Showing 5 results for Ameli
Dr Ahmad Ameli,
Volume 2, Issue 3 (3-2011)
Abstract
This article seeks to modeling social welfare functions, for assessment of how distribution of transfer payment among socio-economic levels. We consider providing social welfare functions two scenarios, first the each socio-economic levels receives amount of transfer payment equal to others, and second the each socio-economic levels receives that with weighted preferences. The four basic functions determine optimal value of how distribution, and then calculate actual value of that by transforming COICOP to ISIC . Finally the difference between optimal and actual values is determined for rural and urban society and for first and second scenario. At the first scenario the difference between optimal and actual value is smaller than second and this difference at rural society is greater than urban society. The other hand the welfare distribution at the former is worse than later.
Dr Ahmad Ameli, Dr Mehdi Sadeghi Shahdani ,
Volume 4, Issue 11 (3-2013)
Abstract
This paper presents an AHP and FLP model for the allocation of energy subsidies to different economic sectors. To do so, we defined a group of socio-economic criteria that may affected by the allocation of energy subsidies. These criteria are: economic growth, energy intensity, labor intensity, inflation, social cost of air pollutions and distribution of energy subsidy among socio-economic levels. According to calculated weights, we determined the priority of the above mentioned criteria. Also, according to the optimum overall rank of economic sectors, the commercial sector has the highest rank followed by industrial, agricultural and household and transportation sectors.
After determining the final coefficients of AHP approach, we determined the allocation of energy subsidies using linier programming approach. We also considerd the change in technology and consumption patterns of household and transportation sectors. Results show that the share of energy subsidies allocated to commercial and transportation sectors should increase to 30.4 and 28.6 percent respectively.
Malihe Ramazani, Ahmad Ameli,
Volume 6, Issue 22 (12-2015)
Abstract
In capital markets, stock price forecasting is affected by variety of factors such as political and economic condition and behavior of investors. Determining all effective factors and level of their effectiveness on stock market is very challenging even with technical and knowledge-based analysis by experts. Hence, investors have encountered challenge, doubt and fault in order to invest with minimum risk. In order to reduce cost and raise the profit of investment, determining effective factors and suitable time for sailing and purchase is one of the important problems that every shareholder or investor in stock market should consider. To reach this goal, a variety of approaches have been introduced, which are often intelligent, statistical, and hybrid. These approaches are mostly used to predict the stock price time series. Our proposed algorithm is hybrid and involves two stages: preprocessing and predictor. The preprocessing stage involves three steps: missing value, normalization and feature selection. Since there are many features in used datasets, genetic algorithm (GA) is used as the feature selection algorithm. In order to intelligent capability of Fuzzy Neural Network (FNN), this network with two structures (Mamdani and Sugeno) is used as a stock price prediction in second stage. This network is capable of extracting fuzzy rules automatically. Back propagation algorithm (gradient decent) is used for adapting all the parameters.
Our algorithm is evaluated on ten datasets with seven features obtained from ten different companies. By comparing the simulation results of the simple and hybrid FNN network, we found that the lack of suitable feature selection algorithm will lead to high computational cost, and in many instances the hybrid algorithm outperforms the simple FNN. This results demonstrate the superiority of the hybrid FNN to the simple one. In general, since the number of Sugeno tuning parameters are more than Mamdani, its performance is better than mamdani. Moreover, our algorithm is comparable to the maximum precision rates of other approaches.
Marzieh Khakestari, Sahar Joleini, Ahmad Ameli,
Volume 9, Issue 31 (3-2018)
Abstract
This paper implements an approach to examine economic problems in which rational agents interact in dynamic markets. We use evolutionary game theory and agent-based modeling in tandem as a means to address intertemporal problems that display evolutionary attributes. This study examines the behavior of the Organization of Petroleum Exporting Countries (OPEC) in the global oil markets during the 1960s and 1970s, which sought to control global oil markets during this period.. To address this, a symmetric evolutionary game theory model is used to examine the behavior of OPEC agents as they learned to take control of their resources. An agent-based modeling approach employs computational power to implement the evolutionary game and provide detailed results. It is shown that OPEC’s behavior over the period is dependent on the growth of petroleum reserves within the member nations. Increasing realizations of natural resource reserves spur increased rates of learning and experimentation, and this enables the cartel to act cooperatively and capture control of global petroleum markets. If reserves are kept constant, OPEC lingers at a state in which the cartel does not come to dominate world oil markets.
Seyed Ali Naseri, Farkhondeh Jabal Ameli, Sajad Barkhordary Dorbash,
Volume 11, Issue 41 (10-2020)
Abstract
Systemic risk arises from simultaneous movement or correlations between market segments; Thus, systemic risk occurs when there is a high correlation between the risks and crises of different market segments or institutions operating in the economy, or when the risks of different segments in a market segment or a country are related to other segments and other countries. This paper presents a measure of systemic risk calculation to effectively describe the systemic importance of each financial institution in a system. The DCC-GARCH methodology with normal and t-student distributions has been used to examine the correlation of time-varying banks. The results of this section show that the application of DCC-GARCH-student-t model is preferable to DCC-GARCH-normal model. In order to investigate the presence of leverage effect, GJR-GARCH model was used and the results of estimation showed the presence of asymmetry and the absence of leverage effect in the data. In the study of dynamic conditional correlation between selected banks, it is also observed that α_C ,β_C are not significant for both estimation cases. Therefore, in both cases, it is estimated based on the normal distribution and t-student α_C=β_C=0 and the conditional correlation becomes constant. Based on the results of shapley value and in order to allocate the total risk between the banks in the sample, Parsian, Mellat, EN, Tejarat and Saderat banks have the most systemic importance for the period of June 17, 2009 to May 7, 2019.
