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The Poverty measurement and its determinants are the most important factors in poverty alleviation programs. In this paper, we estimate poverty line and its dimensions by using Linear Expenditure System. To that end, we use household level data from the Statistics Center of Iran during the period of 1994-2008. We examine the main determinants of poverty for urban households using a probit model. In this model, we assess the impact of key household characteristics on poverty. The results show that the poverty likelihood decreases if the educational level of the head of the household increases. In addition, the chance of being poor declines in a household headed by a male. The age of the head of the household, the ratio of income earners in the household, and the household size are other factors that have a significant role in the probability of being poor.

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The main goal of this research is to calculate VaR index with parametric Markov-Switching GARCH approach for accepted companies in Tehran Stock Exchange and also selecting the optimal portfolio of their stocks. To calculate the index, data and information of weekly stock price of 10 representative firms during the period 2008-2014 has been used which account for 332 working weeks.
The results from estimation of VaR and determination of optimal stock portfolio in the non-linear programming framework showed that optimal portfolio of food-industry companies stock, in the context of VaR has higher returns and risk in the first regime (Boom period) compared to the second regime ( recession period). On the other hand, it has had lower weight in both stock portfolios that had lower average returns compared to the rest of the stocks and compared to the stocks which had lower VaR relative to other stocks that has higher weights.
The Kupiec and Lopez back testing using 10 future week data, showed that both of approaches is valid but the parametric approach has better rank. Therefore the optimal portfolios of stocks under parametric VaR will be accepted as final optimal portfolio.

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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.