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Showing 3 results for Naderi
Chaos Analysis, Wavelet Decomposition and the Performance of Neural Network Models in Forecasting Tehran Stock Exchange Index
Esmaeil Naderi, Dr Hossein Abbasi-Nejad,
Volume 3, Issue 8 (6-2012)
Abstract

This study investigates predictability, chaos analysis, wavelet decomposition and the performance of neural network models in forecasting the return series of the Tehran Stock Exchange Index (TEDPIX). For this purpose, the daily data from April 24, 2009 to May 3, 2012 is used. Results show that TEDPIX series is chaotic and predictable with nonlinear effect. Also, according to obtained inverse of the largest lyapunov exponent, we are able to predict the future values of the series up to 31 days. Besides, our findings suggest that multi-layer feed forward neural network model and fuzzy model based on decomposed data, are of superior performances in predicting the return series. It is worth mentioning that, among these models, MFNN reveals the best performance.


Ramsey Pricing Method for Natural Gas in Household Sector and Fuzzy Regression Estimation Method
Rasoul Naderi, Mohammad Hossein Pourkazemi, Saeed Farahanifard,
Volume 5, Issue 18 (12-2014)
Abstract
Public pricing of products is one of the most important economicalissues, since any changes in the pricing, affects both the welfare ofconsumers and quantity of goods and Services which are produced.
In this paper which is done for natural gas pricing  in Iran, the purpose is giving a price that the government can consider it as a suitable choice for using in subsidies targeting project. These prices have two advantages: first, they try to maximum the social economical welfare (summation of producer and consumer surplus) second, this method solve the problem that the producer has in covering their costs (by marginal cost pricing) because of increasing returns to scale.
This paper deals with the optimal gas pricing in household sector in Iran by the Ramsey method of pricing.
In this regard we have used fuzzy regression (because of its accuracy and devoid of classic regression restrictions) and the data from 1977 to 2011 for estimating production function and returns to scale in natural gas production side. Also for estimating demand function and elasticity we have used ARDL method and data from 1350 to 1389. The results shows that the current prices aren’t optimum and despite implementation of subsidies targeting project the prices are low.
Time-Varying Transmission Mechanism of Oil Shocks in the Global Crude oil Market: A TVP-VAR Approach
Naser Khiabani, Mr. Mohammad Amin Naderian,
Volume 9, Issue 32 (7-2018)
Abstract
In this paper, we have utilized a time-varying parameter vector autoregressive model in order to examine the structural changes in the transmission mechanisms of oil price shocks in the global crude oil market over the period of 1985-2016. In this setting, the contemporaneous response of real oil price and crude oil production to flow oil supply shock, flow oil demand shock, and speculative demand shocks are explored. Results obtained from using Monte Carlo Markov Chain estimation method along with the identification approach proposed by Killian and Murphy (2014) reveal that the impact responses of oil production to the structural shocks follows a decreasing trend throughout the past three decades mainly due to the erosion of global oil production spare capacity. The reaction of oil production to flow oil supply shock is also estimated to be greater than other demand shocks over all dates. Moreover, the contemporaneous impact of structural shocks on real oil prices fail to show a clear pattern, however, jumps experienced in periods where uncertainty heightened and risk aversion strengthened is distinct. The reaction of real crude oil price to flow oil supply shock was more pronounced in 1990s and the period subsequent to oil price plunge in 2014. By contrast, the role of flow oil demand shock in real crude oil price fluctuations was dominant over the period of 2000-2014. While the oil production reacted more strongly to speculative demand shock rather than flow oil demand shocks, the response of real oil price to these two oil demand shocks is completely reversed.

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