Iranian Journal of Finance

Abstract The main objective of this article is to investigate the impact of various financial, economic, and political risks and economic complexity on the development of the Sukuk market in the Iranian economy. The data required to conduct this research based on the variables of the proposed model were used from the Capital Market Central Asset Management Company, the International Country Risk Guide (ICRG) database, and the MIT University website. The data relating to 2010-2022 is seasonal, and REVIEWS 13 software was used. The model estimation results using the Nonlinear Autoregressive Distributed Lag Model Approach (NARDL) show that the negative shock of political risk reduces the development of the Sukuk market in the short and long term. The negative shock of financial risk in the long term has a negative impact on the development of the Sukuk market. The negative shock of economic complexity reduces the development of the Sukuk market in the short term. The positive shocks of political risk, financial risk, economic risk, and economic complexity in the short and long term led to the development of the Sukuk market. Among the three types of risk, political risk and financial risk have the most impact on sukuk market development. The error correction coefficient in this estimate is negative and statistically significant, which shows that 0.42% of the short-term imbalance is adjusted to reach the long-term balance every year.

Abstract The mutual relationship between monetary and fiscal policies and value at risk is one of the most important topics in the financial economics literature and accounts for the vast majority of empirical studies. Therefore, the main objective of this paper is to investigate the effects of monetary and fiscal policies on conditional value at risk in the financial sectors of the stock exchange, bank and insurance during the years 1995-2017. For this purpose, by quantile regression method and in the form of Adrian and Brunnermeier approach, the conditional value at risk of these three financial sectors is estimated and then by using the seemingly unrelated regression equation approach in panel data evaluated the effect of liquidity money variables. The interest rate on facility payments, the real exchange rate, the government's budget deficit, real GDP growth, and the degree of economic openness are subject to conditional risk. The results of the model estimation indicate the significance of the effect of liquidity money, interest rate on facility payments and real exchange rate variables on conditional value at risk in each of three relevant equations, and real GDP growth variable in the model, Exposure to the conditional value at risk of the insurance sector has a negative and significant effect. Also, the degree of openness of the economy in any of the three estimated equations has no significant effect on the conditional value at risk.