درخت حوزه‌های تخصصی

In Iran, especially in the aftermath of economic sanctions of the 2010s, and the intensification of the government's budget deficit, since 2012, there was a diminishing investment, followed by a decrease in production which led to stagnation in the economy. The government plays its economic role by implementing fiscal policy. However, when it is associated with uncertainty, the effectiveness of fiscal policy may be undermined. Therefore, we need to measure the uncertainty of fiscal policy in Iran. We measured the uncertainty index of fiscal policy in Iran using a specified fiscal reaction function. In this function, there are two types of shocks. First, fiscal level shock and second, volatility shock which is a proxy for fiscal policy uncertainty. To this end, we employ particle-filter to estimate fiscal policy uncertainty in Iran. In this paper, by measuring the structural budget balance function for Iran, we utilize it as a dependent variable. The results indicate that the trend of balance or total budget deficit has a compliance with the trend of balance or structural budget deficit for Iran suggesting that the nature of government budget in Iran is a structural one, and in the 2010s the sanctions, adversely affected on the government budget. In 2012, the intensification of sanctions led to the increase in the uncertainty of fiscal policy until 2015 that the trend of fiscal policy uncertainty was mildly downward. In 2017, the uncertainty index of Iran's fiscal policy has increased rapidly until it reached its maximum in 2020, in which the economy did not face with such uncertainty for fiscal policy since 1979.

This paper intends to model the volatilities of returns of 20 different cryptocurrencies using daily data from 08/03/2018 to 09/20/2022. The multivariate factor stochastic volatility model (MFSV) within the framework of the nonlinear space-state approach is used. In this method, the cryptocurrency return volatility is decomposed into volatility rooted in latent factors and idiosyncratic volatility, and the time-varying pairwise correlation and dynamic covariance matrix are estimated in four sub-periods. The MFSV model’s results revealed that each sub-period contains a distinct number of latent factors, 2, 5, 4 and 2, which generally have a favorable impact on all cryptocurrency volatilities. The time-varying positive correlations between the return volatility of all cryptocurrencies are confirmed. Indeed, the strongest pairwise correlations belong to Ethereum, Litcoin, EOS, and VET in each sub-period, respectively. The DOGE, DOGE, Filecoin, and XRP, on the other hand, showedthe weakest correlations . As the pairwise correlations of cryptocurrency volatilities get strenger, especially during descending periods, it seems that the benefits of diversifying a crypto portfolio are getting less and less over time.