سری های زمانی

Today, special attention has been paid to the capabilities of chaos theories and neural networks and the application of these two models in financial markets, especially petroleum products. In this study, daily values ​​of stocks of Iranian petroleum products during December 2007 to June 2016 have been studied. Due to the nonlinear nature of financial data, chaos theory is used to study the chaotic amount of time series. The chaos theory based on Liapanov's exponent and fractal dimension studies the time series caused by nonlinear dynamic systems. In chaos theory, first, using the Lyapanov diagrams and the Poincaré surface of section map and measuring the correlation dimension, the possibility of chaos in time series of daily value of oil products in Iran is investigated. Then, the Lyapunov diagram is plotted using the delay time estimation obtained from the method of the average of mutual information and embedding dimension using the algorithm of the false nearest neighbors. The Lyapunov map and the Poincaré surface of section indicate a chaos in the investigated time series. According to the provocation of chaos in this time series, its nonlinearity was deduced. Therefore, a suitable neural network was designed and the best model was selected to predict the future prices of petroleum products stock with a correlation coefficient of 0.99831 and the error of the training data is 0.0012 and the error of the test data is 0.002 that indicating good accuracy in modeling the price of these industries and can be used to predict its future price.

Recent theoretical econometric studies have focused on mixed frequency data. These studies are of great importance since they emphasize the role of information in economic modeling. In the current Time Series approach, temporal aggregation is often turned to a period of identical alternation; however, such aggregation leads to information loss in higher-frequency data. The mixed frequency studies provide a way to avoid the need for such temporal aggregation. In particular, the main result of this branch of econometric studies is to improve explanatory power, prediction, and efficiency in time series modeling with mixed frequency data. Accordingly, this paper attempts to specify the developments and shortcomings of this new branch of econometrics by reviewing the extant literature.

If domestic currency of a country can’t to perform its functions, foreign currency substitute domestic currency and This phenomenon is called currency substitution. Currency substitution is a common phenomenon in developing countries that it has different effects on the economy of the countries, so it is important to identify the determinants of currency substitution.In this study, is used the method of Kamin and Erickson (2003) to estimate the amount of foreign currency in circulation. Then using a macro approach the determinants of currency substitution investigate in Iran. To estimate the real money demand function of Iran used the method of Johansen-Joselius cointegration and then to investigate the determinants of currency substitution used ARDL method. The Period of variables in this study are from 1338-1392. The results showed that currency substitution in Iran impressed by difference between domestic and foreign interest rates, domestic inflation rate and informal exchange rate, while the real volume of imports, worldwide inflation rate and gross domestic production on the currency substitution are effectless and insignificant. The results show that currency substitution is a function of the parameters of the opportunity cost of money. In other words the main purpose of the substitution is the release of opportunity costs of holding money.

With regard to the importance of oil price changes in Iran’s oil revenues as an oil exporter and its effects on it's economy, the aim of this paper is to investigate the effects of the oil shocks on the changes of the most important index of the Stock Exchange in Iran called the total price index.
Investigating the price of shares in stock exchange and studying and controlling the investment market, which is an important part of financial markets in every country, play an important role in policy making and applying macro-economic policies. Controlling available savings and liquidity and conducting them towards different parts of national economics, this market leads to the investigation and transmission of investment to economic institutions and thereby providing future perspectives of economic firms. Given the fact during a short period of time, shock and the intensity of the movement of liquidity and capital of both the private sector and people of society in transfering from economic parts to non-economic ones are high, the recognition and introduction of information, structure and prediction of short term market for determining appropriate policies is vital.
In this work, having looked at monthly statistics data (2001-2012), the reseachers tried to investigate the factors affecting the value of shares in the stock market. It is done by using a non-nested optimization model test to select the suitable model and to examine the factors affecting the value of shares in the stock market. In addition, their relationship will be described and estimated based on the VAR method. On the other hand, since the prediction of financial and capital market is efficient and stable, we can provide a framework for achieving economic growth and development. Thus, this paper will examine the structural stability and forecast it's market based on Auto Regression Moving Average process and according to the results, the applicable policies will be suggested.
The primary duty of exchange stock is providing capital stock for the government, the private, and industry sectors in the form of collecting stagnant savings and the liquidity of private sector in order to finance the long-term investment projects.
Stock Exchange is the official and confident authority where holders of stagnant savings can relatively find reliable investment places and use their surplus funds to invest on companies or buy bonds to gain a guaranteed profit.
In other words, the Stock Exchange plays a dual function in the structure of a free economy: one the one hand, it helps to increase the capital of the government and the private sector, and on the other hand, it creates the secondary market to meeting the potential and actual investors. Investigation, controlling and conducting the market can lead to the financial resources of the capital market and investment for development and growth and the policies are planned on the basis of the goals of macroeconomic. Economists believe one of the reasons for the lack of the development of developing countries is the low level of investment. In this regard, the capital market is the main and important centre to attract these savings. Accordingly, we should identify its influential factors in the market, and thus use appropriate policies for the progression and growth of the market.
Achieving the optimal growth and development of economy without mobilization of financial resources in long term is impossible. In this regard, the position and the role of capital market are of high importance.
The capital market, which is the market of demand and supply of financial resources, can play a vital role when the process of supply and demand of its financial sources is the optimal allocations. The main prerequisite for the optimal allocations of resources in the capital market is the efficiency in their performance.
Thus, the difference between effective and efficient capital markets with inefficient markets is in the phenomenon of information and having access to them.
As the information related to the capital market (for a comprehensive, coherent and effective market activity) increases, the impact of the capital market on the growth and development of economy becomes more.
Conclusions and Suggestions: Thus, policy makers should pay attention to the Stock Exchange, its flourishing growth and to government policies, control the foreign exchange market, prevent the uncontrolled growth of liquidity, eliminate the cumbersome investment regulations and incentives tax, and finally draw a plan for implementation of economic policies.
In this regard, it is essential the country's economic managers identify macro-economic variables affecting the stock market, especially government-controlled variables and how their influence can lead to appropriate policies for the stimulatation of the market and growth of economy.

