Monetary policy in order to prevent losses arising from changes in exchange rates of disruptive are Always trying to find a suitable method to predict exchange rates. However, multi-dimensional characteristics of the converter makes it is complicated and nonlinear behav More
Monetary policy in order to prevent losses arising from changes in exchange rates of disruptive are Always trying to find a suitable method to predict exchange rates. However, multi-dimensional characteristics of the converter makes it is complicated and nonlinear behavior. One of the traditional methods of forecasting, time series analysis, which is based on two as sumptions static linearity. Some doubts about the performance of these traditional models have been created One of the alternative methods, artificial neural networks that In some cases are shown a good potential for time series prediction. In this Article , After reviewing the research conducted to clarify the predictive ability of mass moving average models and Artificial Neural Networks to compare The two methods for the prediction of the daily exchange rate has been made in the period from 01.01.1990 till 01.01.2012. The results showed that the neural network approach estimates the Autoregressive Integrated Moving Average (ARIMA) method provides better responses. In this study, MATLAB software and computational tools and data STATGRAPHICS economies of Australia, Canada, Japan and the United Kingdom, and the dollar exchange rate in those countries than in America is using.
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Due 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 More
Due 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.
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This article examines the forecast performance of ARFIMA and ARIMA models using data on daily stock price index of Tehran in period 25/11/2001 to 30/11/2011. To estimate the d parameter and other parameters, the NLS method in the software package Oxmetric / pcgive was u More
This article examines the forecast performance of ARFIMA and ARIMA models using data on daily stock price index of Tehran in period 25/11/2001 to 30/11/2011. To estimate the d parameter and other parameters, the NLS method in the software package Oxmetric / pcgive was used. After comparing the results of research models, ARFIMA models based on AIC, the model was found superior in modeling TEPIX. Also we use naive methods for estimating the prediction. Comparing the accuracy of the prediction models by criteria such as MAPFE and RMSFE and confidence intervals of the real values, we can deduce that the first Performance difference between the predicted long-term memory ARFIMA model is very minor compared to the ARIMA model And Secondly, inefficient ARFIMA model in Tehran capital market forecast is quite evident.
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Fluctuations in crude oil prices in addition to affect the economy of the exporting countries, is one of the sources of disruption in oil-dependent economy. Always predict the price and volatility has been of the challenges facing traders in oil markets and price foreca More
Fluctuations in crude oil prices in addition to affect the economy of the exporting countries, is one of the sources of disruption in oil-dependent economy. Always predict the price and volatility has been of the challenges facing traders in oil markets and price forecast is raised as an imperative and functional however, should be noted forecasts that will take place in more accurate and less error than the observed actual results. In order to predict the weekly price of Brent crude oil as an oil indicator given the difficulty of accurately identifying linear and nonlinear models in economic and financial time series from combining Autoregressive Integrated Moving Average models (ARIMA) by the assumption that the time series have a linear pattern and support vector machine (SVM) which has great potential in modeling nonlinear model is used to enhance the accuracy of prediction. Given two paired comparison performance criteria of root mean square error test (RMSE) and the mean absolute magnitude percentage error (MDAPE) which are resulting from the predicted values and actual values for each model, this indicates that in most cases the hybrid model provide smaller errors in predicting the future price of crude oil as compared to the individual applications of autoregressive integrated moving average models and the support vector machine.
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One of the most important issues of banking managers as an influential variable on the banking industry is the knowledge of the status of bank deposits that the bank depends on a large extent on it. Therefore, bank managers are keen to know how much the total bank depos More
One of the most important issues of banking managers as an influential variable on the banking industry is the knowledge of the status of bank deposits that the bank depends on a large extent on it. Therefore, bank managers are keen to know how much the total bank deposits will be at a given time in the future. Predicting the amount of deposits, changes and fluctuations of these deposits can help banks in planning and decision making. In this research, using statistical techniques and approach of artificial neural network models, we have tried to introduce a model with the highest estimation power and the least amount of error to predict the amount of deposits or the same sources of finance by their different types for the desired bank. To test the hypotheses, one private bank information was used during the period of 1387-1396. In this research, we compared the predictive power of ARIMA and artificial neural network method. To assess the accuracy of forecasting the bank's resources, the ARIMA method used Coopiff and Christopherson tests. The results of the research on the amount of bank deposits monthly showed that the neural network method provides better estimates than the ARIMA method.
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