Given the importance of oil prices, proper prediction of the OPEC Reference Basket can have an essential role in the immunization of economies in these countries against the effects of these fluctuations. This research is an effort to introduce an optimal model for modeling and predicting the fluctuations in OPEC crude oil prices. In this regard, we used data of daily oil prices between 2/1/1986 and 13/2/2017. According to this, the existence of long-term memory in the average equations and variance of crude oil prices were assessed and modeled and the result of the ARFIMA, confirms the existence of long-term memory in both the average equation and series variance. However, tests confirm non-linear and exponential behavior in crude oil prices. For this reason, results are specifically based on the information criteria and also MAPE and indicate the selection of a mixed model of partial augmented average movement and the model of conditional exponential Heteroscedasticity EGARCH (1,1) AFIRMA (4,0.09,3) as the best model for modeling and predicting the OPEC crude oil fluctuations in prices and lack of attention to exponential non-linear variance in the long term memory of crude oil prices can cause an error in the calculation of analysts and especially economic decision maker and deviation optimal policies.

Estimation of conditional variance has lots of application reflecting economic, especially financial economics, social economics and political economics’ risk and volatility research. Therefore, obtaining accurate estimation of the conditional variance is especially important. Recently Hansen has modeled the conditional variance and realized volatility simultaneously which is known as Realized GARCH model. In this paper, we introduce a fuzzy coefficient in the Realized GARCH, and then compare this model with GARCH, EGARCH and GJR-GARCH methods as well as the RGARCH model with 2 different criteria of the realized volatility concerning Tehran Stock Exchange Index. The log likelihood value used to evaluate in-sample fitting. According to this criterion, our proposed model has a better fit than the rest of the models. To evaluate the accuracy of prediction of conditional variance, the rolling window method used with two MSE and QLIKE loss functions. The results indicate that our model, the Realized GARCH with fuzzy coefficient has the best performance with both loss functions.

In recent years, investment in gold has been remarkable for investors because of a recession in stock exchange. This increase in demand of gold caused increase in gold price. Because of increase in gold price, dealing of gold expanded and so volatility of gold price return increased intensly. So we have to use a model to predict volatility beside return to make decision for investment.Find a model that it can do a better forecast of price return volatility is a debatable topic in the finance literature. Around this topic some models have been presented and these models have some advantages and disadvantages. These models have been applied for predict of volatility of crude oil and exchange rate more than other fields. Between all models, GARCH models have been more applicable than others. So we use this group of models too, but in a different way. This way is a nonparametric approach to GARCH model that presented by Buhlman and McNeil for first time in 2002. In this research we use this approach to forecast volatility of gold price return and compare it with other GARCH models by two loss function (QLIKE-MSE). The result of this research shows that nonparametric GARCH has a better performance than the other GARCH models based on QLIKE loss function with a statistical significance, but based on MSE loss function we can’t judge.

The present study, in the form of a state-dependent model and during the period 2001:6-2019:12, investigates which of the components of monetary aggregates has the greatest impact on real exchange rate volatility of Iran. In order to achieve this goal, the variables of money, quasi-money, liquidity and monetary base are considered as monetary aggregates and in order to avoid multicollinearity between monetary aggregates, four Markov Switching Exponential GARCH models with fixed transition probability are estimated. The findings of the study also indicate that in both low and high regimes of real exchange rate volatility, monetary aggregates have a positive and significant effect on real exchange rate volatility and the effect of monetary aggregates in low regime of real exchange rate volatility is different from the high regime, so monetary variables have an asymmetric effect on real exchange rate volatility. In addition, in both the low and high regimes of real exchange rate volatility, the monetary base had a greater effect on real exchange rate volatility than other components of monetary aggregates. Therefore, controlling the components of monetary aggregates, given the importance of each in the up and down state of real exchange rate regime, can be considered as a strategic point by economic policymakers.

Housing is one of the fundamental human needs. Accordingly to Maslov’s hierarchy of needs, housing as a physiological and basic need, set the stage for other needs for safety and survival, social relation, self-esteem and self-actualizution. Fulfilling a wide range of needs with housing, means that housing market can influence the behavior of individuals as well as the entire society. Given that, the housing market is linked to the entire economy of a country, it is therefore considered a very important market. On the other hand, various studies show that the exchange rate is a key variable that failure to pay attention to proper management can create problems for the economy of any country in various dimensions. So that Iran’s economy is no exception. Accordingly, the objective of this research is studing the effect of exchange rate volatility on house price in Iran from first quarter of 2006 to fourth quarter of 2019. The method of collecting research data is the library research method and to study the research topic, first the exchange rate volatility is estimated using the exponential GARCH Model and then the effect of exchange rate volatility on house price is investigated using the quantile regression method. The result indicate that, exchange rate volatility has a significant negative impact on house price and interaction effect of exchange rate volatility and inflation on house price is significant positive.

In this paper we compare a set of different standard GARCH models with a group of Markov Regime-Switching GARCH (MRS-GARCH) in terms of their ability to forecast the petroleum futures markets volatility at horizons that range from one day to one month. To take into account the excessive persistence usually found in GARCH models that implies too smooth and too high volatility forecasts, MRS-GARCH models, where the parameters are allowed to switch between a low and a high volatility regime, are analyzed. Both gaussian and fat-tailed conditional distributions for the residuals are assumed, and the degrees of freedom can also be state-dependent to capture possible time-varying kurtosis. The forecasting performances of the competing models are evaluated with statistical loss functions. Under statistical losses, we use both tests of equal predictive ability of the Diebold-Mariano-type and test of superior predictive ability, such as White’s Reality Check and Hansen’s SPA test. The empirical analysis demonstrates that MRS-GARCH models do really outperform all standard GARCH models in forecasting volatility at shorter horizons according to a broad set of statistical loss functions. At longer horizons standard asymmetric GARCH models fare the best. All this tests reject the presence of a better model than the MRS-GARCH-t in this research.

The main objective of this paper is to study the volatility trend in Tehran’s stock exchange from 1999:03 to 2008:02. This research are looking for answer to this question that, if the Tehran’s stock exchange in the study period had experienced volatility or not. To quantitative volatility, EGARCH Model is used. The results indicated. The Tehran’s stock exchange is experienced volatility trend between 1999 to 2008. So as, the most volatility level (instability) is occurred in 2003:01 and Then The most instability has experienced in 2007:02, but in comparision with volatility occurred at 2003:01 is less.

By employing conditional variance modeling and Granger causality test, we specified and compared the linkage between inflation and inflation uncertainty in Iran and three other OPEC members. To achieve this purpose we used consumer price index quarterly data for the years 1985-2009. The results suggest strong evidence that shows that in all these countries the increase in inflation leads to increase in inflation uncertainty. But in reverse, only in Iran and Venezuela, a causal relationship from inflation uncertainty to inflation can be found.

In previous studies, the normal mixture, as well as the Markov process, were used to model the financial return, separately. In this study, the normal mixture model is extended to the Markov mixture of normals. The mixture weights in every state are considered time-varying and as a function of past observations, so the limit of constant weight assumption is removed. The proposed model is estimated using Bayesian inference and a Gibbs sampling algorithm has been created to compute posterior density. The performance of algorithm is tested with simulation, then a two-state Markov time-varying Mixed Normal-GARCH model (MMN) with one and two components in every state, as well as limited cases (mean zero), were compared by comparison of their likelihood function. Finally, the model is applied to S& P500 and TEPIX daily return and results show that MMN models with two components provide better results than MMN model with one component which is so-called Markov switching GARCH model.