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Showing 2 results for Time Series
Dr. Vahid Mahmoudi, Dr. Shapour Mohammadi, Dr. Hasti Chitsazan, Volume 1, Issue 1 (12-2010)
Abstract
The characterization of memory effects in crude oil markets is an interesting issue that has attracted the attention of researchers from different disciplines, from econophysics to more classical economics. The importance of the problem relies in the fact that the departure from uncorrelated behavior would imply the presence of not-random effects which, in principle, can be exploited for arbitrage.
This paper tries to contribute into the issue by estimating the memory effects by means of different parametric, semi-parametric, and non-parametric methods. In the other words, this paper provides analysis on the memory of the oil markets measured via the fractional integration parameter (d) by estimating it with various methods such as the MLE, NLS, GPH, Whittle, Lo, Hurst Exponent and Wavelet. To achieve this goal, we use the daily time series for WTI and Brent spot crude oil prices as well as 3-month futures, and further divide them into yearly subsections to obtain the historical series of memories.
Results of the whittle and wavelet estimations, which are better suitted for this analysis, show no evidence of a long memory process. However, the oil price time series exhibits a nonstationary mean-reverting behaviour.
Note that in this paper the behaviour of memory is our concern instead of the memory value itself. The results of memory changes trend shows that memory of international oil markets does not have an important trend change. In the other words, in our study period the efficiency of the market does not have an important decline or increase.
Mehran Amirmoeini, Teymour Mohammadi, Morteza Khorsandi, Volume 5, Issue 18 (3-2015)
Abstract
This paper tries to model the electricity demand in Iran’s industrial sector which captures economic factors and also non-economic exogenous factors. The structural time series model (STSM) approach is employed which allows using economic theory and time series flexibility. In this approach the role of UEDT (Underlying Energy Demand Trend) including technological improvement and structural changes is modeled, therefore the income and price elasticity are estimated more accurately. The results show that the UEDT has the stochastic nature. And UEDT has a great impact on industrial energy demand during 1975-2012. So, the electricity has not been used efficiently in this sector. In the short run the estimation of the income and price elasticity are 0.42 and 0.11 respectively. The value of the cross-price elasticity of electricity demand is estimated about 0.06. It shows that natural gas substitute electricity in industrial sector, however it is small.
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