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Showing 2 results for Behradmehr
Volume 20, Issue 4 (winter 2020 2020)
Abstract
This paper investigates the role of financial development factors on how to affect oil price on oil and gas rents in Iran. In order to construct a multidimensional financial development index, the principal component analysis and weighted average of nine financial development indicators are used. The oil price is derived from the estimates of spot prices. Data is collected seasonally for Iran during the period of 1970Q1-2016Q4. In order to evaluate the how to affect oil price on oil and gas rents, a simultaneous equations system, the SUR estimator, and rolling regression method are used in two stages. In the first step, the ARDL rolling method is used to estimate the effect of oil price on oil and gas rents. Then, the effect of multidimensional financial development index on the oil price is determined by simultaneous equations system of oil and gas rents. The findings indicate the positive effect of multidimensional financial development index on how to influence oil price on oil rent and gas rents. It means that increasing multidimensional financial development index strengthens the effectiveness of oil price on oil and gas rents in Iran.
Nafiseh Behradmehr, Mehdi Ahrari,
Volume 21, Issue 3 (7-2014)
Abstract
In general, energy prices, such as those of crude oil, are affected by deterministic events such as seasonal changes as well as non-deterministic events such as geopolitical events. It is the non-deterministic events which cause the prices to vary randomly and makes price prediction a difficult task. One could argue that these random changes act like noise which effects the deterministic variations in prices. In this paper, we employ the wavelet transform as a tool for smoothing and minimizing the noise presented in crude oil prices, and then investigate the effect of wavelet smoothing on oil price forecasting while using the GMDH neural network as the forecasting model. Furthermore, the Generalized Auto-Regressive Conditional Hetroscedasticity model is used for capturing time varying variance of crude oil price. In order to evaluate the proposed hybrid model, we employ crude oil spot price of New York and Los Angles markets. Results reveal that the prediction performance improves by more than 40% when the effect of noise is minimized and variance is captured by Auto-Regressive Conditional Hetroscedasticity model.
