Journal of Economic Modeling Research

This paper aims at estimating Geometric Brownian Motion (GBM) Model, based on two central parameters in this model (volatility and drift), and forecasting Henry Hub natural gas daily spot prices (07/01/1997-20/03/2012). Researches reveal that two mentioned parameters estimation can be satisfied with different approaches and in various time scales. Therefore, two approaches of backward looking and forward looking have been used in different time scales and sub-periods. Results show that the volatility and drift values are highly dependent on the time scale and backward results are lower than the forward ones. Moreover, along with increasing the number of random runs of the model although the fluctuating range decreases, the predicted line slope is very close to the actual line. Ultimately, the performance evaluation criteria yields that forward method, clearly in 2009, has the best performance. The sub-periods of 2001-2004 in backward and forward methods have the next best performances, respectively. These sub-periods can be used as a basis for calculating the central parameters of the model. In addition, the results suggest that relying on data used in the most recent period is not sufficiently accurate. Also, it is observed that sub-periods or time scales with higher volatility show better performance evaluation criteria, therefore they can be applied in price forecasting with GBM model.

Given the importance and role of capital markets in the economy, its characteristics have been regarded by researchers in this field. Hence, the main purpose of the present study is testing the existence of multiple price bubbles in Tehran stock market. For this purpose, the monthly data on the total price index and price-dividend ratio for periods 2000 – 2013 has been used. In this study generalized supremum Augmented Dickey – Fuller test, which has been recently introduced, is used due to critical review of conventional methods of testing the bubbles and also the possibility of a multiple bubble in time series. In addition to the testing of multiple Bubbles, with using this method there is the possibility of determining their period of creation and decay. The results showed that in the period under review, in the period 2003:3 - 2003:5 and 2004:12 - 2005:7 hypotheses price bubble in the stock market is confirmed.

Structural parameters are necessary and important in some economic studies, especially in general equilibrium models. One of these structural parameters is degree of price rigidity. In this article we try estimate degree of price rigidity in Iran economy in a General Equilibrium Dynamic Stochastic Model with Bayesian method. Our result with using seasonal data of real consumption, GDP, inflation and taxes for 1377-1387 reach us to number 46 percent for price rigidity which show 46 percent of Iran's firms could not optimize their price in each period.

In capital markets, stock price forecasting is affected by variety of factors such as political and economic condition and behavior of investors. Determining all effective factors and level of their effectiveness on stock market is very challenging even with technical and knowledge-based analysis by experts. Hence, investors have encountered challenge, doubt and fault in order to invest with minimum risk. In order to reduce cost and raise the profit of investment, determining effective factors and suitable time for sailing and purchase is one of the important problems that every shareholder or investor in stock market should consider. To reach this goal, a variety of approaches have been introduced, which are often intelligent, statistical, and hybrid. These approaches are mostly used to predict the stock price time series. Our proposed algorithm is hybrid and involves two stages: preprocessing and predictor. The preprocessing stage involves three steps: missing value, normalization and feature selection. Since there are many features in used datasets, genetic algorithm (GA) is used as the feature selection algorithm. In order to intelligent capability of Fuzzy Neural Network (FNN), this network with two structures (Mamdani and Sugeno) is used as a stock price prediction in second stage. This network is capable of extracting fuzzy rules automatically. Back propagation algorithm (gradient decent) is used for adapting all the parameters. 
Our algorithm is evaluated on ten datasets with seven features obtained from ten different companies. By comparing the simulation results of the simple and hybrid FNN network, we found that the lack of suitable feature selection algorithm will lead to high computational cost, and in many instances the hybrid algorithm outperforms the simple FNN. This results demonstrate the superiority of the hybrid FNN to the simple one. In general, since the number of Sugeno tuning parameters are more than Mamdani, its performance is better than mamdani. Moreover, our algorithm is comparable to the maximum precision rates of other approaches.

The present article studies the interactive relationships between oil price volatility and industries stocks of basic metals, petroleum and chemical products by using Vector Auto Regressive (VAR) and Multivariate Generalized Autoregressive Conditional Heteroskedastisity (GARCH) models from March 2004 to March 2015 empirically . In this research, the VAR-GARCH model is proposed, which is developed by Ling and McAleer (2003). The model survives the return and volatility problems among the considered series and this is the VAR-GARCH advantage. The results show that there are Average effects between oil market and stocks market of basic metals and petroleum products, But this effects are not true for chemical industry market.  The volatility effects between world oil price and chemical and basic metals industry markets  is not existed, but between oil market volatility and petroleum products stock volatility, Significant negative relationship is existed. There for, the investors should reduce their portfolios basket dependences on oil price as much as possible.

The bubble of Asset Price is the deviation of the asset price from its fundamental value. Since the many of the financial crisis arise from bursting bubble of financial assets, the explore of bubble behaviors in these markets and the early detection for the prevention of adverse economic consequences is important. Considering the criticisms of conventional tests for detecting price bubbles and also the importance of the subject, in this study, we have considered the new methods proposed by Phillips, et al. (2011, 2012) based on Right-Tailed Augmented Dickey-Fuller (RTADF) tests. In this regard, in order to testing explosive behavior and multiple bubbles and determining bubble periods in Iranian informal exchange market, we have applied the tests of SADF and GSADF according to monthly data for the nominal exchange rate from 2002:04 to 2016:03. Since the explosive behavior in nominal exchange rate might be driven by the its fundamentals, to comment on the existence of rational bubbles in the exchange market, we have evaluated the ratio of the nominal exchange rate to the relative prices of tradable and non-tradable goods. Based on the obtained results, the Iranian foreign exchange market has been experienced explosive behavior and multiple bubbles in the period of under study. Moreover, the relative prices of traded goods explain some explosiveness in the Iranian exchange market. Our findings suggest that the explosive behavior in nominal exchange rate from 2008:10-2008:12, 2012:01-2012:03 and 2013:09-2013:11 was because of rational bubbles in exchange rate and in other periods was driven by the relative price of tradable goods. Therefore, it is suggested to control the sharp exchange rate movements, in addition to bubbles, fluctuations in prices of traded goods market require more attention. Also, due to the possibility of bubbles repetition, the GSADF test is the better test to detect bubbles.

Expectation has an important role in oil price fluctuation and it seems which one of the important factors is for changing supply behaviour however oil price changes. Identification of mentioned expectation could help us for partly and continuously control the oil market situation.one of the important factor that could have effects on future oil price expectation is volume of current reserve oil and specifically OPEC members reserves. For OPEC members not only high reserve oil is prestige but also give them chance for having more OPEC production share however after applying market sharing system based on production for OPEC members in early 1980s,volume of reserve oil considered as a main benchmark and after that started increase reserve oil competition among OPEC members. In this paper tried study transition s of Iran’s oil reserves and its effectiveness on the oil producer’s countries’ information and also its accuracy. According to some statement reserve oil extra announcement could create chaos in oil market. Based on this study there is no any relation between increasing of oil reserves and oil production changes in Iran as one of the OPEC member's country and it seems extra reserve oil announcement more than reality is a hidden competition among members for getting more credit.