FINANCIAL ENGINEERING AND SECURITIES MANAGEMENT (PORTFOLIO MANAGEMENT)
Year:2013 | Volume:4 | Issue:14|Papers Count:8

The purpose of this study is estimating and evaluating the value at risk in forex market. For this purpose based on logritme of Euro/Dollar ratio the value at risk were estimated to three methods: Parametric, Historical and Montecarlo, Simulization from 12/10/2004 to 12/3/2009 in time periods 3, 6, 9, 12, 36 months and in confidence level 90, 95, 99 percent. The results of multi-variable tests demonstrated that there is no significant difference in value at risk average based on three methods and the confidence level and multi-time periods on two currency Euro and dollar. The results of back tests demonstrated that validity of calculations for the minimum value at risk is not confirmed but for the maximum value at risk is confirmed.

The Purpose of investment is efficiency gain but it is need to risk tolerance such are the nature of trade and investment activities. Risk can be controlled and minimized to appropriate risk management techniques but cannot be deletion. Nowadays Value at risk (VaR) is the new science of risk management and in recently year criteria of market risk equivalent to Value at risk. VAR is the statistical scale which calculates the maximum expected disadvantage for keeping an asset, for a specified period of time and with a certain probability.In this research is examined the possibility of using value at risk, In order to anticipate and management of investment risk in metallic minerals industry. The VaR is estimated by parametric methods and Index of metallic mineral industry. For this purpose, logarithmic return on the industry index that is considered as basic historical observations is calculated daily for a period of time from 1386 Until Shahrivar of 1391. Forecasting of return volatility is calculated by Simple moving average and exponential moving weighted average method with JP Morgan index (0.94) and smoothing coefficient (0.97) in 95% and 99% confidence level. For examining of accuracy of forecasts by VAR are used Kupiec test failures.The results show the method of weighted exponential moving average with smoothing coefficient (0.97) is better predictor for both of confidence levels. Finally, it can be concluded that market risk of metallic minerals industry is Predictable and Manageable by VAR.

Financial bubbles are one of the main problems that modern economics is dealing with today. Due to straight relationship between bubbles and financial crises, researchers have been ever looking for methods to understanding, identifying, forecasting bubbles as well as evaluating the losses and magnitude of the subsequent crashes. One of the methods that will detecting the bubbles is the log-periodic power law (LPPL) model. That consider the faster-than-exponential (power law with finite-time singularity) increase in asset prices decorated by accelerating oscillations as the main diagnostic of bubbles. The LPPL model in detecting bubbles and crashes has been performed successfully in various kinds of markets. But in this research, we performed it for first time in Iran. In this research, we use the LPPL model to detecting bubble and predicting subsequent crash of Tehran Exchange Dividend Price Index (TEDPIX) in period of 2006-2008. Consequently, in order to certainty of existence of log-periodicity in data, we performed Lomb spectral analysis on data. The results indicated the model has fitted the data well and Lomb spectral analysis well has confirmed the existence of log-periodicity. This way which can be concluded the data have behavior according to the LPPL model. The model in this period has identified a bubble and also has provided reasonable critical time prediction of the bubble.

The process of portfolio selection is a subject that many researchers focus on it. Different criteria that affect this process change during the time and this situation make it important use suitable investment decisions support tool. In this paper, by using a multi-stage mathematical model, a new method is provided for solving stock selection problem according to fundamental analysis approach. First, we determine the priority of factors affecting industries selection by using analytical hierarchy process and then by solving a linear programming model, the weights of each industry is determined according to the common constraints and output of analytical hierarchy process approach. Finally, by using goal programming model, the weights of stock in each industry is determined. In this paper, in order to validating the presented model, we test it by real data. The results show that the presented model can lead to select a portfolio with higher return.

Asset – Liability Management (ALM) of banks is one of the significant issues that many managers are being faced with that. Also it is a challenge in the firms' path and especially financial institutions and banks, while many restrictions and rules are being enacted by supervisory organizations and market. On the other hand banks have different objectives that some of them are in different directions. Therefore, Goal Programming is the best method and technique in optimization. In this research a model designed including the operational profit and bounds or restrictions, legal and structural. Coefficients were found through statistical simulation methods. Then LINGO software was ran. There is a significant division out coming from the model and reality, but management can not conform itself with the model, because it is impossible to change the deposits and loans completely and suddenly. The model can be used as a guide in the bank rout.