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Open Access Article
1 - Long memory investigation and application of wavelet decomposition to improve the performance of stock market volatility forecasting
شمس اله شیرین بخش اسماعیل نادری نادیا گندلی علیخانیBecause of very large frequency and volatility in Financial markets Indicators, acertain type of non stationary is created that it refers to the fraction non stationary. Thiscauses, provides Long memory in this type of time series. Hence, this study has inaddition to ex MoreBecause of very large frequency and volatility in Financial markets Indicators, acertain type of non stationary is created that it refers to the fraction non stationary. Thiscauses, provides Long memory in this type of time series. Hence, this study has inaddition to examine the existence of the long memory in both mean and varianceequations in the return series of Tehran stock exchange, Pays to forecasting the volatilityof this index. For this purpose, the daily data from fifth Farvardin 1388 to eighteenthOrdibehesht 1391 is used. Our results confirm the existence of Long Memory in bothmean and variance equations. However, among others, based on the information criteriaand MSE, ARFIMA (1,2)-FIGARCH(BBM) model has been selected as the bestspecification to model and forecast the volatility of Tehran stock exchange’s return. Aswell, in order to Forecasting the volatility of this series, was used Combination of theabove model with Level and decomposed data. The results show that, according to theforecasting error criteria (MSE and RMSE), the result of model’s based on decomposeddata (with wavelet technique), more acceptable. Manuscript profile -
Open Access Article
2 - Capability Comparison of the Models based on Long Memory and Dynamic Neural Network Models in Forecasting the Stock Return Index in Tehran Stock Exchange
اکبر کمیجانی اسماعیل نادریThe aim of this study is to introduce an efficient nonlinear model for predicting thereturn of Tehran Stock Exchange (TSE) Price index. For this purpose, the daily timeseries of price index from Farvardin 1388 to Aban 1390 is used. This study includes616 observations; 9 MoreThe aim of this study is to introduce an efficient nonlinear model for predicting thereturn of Tehran Stock Exchange (TSE) Price index. For this purpose, the daily timeseries of price index from Farvardin 1388 to Aban 1390 is used. This study includes616 observations; 90% of which used for estimating coefficients and the remaining 60observation are deduced for out of sample forecasting. By comparing the results of anonlinear dynamic artificial neural network (NNAR) and a nonlinear regression model(autoregressive fractional integration moving average «ARFIMA»), we found thatNNAR models have better performance in out of sample forecasting based on meansquare error criteria (MSE) and root mean square error criteria (RMSE) than thenonlinear regression models (ARFIMA). Manuscript profile -
Open Access Article
3 - Modeling and Forecasting Air Pollution of Tehran Application of Autoregressive Model with Long Memory Properties
reza akhbari Hamid AmadehBackground and Objective: Environmental pollution modeling is one of the essential requirements in the field of air quality monitoring which with using the output of the model, improvement of future situation can be possible. The existing literature of the modeling of e MoreBackground and Objective: Environmental pollution modeling is one of the essential requirements in the field of air quality monitoring which with using the output of the model, improvement of future situation can be possible. The existing literature of the modeling of environmental pollution –especially air pollutants- could be divided to two whole categories. First, those researches that in addition of pollutants data, they used some factors such as temperature, wind direction, wind speed and humidity. The second one –which this study belong to- with using time series regression models and by usage of the existing data about each pollutant, the future situation was forecasted. Method: In this study, we forecast future pollutants (CO,PM10,NO2,SO2,O3,PM2.5) status with ARIMA, ARFIMA and ARIMA-GARCH models with Box-Jenkins approach, then the best model is determined with MSE, RMSE, MAE and MAPE. Findings: Results indicate that the assumption of existence of long-memory is acceptable but the hypothesis that always ARFIMA models prepare the best forecast is rejected. Discussion and Conclusion: This study proves the application of econometric models to predict the pollutants state. Based on the high social costs of pollutant emissions, it is recommended that using these models, identify the pollutants affecting the future of the city and reduce the level of their dissemination of efficiency plans. Manuscript profile -
