جستجوی مقالات مرتبط با کلیدواژه "نااطمینانی" در نشریات گروه "عمران"

The Railway transportation industry is one of the major indicators of the development of countries. On the other hand, the railway transportation sector has many risks and uncertainties that can be considered as a negative factor from the perspective of investors. The purpose of this study is to identify and manage the risk of the railway transportation sector, which has been used as an example of the listed Stock exchange companies of railway transport. The risks of the railway transportation sector, especially the systematic risk of rail stock companies, are estimated with the beta coefficient, and then the ranking of rail stocks will be done according to the systematic risk. Related stocks are priced according to systematic risk with the capital assets pricing model. After identifying the risks of this sector, for risk management, option valuation will be used using the binomial model. Finally, the sensitivity of the option price of railway shares is checked using Greek parameters. Due to the large volume of data and their non-normality, in order to predict the future price and the exercise price of stocks, the Monte Carlo simulation method is used for option pricing. According to the outputs of the model and the examination of the movement path and price changes of rail stocks and option prices with the help of the binomial model in Python and DERIVAGEM software, as well as the estimation of the option price sensitivity by Greek parameters, relevant analyzes are expressed for different rail stocks.

According the importance of the transport sector in the economic structure and its crucial role in process of trade and other economic variables, identifying the affecting variables on the activities of these sectors have a high necessity. Therefore, the main purpose of this study is to investigate the effect of exchange rate uncertainty on the value added of the transportation sector from the reducing trade channel. For this purpose, the data for Iran economic in period of 1991-2017 are used and the Bayesian Vector Auto-Regressive (BVAR) model applied to estimates the coefficients. In this study, the Generalized Auto-Regressive Conditional Heteroskedasticity (GARCH) model is used for calculation of uncertainty in the exchange rate. Results indicate on the negative and statistically significant impacts of exchange rate uncertainty on the transport value added. Results also show that GDP and oil revenues have the significant positive impact on the transport value added, but transport price level didn’t have a significant impact.