Monte Carlo Simulation

Studying stock options is still a pristine area of research in the Iranian capital market. This is due to the lack of data as well as the complexity of valuation methodologies. In the present paper, using the Monte-Carlo simulation, we have estimated the value of stock options traded on Tehran Stock Exchange and examined whether the use of a control variate or antithetic variate augmented methods can lower the standard error of estimates. Furthermore, the estimated values of the three models under consideration, including crude Monte-Carlo, control variates augmented Monte-Carlo, and antithetic variates augmented Monte-Carlo are compared with each other and with options market prices. The results show that the standard error of the antithetic variate method is less than the crude method and control variate method. However, the control variate augmented Monte-Carlo model is more powerful than the crude Monte-Carlo and antithetic variate augmented Monte-Carlo method. Therefore, we can conclude that the control variate augmented Monte-Carlo model has a better performance in estimating the value of trading stock options and its estimated values are closer to the market prices.

In this paper, we study the identification problem of parameters of Dynamic Stochastic General Equilibrium Models with emphasis on structural constraints, in order to make the number of observable variables is equal to the number of exogenous variables. We derive a set of identifiability conditions and suggest a procedure for a thorough analysis of identification at each point in the parameters space. The procedure can be applied, before DSGE models are estimated, to determine where identification fails. We also use a Monte Carlo simulation and study the effect of restrictions on the estimate. The results show that the use of restrictions for estimation, when identification is reduced, leads us to inaccurate estimates and unreliable inference even when the number of observations is large.

INTRODUCTION: Iran has long been recognized as one of the most accident-prone areas in the world due to its special geographical location. Therefore, it is of utmost importance to devote special attention to crisis management in organizations and the identification of related strengths, weaknesses, opportunities, and threats. METHODS: The present study aimed to identify the strategic positioning of crisis management after natural disasters at the Ferdowsi University of Mashhad as the center of excellence in the east of the country. To this end, the internal and external factors which militate against university crisis management were identified. Thereafter, the strategic positioning of this university was determined among four positions, namely offensive, defensive, adaptive, and contingency, using a combination of SWOT (strengths, weaknesses, opportunities, and threats) analysis, analytic hierarchy process (AHP), and Monte Carlo simulation, and strategies were presented. The statistical population were university facility experts and managers among whom 10 cases were selected by purposive sampling. FINDINGS: Based on the obtained results, the highest weights in different sub-criteria were as follows: compliance with the required technical and safety standards in new constructions of the university campus (strength), the absence of any specific plan for dealing with various types of crises in different buildings (weakness), the proximity of the fire station to the university campus (opportunity), and indecisiveness of organizations supervising building retrofitting, including provincial government and Roads and Urban Development Office (threat). CONCLUSION: The results of the present study demonstrated that strategic positioning can be identified by analyzing internal and external factors. Moreover, among the four strategic positions, it was found that the Ferdowsi University of Mashhad adopted an offensive crisis management strategy in 45.4% of the cases.