Open Access Article
4 - A comparative study between the effectiveness of ARIMA and ARFIMA models in predicting the interest rate and the treasury exchange rate in Iran
mohadeseh razaghi hashem nikomaram Alireza Heidarzadeh Hanzaei farhad ghaffari Mahdi Madanchi ZajDue to the importance of predicting economic variables, different models have been created to predict the future values of variables. In fact, economic models can be tested by checking the level of forecasting accuracy. The main purpose of this study is prediction of Ir MoreDue to the importance of predicting economic variables, different models have been created to predict the future values of variables. In fact, economic models can be tested by checking the level of forecasting accuracy. The main purpose of this study is prediction of Iran interbank offered rate and Iran treasury exchange rate as interest rates indicators for facilitating interest rate risk management. Two econometric models including ARFIMA and ARIMA have been used for forecasting. Thus, the ARFIMA model considering long-term memory and the ARIMA model without considering long-term memory have been considered. The evaluation of the prediction accuracy of the two models using the monthly Iran interbank offered rates data and also the monthly Iran treasury exchange rates data shows that both the interbank offered rates data and the Islamic treasury bond rates data, ARIMA model has a better performance compared to ARFIMA model in predicting data. Manuscript profile -
Open Access Article
5 - ARIMA and ARFIMA Prediction of Persian Gulf Gas-Oil F.O.B
H. Amadeh A. Amini F. EffatiGas-oil is one of the most important energy carriers and the changes in its prices could have significant effects in economic decisions. The price of this carrier should not be more than 90 percent of F.O.B price of Persian Gulf, legislated in subsidizes regulation law MoreGas-oil is one of the most important energy carriers and the changes in its prices could have significant effects in economic decisions. The price of this carrier should not be more than 90 percent of F.O.B price of Persian Gulf, legislated in subsidizes regulation law in Iran. Time series models have been used to forecast various phenomena in many fields. In this paper we fit time series models to forecast the weekly gas-oil prices using ARIMA and ARFIMA models and make predictions of each category. Data used in this paperstarted with the first week of the year 2009 until the first week of 2012 for fitting the model and the second week of 2012 until 13th week of 2012 for predicting the values, are extracted from the OPEC website. Our results indicate that the ARFIMA(0.0.-19,1) model appear to be the better model than ARIMA(1,1,0)and the error criterions RMSE, MSE and MAPE for the forecasted amounts is given after the predictions, respectively Manuscript profile -
Open Access Article
6 - New Criterion For Fractal Parameter In Financial Time Series
Mehrzad Alijani bahman banimahd Ahmad Yaghobnezhad -
Open Access Article
7 - Does Exchange Rate Non-Linear Movements Matter for Analyzing Investment Risk? Evidence from Investing in Iran’s Petrochemical Industry
Alireza Khosrowzadeh Aboutorab Alirezaei Reza Tehrani Gholamreza Hashemzadeh Khourasgani -
Open Access Article
8 - Comparative Approach to the Backward Elimination and for-ward Selection Methods in Modeling the Systematic Risk Based on the ARFIMA-FIGARCH Model
Nemat Rastgoo Hossein Panahian -
Open Access Article
9 - Applying Semi-parametric and Wavelets Methods to Study Persistent Rate of Inflation in Iran
Ahma Jafari Samimi Roozbeh BaloonejadIn this study, the existence of inflation persistent rate is examined in Iran. For this purpose, the degree of fractional integration is estimated by using GPH, Robinson adjustment, Reisen, Whittle, wavelets methods and consumer price index data of Central Bank during 1 MoreIn this study, the existence of inflation persistent rate is examined in Iran. For this purpose, the degree of fractional integration is estimated by using GPH, Robinson adjustment, Reisen, Whittle, wavelets methods and consumer price index data of Central Bank during 1972-2010. The results indicate a persistent rate of inflation in Iran. The stationary and persistence of inflation rate indicates that, by a shock in inflation rate, its effects remains for a long time. This may be considered by economic decision-makers to select appropriate policies. Manuscript profile -
Open Access Article
10 - پیشبینی قیمت بنزین فوب خلیجفارس با استفاده از مدلهای ARIMA و ARFIMA
حمید آماده فرشید عفتی باران امین امینی