Saeed Rasekhi; Mahdi Shahrazi
Volume 18, Issue 57 , February 2014, , Pages 1-26
Abstract
Based on efficient market hypothesis, financial markets are impossible to forecast. The purpose of this paper is to examine the weak-form efficiency of the Iranian foreign exchange rate (defined by the Rial/Dollar) during time period 1999:25:01 to 2010:17:06 from long memory viewpoint. For this, we have ...
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Based on efficient market hypothesis, financial markets are impossible to forecast. The purpose of this paper is to examine the weak-form efficiency of the Iranian foreign exchange rate (defined by the Rial/Dollar) during time period 1999:25:01 to 2010:17:06 from long memory viewpoint. For this, we have employed three methods of scaling analysis including classical rescaled range (R/S) analysis, modified rescaled range (M-R/S) analysis and detrended fluctuation analysis (DFA). We have divided the time period into two sub-periods, 1999:25:01-2002:21:03 and 2002:21:03-2010:17:06. In the former time period, Iran had a fixed exchange rate regime and in the latter period, the country followed a managed floating exchange rate regime. The obtained results from these methods are not the same. To achieve more explicit conclusions, we’ve used two more widely applied econometric tests namely augmented Dickey-Fuller (ADF) test and Phillips-Perron (PP) test to determine whether or not the time series under consideration behave as random walk consistent with the weak-form efficiency. The findings indicate that the result of DFA is in line with the econometric approach. We conclude that the Iranian foreign currency market at the first sub-period is less efficient relative to the second sub-period. Another important result is that relying on only one method to make a conclusion about market efficiency may be very misleading. Therefore, one should first carefully select more reliable methods and then compare their results to achieve a reliable conclusion.
Hassan Heydari
Volume 16, Issue 46 , April 2011, , Pages 77-96
Abstract
This paper focuses on the development of modern non-structural dynamic multivariate time series models and evaluating performance of various alternative specifications of these models for forecasting Iranian inflation. The Quasi-Bayesian method, with Literman prior, is applied to Vector autoregressive ...
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This paper focuses on the development of modern non-structural dynamic multivariate time series models and evaluating performance of various alternative specifications of these models for forecasting Iranian inflation. The Quasi-Bayesian method, with Literman prior, is applied to Vector autoregressive (VAR) model of the Iranian economy from 1981:Q2 to 2006:Q1 to assess the forecasting performance of different models over different forecasting horizons. The Bewley transformation is also employed for the re-parameterization of the VAR models to impose the mean of the change of inflation to zero. Applying the Bewley (1979) transformation to force the drift parameter of change of inflation to zero in the VAR model improves forecast accuracy in comparison to the traditional BVAR.[1]
[1]. Acknowledgement
I would like to thank Paolo Girodani for comments and providing some GAUSS procedures, Ronald Bewley, David Forrester, Jan Libich, and two anonymous referees for their helpful comments and suggestions on an earlier version of this paper. Financial support from the Urmia University is gratefully acknowledged. The usual disclaimer applies.
Hamid Abrishami; Ali Moeini; Mohsen Mehrara; Mehdi Ahrari; Fatemeh Soleymani Kia
Volume 12, Issue 36 , October 2008, , Pages 58-37
Abstract
In this paper, we use GMDH neural network based on Genetic Algorithm to model and forecast the price of gasoline using two approaches; Deductive Method and Technical Analysis. The results of deductive method indicate that the accuracy of prediction could reach up to 96% and in technical analysis could ...
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In this paper, we use GMDH neural network based on Genetic Algorithm to model and forecast the price of gasoline using two approaches; Deductive Method and Technical Analysis. The results of deductive method indicate that the accuracy of prediction could reach up to 96% and in technical analysis could reach up to 99%. Furthermore the comparison reveals that the GMDH neural networks model consistently outperforms the regression model used in this study.
Saeed Moshiri
Volume 4, Issue 12 , October 2002, , Pages 29-68
Abstract
Chaos theory is rather new in science, but, it is, in fact, rooted in ancients' perception of the world. The main idea is that although a complex system, such as world, seems to be generated by a random, and therefore, unpredictable process, it may run by a nonlinear deterministic process. Chaos theory ...
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Chaos theory is rather new in science, but, it is, in fact, rooted in ancients' perception of the world. The main idea is that although a complex system, such as world, seems to be generated by a random, and therefore, unpredictable process, it may run by a nonlinear deterministic process. Chaos theory has been applied to some Economic time series to see if they have an order, and, therefore, predictable. Some Economic time series, such as stock prices, look random, but, according to the chaos theory, they may come from a nonlinear deterministic process. If the data generating process is nonlinear, using traditional linear methods in estimation and forecasting can be misleading. Chaos theory is also applied to macroeconomic models. Some macroeconomic concepts, such as endogenous business cycles, can now be explained by the theory. In this paper, I try to review the chaos theory and its mathematical root for economists. Then, I will survey the Economic applications of the theory, and finally, will analyze different methods introduced for testing for chaos.
Mohammad Reza Asgari Oskoei
Volume 4, Issue 12 , October 2002, , Pages 69-96
Abstract
Application of non-classical methods in modeling complex systems and forecasting their behavior has become as more as usual for the scientists and professionals. In most complex systems, especially in non-linear systems, application of classical methods is very difficult or even useless. Non-classical ...
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Application of non-classical methods in modeling complex systems and forecasting their behavior has become as more as usual for the scientists and professionals. In most complex systems, especially in non-linear systems, application of classical methods is very difficult or even useless. Non-classical methods are intelligent, knowledge-based, very flexible, and therefore effective in modeling and forecasting. Neural Networks are one of the well-known and innovative nonclassical methods, which have being used in modeling, pattern recognition, clustering and forecasting. This paper tries to predict the economic time series by neural nets. Economic time series are considered as outputs of complex and non-linear economic systems, which can be modeled and forecasted by the neural nets.It has been shown that the performance of neural nets (as prediction machine) is very sensitive and dependent on the structure, size, and learning method of neural nets. In this paper, using MATLAB neural nets toolbox, some Iranian economic time series are being used as case studies for the neural network application.
Mohammad Reza Ghadimi; Saeed Moshiri
Volume 4, Issue 12 , October 2002, , Pages 97-125
Abstract
Artificial neural networks(ANN) are flexible models used for data analyzing and Modeling non-linear relations.Most economic applications of the ANN models have been in financial markets. Only recently there have been same macroeconomic applications of ANN models. In this paper, we set up an ANN model ...
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Artificial neural networks(ANN) are flexible models used for data analyzing and Modeling non-linear relations.Most economic applications of the ANN models have been in financial markets. Only recently there have been same macroeconomic applications of ANN models. In this paper, we set up an ANN model to forecast Iranian Economic growth using a long data from 1315 to 1375. We used different inputs based on Economic growth models and time series forecasting models in the ANN model. The forecasting results are then compared with those of the economic and time series models. The results show that in most of the cases, the ANN model outperform other traditional forecasting models.
