Showing 5 results for Seifi
Narges Salehnia, Mohamad Ali Falahi, Ahmad Seifi, Mohammad Hossein Mahdavi Adeli,
Volume 4, Issue 14 (12-2013)
Abstract
Developing models for accurate natural gas spot price forecasting is critical because these forecasts are useful in determining a range of regulatory decisions covering both supply and demand of natural gas or for market participants. A price forecasting modeler needs to use trial and error to build mathematical models (such as ANN) for different input combinations. This is very time consuming since the modeler needs to calibrate and test different model structures with all the likely input combinations. In addition, there is no guidance about how many data points should be used in the calibration and what accuracy the best model is able to achieve. In this study, the Gamma Test has been used for the first time as a mathematically nonparametric nonlinear smooth modeling tool to choose the best input combination before calibrating and testing models. Then, several nonlinear models have been developed efficiently with the aid of the Gamma test, including regression models Local Linear Regression (LLR), Dynamic Local Linear Regression (DLLR) and Artificial Neural Networks (ANN) models. We used daily, weekly and monthly spot prices in Henry Hub from Jan 7, 1997 to Mar 20, 2012 for modeling and forecasting. Comparing the results of regression models show that DLLR model yields higher correlation coefficient and lower MSError than LLR and will make steadily better predictions. The calibrated ANN models specify the shorter the period of forecasting, the more accurate results will be. Therefore, the forecasting model of daily spot prices with ANN can interpret a fine view. Moreover, the ANN models have superior performance compared with LLR and DLLR. Although ANN models present a close up view and a high accuracy of natural gas spot price trend forecasting in different timescales, its ability in forecasting price shocks of the market is not notable.
Narges Salehnia, Mohammad Ali Fallahi, Ahmad Seifi, Mohammad Hossein Mahdavi Adeli,
Volume 6, Issue 20 (7-2015)
Abstract
This paper aims at estimating Geometric Brownian Motion (GBM) Model, based on two central parameters in this model (volatility and drift), and forecasting Henry Hub natural gas daily spot prices (07/01/1997-20/03/2012). Researches reveal that two mentioned parameters estimation can be satisfied with different approaches and in various time scales. Therefore, two approaches of backward looking and forward looking have been used in different time scales and sub-periods. Results show that the volatility and drift values are highly dependent on the time scale and backward results are lower than the forward ones. Moreover, along with increasing the number of random runs of the model although the fluctuating range decreases, the predicted line slope is very close to the actual line. Ultimately, the performance evaluation criteria yields that forward method, clearly in 2009, has the best performance. The sub-periods of 2001-2004 in backward and forward methods have the next best performances, respectively. These sub-periods can be used as a basis for calculating the central parameters of the model. In addition, the results suggest that relying on data used in the most recent period is not sufficiently accurate. Also, it is observed that sub-periods or time scales with higher volatility show better performance evaluation criteria, therefore they can be applied in price forecasting with GBM model.
Azadeh Mehrabians, Parima Bahrami Zonooz, Roya Seifipour, Narciss Aminrashti,
Volume 12, Issue 45 (11-2021)
Abstract
Capital adequacy ratio is one of the most important indicators in analyzing the situation of banks in order to manage banks against risks such as bankruptcy and their inability to meet obligations. This controls the risk management of banks. The aim of this paper is to investigate the effect of banking variables on the capital adequacy ratio (CAR) in private banks in Iran during the period 2011-2018 and in Malaysia quarterly during the period 2012:01-2019:04 by Threshold Auto regression Method. The results showed that the CAR in the low regime with four lags had a negative effect and in the high regime had a direct effect on the CAR of Iranian banks. But it did not have a significant impact on the Malaysian banking system. The share of bank deposits in Iran in both regimes has a negative effect on the CAR. But it had a direct effect on the Malaysian banking system in the high regime. The size of the bank in the low regime had a direct effect on the CAR of private Iranian banks. But in Malaysia, in both regimes, it had a direct impact on the capital adequacy ratio. The share of credits in both regimes had a direct impact on the CAR in Iran. But in the Malaysian banking system in both regimes had a negative impact on the CAR. Liquidity in the low regime has a negative effect on the CAR in private Iranian banks. But in the high regime did not have a significant effect. While in the high regime, liquidity has a direct and significant effect on the CAR in the banking system of Malaysia. Returns of assets in the low regime do not have a significant effect on the CAR of Iranian banks. But returns of assets in the low regime have a direct and significant effect and in the high regime have a negative effect on the CAR in the Malaysian banking system. Financial leverage in the low regime does not have a significant effect on the CAR of Iranian banks, but in the Malaysian banking system in the low regime has a negative effect and in the high regime has a direct effect.
Nasrin Motedayen, Rafik Nazarian, Marjan Damankeshideh, Roya Seifi Pour,
Volume 12, Issue 45 (11-2021)
Abstract
Credit risk is the probability of default of the borrower or the counterparty of the bank in fulfilling its obligations, according to the agreed terms. In other words, uncertainty about receiving future investment income is called risk, which is of great importance in banks. The purpose of this article was to estimate the credit risk of Mellat Bank's legal customers. In this study, the statistical information of 7330 real customers was used. In this regard, the results of neural network model and support vector machine model have been compared. The obtained results have shown that the components considered in this study based on personality, financial and economic characteristics had significant effects on the probability of customer default and credit risk calculation. Also, the results of this study showed that the application of control policies at the beginning of the repayment period suggests facilities that have the highest probability of default with long life and high repayment. Comparing the results obtained from the prediction accuracy of different models, it was observed that the explanatory power of the support vector machine model and the use of the survival probability function was higher than that of the simple neural network model for the studied groups of real customers.
Mrs Roghayeh Soltani, Dr Roya Seifipour, Dr Mir Hossein Mousavi, Dr Saman Ziaee,
Volume 13, Issue 49 (12-2023)
Abstract
Applying a favorable tax system has important conditions such as justice and efficiency, therefore, consumption tax and income tax will comply with the principles of benefit and ability to pay. In this regard, value added tax is known as the most important innovation of the 20th century in terms of tax collection on consumption. Since increasing government revenue is one of the important goals of imposing this type of tax, the government has tried to determine the rate of this type of tax effectively and efficiently. Disproportionate increase in value added tax rates can have negative social effects on inflation, economic growth, income distribution, and general well-being in society. It may also have disruptive effects on other variables and sectors of Iran's economy. To manage the rate increase, one approach is to simulate and examine its consequences and effects on macroeconomic variables in the form of a multi-regional calculable general equilibrium model (MRCGE). Three different scenarios were applied and examined to simulate the shock effects of the increase in the value-added tax rate (12% , 15% , and 20 %) on four macro variables of Iran's economy: inflation, gross domestic product, consumption, and investment. The simulations were conducted at the country level using a multi-regional calculable general balance model, known as the ORANI-G Iran model, using the 2016 input-output table and regional accounts of the country. The results indicate that the effect of increasing the tax rate on value-added will increase inflation and investment and decrease GDP and consumption.
