درخت حوزه‌های تخصصی

Inflation targeting framework has become a predominant monetary approach across the globe. Williams (2015) believes that in a very real sense, almost all economies are inflation targeters -either explicit or implicit- now.(1) Due to the increasing spread of this policy, it is necessary to consider the way it affects macroeconomic variables. using prevalent economic models for evaluating the effectiveness of inflation targeting causes bias selection which for solving this problem, it is proposed propensity score matching model. the goal of this paper is to evaluate the effect of the adoption of Inflation Targeting policy on indirect taxes and its components in two selected groups of oil exporter and oil importer countries during 1990-2016 by using propensity score matching model. our results have shown that inflation targeting adoption in average has a positive and significant effect on indirect tax revenues in oil importer countries group but its effect on indirect tax revenues in oil exporter countries is insignificant. JEL Classification: E5, H2, C21.

This paper aims to estimate the Value-at-Risk (VaR) using GARCH type models with improved return distribution. Value at Risk (VaR) is an essential benchmark for measuring the risk of financial markets quantitatively. The parametric method, historical simulation, and Monte Carlo simulation have been proposed in several financial mathematics and engineering studies to calculate VaR, that each of them has some limitations. Therefore, these methods are not recommended in the case of complications in financial modeling since they require considering a series of assumptions, such as symmetric distributions in return on assets. Because the stock exchange data in the present study are skewed, asymmetric distributions along with symmetric distributions have been used for estimating VaR in this study. In this paper, the performance of fifteen VaR models with a compound of three conditional volatility characteristics including GARCH, APARCH and GJR and five distributional assumptions (normal, Student’s t, skewed Student’s t and two different Lévy distributions, include normal-inverse Gaussian (NIG) and generalized hyperbolic (GHyp)) for return innovations are investigated in the chemical, base metals, automobile, and cement industries. To do so, daily data from of Tehran Stock Exchange are used from 2013 to 2020. The results show that the GJR model with NIG distribution is more accurate than other models. According to the industry index loss function, the highest and lowest risks are related to the automotive and cement industries.