فصلنامه اقتصاد مقداری
سال سیزدهم شماره 3 (پیاپی 50، پاییز 1395)

Value at risk is a new risk measure for measuring risk in financial institute. In this paper, a practical model base on fuzzy credibility theory for measuring value at risk is introduced. For this purpose, return of assets are considered in shape of triangular fuzzy numbers and value at risk is estimated by credibility distribution for triangular fuzzy variables. Then, value at risk for an investment company with different approaches is calculated for analyzing the results of this modeling approach and obtaining the appropriate time window. For this purpose, value at risk is estimated by three methods which are using fuzzy credibility theory with the time windows of 6 months and 4 months for parameters estimation and the traditional method of simple variance-covariance and the results are compared by Bernoulli unconditional coverage test. The result shows that value at risk by fuzzy credibility theory which use the four-month window of time for estimating of parameters provide more accurate specifying. Because return and risk of assets are uncertain variables in real world, using this model can transfer calculations to the uncertain environment and researchers are leaded to correct conclusions. Moreover, introduced model reduce volume of computations dramatically and value at risk is estimated easier than conventional methods such as methods base on GARCH family.

So far, a variety of methods have been used to detect the price bubbles in asset markets. Regarding to criticisms on the common tests to detect the bubbles, in this paper, we have used the Supremum Augmented Dickey-Fuller (SADF) and Generalized Supremum Augmented Dickey-Fuller (GSADF) Right-Tailed unit root tests to find and detect the bubble periods in Tehran stock exchange market from 2002:03 to 2016:01. Unlike the conventional methods to detect the price bubbles, these tests have the ability to exoplor the explosive behavior, detect the presence of multiple bubbles at a time period, and estimat the creation and collapse date of each bubbles. The results of the tests have confirmed the explosive behavior and the presence of multiple bubbles in the Iran stock market. Also, all three indices (the total price index, real total price and price-dividend ratio indices) jointly have showed the bubbles in 2003:06-2003:08, 2009:09-2009:11 and 2011:03-2011:05. Moreover, based on the all indexes, the Tehran stock market had no bubble in 2015.

This study has been investigated price regimes of two prime index in the world oil market(Brent and WTI) based on weekly data over the period 2003/1/3-2007/5/25 (before financial crisis) and 2009/3/6-2012/12/14(after financial crisis) by using marko regime switching model whit dynamic autoregressive coefficient. The results show that the model MSMAH(3)-AR(2) is the optimal model for description price regimes of Brent and WTI index before and after the financial crisis. Also the recent financial crisis causes a change in dominate price regime of Berent while WTI tends to stay in the its previous regime. This may lead to the abnormal price spreads between them after the financial crisis. in our opinions, the reasons for this are attributed to their difference fundamentals drivers and closely related whit the recent dynamics in crude oil markets

Proponents of real business cycles emphasize the roles played by employment and technological cycles in creation of these business cycles. This study attempts to examine the effects of these cycles on creation of business periods in Iran during 1959 – 2014 period. To this end, first target cycles were extracted via Hodrick–Prescott filter and then these cycles were analyzed by employing Markov switching multifractal model.The results revealed that business cycles in Iran were asymmetric in the studied period and booming cycles were stronger and longer compared to recession cycles. They also revealed that capital accumulation, employment and productivity cycles were dependent, leading and concurrent variables, all three favoring the business cycles.Hsiao's Granger causality test also showed that capital accumulation cycles and business cycles had mutual causality. However, business cycles had unilateral causal relationships with two other variables. Therefore, generally speaking, the research findings indicate the significance of supply side variables in production changes and highlight the importance of these three variables in managing business cycles.

Over the last decade, especially after the recent global financial crisis, the governments financial plans and their effects on macroeconomic variables, has been the central issue of debate among economists. Private consumption is also among the variables that are in close relationship with government programs. Because of the importance of private consumption on the one hand and the problem of chronic budget deficit and growing public debt in the developing countries on the other hand, this paper investigates nonlinear effects of government debt on private consumption in OPEC countries for the period of 2000 to 2012, using Panel Smooth Transition Regression (PSTR) model. To this end, Debt-to- GDP ratio is used as transition variable. The results showed a strong nonlinear relationship between the variables studied and suggest a two-regime model with a threshold parameter of 7 percent and the slope parameter of 8669/1. In the first regime, government debts have a positive impact on private consumption but crossing the threshold value, in the second regime, have a negative impact. Therefore, in high levels of government debt because the probability of the government's ability to repay debt and the resulting increase in tax from people to finance this debt, is high, Thus, households expect higher taxes in the future, and the current consumption can be reduced.

The main reason of this study is investigation in effect of the number of large industrial segments on Gini coefficient from 1974 to 2012. To this end, linear and nonlinear models are estimated. The results show that nonlinear model has more explanatory power than linear model. We defined three threshold include up, middle and down threshold, with regard to acceptance of nonlinear model. The results also show that variation in the number of industrial segment of last period has the most effect on current Gini coefficient in low threshold and is reduced with moving toward up threshold. The results also show that variation in the number of industrial segment of last period has the most effect on current Gini coefficient in low threshold and is reduced with moving toward up threshold.