The effects of foreign exchange rates on economic variables in every countries show the importance of modeling and prediction of exchange rates.In this paper, singular spectrum analysis (SSA), which is a non-parametric technique for time series analysis, are used for modeling and predicting daily exchange rate US dollar when compares with Iranian Rials (USD/IRR) during June 2013 to Sep. 2015. ARIMA model is used as a benchmark to assess the performance of SSA. In order to find the best ARIMA model, R package auto.arima is used. In addition, errors in sample and out of samples for short, medium and long term forecasts are considered to compare the capabilities of models with together. Results indicate that SSA is able to be used for modeling exchange rate data.

With regarding importance of oil price changes in oil income variation and their impressions in Iran oil dependent economy، in this paper، we investigate the impacts of oil price shocks on the main stock exchange index (stock price index) in Iran as an oil exporting country. Our data cover monthly time series from 1991:4 to 2011:12. For investigating the relationship between oil price and stock price، in addition to oil price and stock price index variables، real exchange rate and liquidity has been added to our model. We applied three different yardsticks for illustrating oil shocks: linear shocks، scaled shocks and net shocks. Statistical analysis has been done in the time series framework consist of the GARCH and the Vector Autoregressive (VAR) models. According ouz results، oil shocks have positive effects on stock indices so that with increasing in oil price، stock indices increase. Also our findings show that monetary shocks are more powerful than oil shocks in affecting stock prices. Asymmetric effect tests couldn’t prove any asymmetric impression on stock price index.

In this paper، we have estimated the memory of theTehran stock exchange indices. The estimation of fractional differencing parameter is carried out by various methods such as MLE، NLS، Hurst Exponent، GPH، Lo، whittle and wavelet. The estimation results of whittle، wavelet، Hurst، and Lo methods allow us to conclude that the returns on stock indices (TEPIX، TEDPIX، TEDIX، Financial index and Industrial index) have long memory. The results obtained from GPH method show the existence of long memory in all indices of the Tehran stock market with the exception of TEDIX. Since estimations which obtained from ML and NLS methods are not significant in almost all intervals، we do not use them for studying the trend of the market’s memory. The results also show the memory of the Tehran stock exchange does not have an important trend. In the other words، in our study period the efficiency of the market does not show any significant changes

The characterization of memory effects in crude oil markets is an interesting issue that has attracted the attention of researchers from different disciplines, from econophysics to more classical economics. The importance of the problem relies on the fact that the departure from uncorrelated behavior would imply the presence of non-random effects which, in principle, can be exploited for arbitrage. This paper tries to contribute to the issue by estimating the memory effects by means of different parametric, semi-parametric, and non-parametric methods. In other words, this paper provides an analysis of the memory of the oil markets measured via the fractional integration parameter (d) by estimating it with various methods such as the MLE, NLS, GPH, Whittle, Lo, Hurst Exponent and Wavelet. To achieve this goal, we use the daily time series for WTI and Brent spot crude oil prices as well as 3-month futures, and further divide them into yearly subsections to obtain the historical series of memories.Results of the whittle and wavelet estimations, which are better suited for this analysis, show no evidence of a long memory process. However, the oil price time series exhibit a nonstationary mean-reverting behaviour. Note that in this paper the behaviour of memory is our concern instead of the memory value itself. The results of memory trend show that memory of international oil markets does not have an important trend change. In other words, in our study period the efficiency of the market does not have an important decline or increase.

Ability of Artificial Neural Networks (ANNs) as a powerful tool in simulation and prediction in science and engineering has made it attractive to economists. In this article, after a brief review of literature, a comparison of forecasting performance of ANN versus ARIMA is made. The data used are daily prices of oil for the period April 1983 to June 2005. In addition, sensitivity analysis is implemented for illustrating contribution of each input to the price changes in ANN models. The results show that the ANN model generates more accurate forecasts for the daily oil prices of oil than ARIMA model.

This article is a comparative study of estimation power of artificial neural networks and autoregressive time series models in inflation forecasting. Using 37 years Iran’s inflation data, neural networks performs better on average for short horizons than autoregressive models. This study shows usefulness of early stopping technique in learning stage of neural networks for estimating time series.

Business cycles analysis is one of the most important tasks for economists and statisticians. Various methods such as HP filter, BP filter, CF filter spectral analysis and Fourier transformation are used in time series analysis. All of these methods are sensitive to stationarity of time series. Also the traditional methods dont offer information in scale analysis. In this paper we introduce the Wavelet theory and its applications especially to economics. Also decomposition of seasonal GDP of Iran and analysis of business cycles are the other main propose. Results of GDP decomposition show 7 cycles with length of 16-32 quarters and 13 cycles with 8-16 quarters. Volatility analysis implies no change in variance of Wavelet coefficients in prewar and war periods. However volatility of GDP increased in the post war period.