAVAR) shows less forecast error in some macroeconomic variables compared to the factor-augmented vector autoregressive model (FAVAR) and also the vector autoregressive time-varying parameter factor-augmented model (FA-TVP-VAR). The use of combined time-varying parameter factor-augmented vector autoregressive models when the coefficients and selected variables of the financial conditions index change depending on the economic condition, improve the performance of the financial conditions index in forecasting macroeconomic variables. The cumulative sum of log-predictive likelihoods shows that using variable parameter patterns does not have a great effect in reducing the forecast error of some macro variables.

Monetary Policy is one of the important instruments of the economic demand management and is much important in policy making decisions. In this direction, determination the rule of monetary policy making and discretion of expansionary and contractionary monetary policies are also important for policy makers and economists. In this study, the monetary conditions index (MCI) over the period 1352-91 and financial conditions index (FCI) over the period 1377-91 calculated using the Principal Component Analysis for Iran's economy. According to the results, the monetary conditions index impact any 3 channel exchange rate, interest rate and credit channel. Also in the calculation of index financial conditions, in addition to the the above channel 3, also has its effect channel Finance. Credit channel has the greatest weight in both financial and monetary conditions index, while the interest rate channel of the lowest weight than the 3 other channels in both the index, implying that credit channel is more important than the other channels in determining the level of output in Iran.

Structural changes and economic and financial condition change the nature of economic models, and this shows the importance of using models that consider the dynamics of model parameters over time.In this paper, we use factor augmented vector autoregressive models with time-varying coefficients and stochastic volatility to construct a financial conditions index that can track expectations about financial conditions and economic trends. Time variation in the model’s parameters allows for the weights attached to each financial variable in the index to evolve over time. Furthermore, we develop methods for dynamic model averaging or selection that allow the financial variables entering into the FCI to change over time. The results show that the financial conditions in our country have been associated with many instabilities, which has periodically weakened the efficiency of the country's economy by creating imbalances in the financial system of the economy.

Due to the forward-looking nature of economic decisions, forecasting the trend of macroeconomic variables has a fundamental role in economic planning and policies. For this purpose, the present study attempts to extract the financial conditions index for the period 1991-2019, then the movement trend of macroeconomic variables has been forecasted using the Time-Varying Parameter Factor-Augmented Vector Auto-Regressive (TVP-FAVAR). The results show that the financial conditions index has undergone significant changes in recent years, especially in the 2010s, which has had an effective role in the fluctuations of other markets due to the influence and change in the behavior of other macroeconomic variables. Furthermore, forecasting the trend of macroeconomic variables also indicates that this trend is unstable; in other words, instability in the trend of macroeconomic variables is evident and we cannot expect a positive outlook for the above variables.

This paper used a factor-augmented vector autoregressive model with time-varying coefficients to construct a financial conditions index. Time variation in the model’s parameters allowed the weights to be attached to each variable in the index to evolve and evaluate dynamics across time. The ability of the constructed index to predict various variables was also evaluated. The Financial Condition Index (FCI) was estimated by using the TVP-FAVAR method based on the quarterly data of the period of 1989-2019. The variables used included interest rate, exchange rate growth, inflation rate, consumption growth, banking facility growth, total stock market index growth, money supply growth, oil revenue growth, and gross domestic product growth rate. The findings indicated significant volatilities in the model’s parameters. The shock from improving the FCI led to a positive response to the stock market index. According to the findings, the constructed FCI had high predictability.

Since financial developments that are not driven by fundamental factors of monetary policy may have a large impact on the economy, policymakers have placed significant emphasis on the financial conditions index over the past recent years. Policymakers must maintain a vigilant watch over financial conditions, as their significance becomes increasingly significant. The construction and use of the financial condition index include three issues, which are: a) the selection of financial variables to enter the financial index, b) weights used to relate financial variables to the index, and finally c) the relationship between this index and the macroeconomy. There are many reasons for the changeability of these three cases over time, which can be discussed about the reasons for their occurrence and effect on the results. Many changes affect the way a financial index is made. Therefore, in this research, the goal is to design an index of financial conditions using time-varying dynamic models to improve the forecasting of macroeconomic variables.

The process of implementing the conceptual model can be explained in the following steps. First, extracting the desired variables to be used in the desired models (for this purpose, we have used monthly data during the years 2001 to 2021 for 19 financial variables and 5 macroeconomic variables). It should be noted that since all the variables must be in the form of rates - if necessary, all the variables were converted into growth rates. Also, all the variables were examined from the dimension of stationary, and the problem of their stationary has been solved. Second, the desired models were calculated to predict macroeconomic variables. Using time-varying models, dynamic averaging models, and dynamic selection models, the financial condition index was constructed in such a way that this index could include various variables to adopt different coefficients from its previous and subsequent periods and challenge constant parameter and variable models. Third, the predictive power of each of the proposed models in the estimation of macroeconomic variables (sum of squares of prediction error) was analyzed and estimated. Fourth, financial index extraction was done based on the model selected in the third step.

The findings suggest that employing models that solely consider variability in the coefficients (without accounting for variability in the variables) leads to enhanced predictions of the unemployment rate compared to vector autoregression models and vector autoregression models incorporating the basic component. In addition, by moving from the generalized factor vector autoregression models and the time-varying generalized factor vector autoregression and the generalized factor time-varying vector autoregression, in which only the variability in the parameters is included (without considering the variability in the variables) towards the models that consider the variability in the parameters both in a Bayesian and dynamic way, a reduction will occur in the forecast error in the variables of the consumer price index, liquidity, monetary base, and gross domestic product. However, the amount of improvement in each of the variables is different from each other.

This study employed time-varying models to derive the financial condition index, aiming to provide the most accurate estimation of macroeconomic variables. The findings demonstrate that the utilization of these models outperforms other approaches in forecasting a majority of the macroeconomic variables

In this study, the asymmetric effects of financial conditions on the growth of GDP in Iran are investigated. For this purpose, in the first stage, to extract the weights; It includes 12 variables for the construction of Iran's financial conditions index (IFCI) in order to investigate the current issue in the form of the final quantile model for the period of 1991-2021. Based on the results; in the first and second (lower) quadrants; Financial condition indicators (IFCI) have a negative effect on GDP growth, and in the third and fourth quarters, the intensity of its impact on GDP growth increases. In other words, the financial condition indices (IFCI) of the first (Q1) and second (Q2) quarters have a strong negative temporal correlation with the GDP growth. The fluctuations of downside risks are more severe than those of upside risks, especially in bad financial conditions. According to the chart trend of forecasting the GDP growth of the last quarter, the financial shocks are affected by the first, second and third order shocks. As the financial conditions worsen, the average GDP has decreased, and finally the costs have increased despite a financial crisis. These fluctuations affect the amount of investment by affecting the indicators related to production. Due to the different infrastructures, a separate study of how production is influenced by the uncertainty of the government's monetary policies, government's financial policies, and government's currency policies can provide a correct view of how Iran's financial market changes due to these fluctuations in the macro decisions of the country.