FINANCIAL KNOWLEDGE OF SECURITY ANALYSIS (FINANCIAL STUDIES)
Year:2017 | Volume:9 | Issue:32|Papers Count:7

The purpose of this article is modeling the behavior of stock price using stochastic differential equations. The data for this study include daily observations of the total stock market index, the index of the top 50 companies and the index of the 30 largest companies in the Tehran Stock Exchange. The data are daily from March 25, 2006 to April 15, 2015.The geometric Brownian motion and geometric Brownian motion with nonlinear GARCH are used to modeling the behavior of price index. The results of this study includes the following: (1) According to the log likelihood function, geometric Brownian motion with nonlinear GARCH in the three groups studied data has better performance than the geometric Brownian motion. (2) Based on the model of stochastic differential equations with stochastic volatility, the total market index is more influenced by the good news. (3) The impact of the bad news on the index of the 30 largest companies is more than the impact of the good news. (4) The unconditional variance of the total stock market index has two structural breaks; the unconditional variance of index of the top 50 companies has one structural break and no structural breaks in the unconditional variance of index of the 30 largest companies.

One of the most important branches of finance is modeling and evaluation of assets pricing. For this reason, many models have been proposed to explain the pricing of assets. Studies of last two decades refer to limits on the models. Such issue is equity risk premium puzzle. In this paper was investigated the theoretical equity risk premium puzzle with the experimental study of this phenomenon using data of years 1367 to 1391 seasonally from the Stock Exchange in Tehran. To investigate this puzzle addition to Mehra and Prescott (1985) method is used estimation of S-CCAPM model using GMM method. S-CCAPM model is adjustment of CCAPM model that was created with the import savings to utility function. Models results show that according to Mehra and Prescott method equity risk premium is obtained 5.7. This value shows that there is risk premium puzzle in the economy of Iran because with the use of empirical data equity risk premium is 0.129. But at second method, equity risk premium gained 0.4. This implies that the adjustment in the base CCAPM can be helped to solve the puzzle.

The present study compares the performance of the long memory FIGARCH model, with that of the short memory GARCH specification, in the forecasting of multi-period value-at-risk and expected shortfall across 3 industry indices in Tehran Stock Exchange such as chemical, vehicle and metals. The dataset is composed of daily data covering the period from May, 2011 to May, 2015. According to the result of this research accounting for fractional integration in the conditional variance model does not appear to improve the accuracy of the VaR forecasts for the 1-day-ahead, 10-day-ahead and 20-day-ahead forecasting horizons relative to the short memory GARCH specification. Furthermore, the GARCH model has a lower quadratic loss between actual returns and ES forecasts, for the majority of the indices considered in 1-day, 10-day and 20-day forecasting horizons. Therefore, a long memory volatility model compared to a short memory GARCH model does not appear to improve the VaR and ES forecasting accuracy, even for longer forecasting horizons.

Make decision is one of the important factor in organization and the managers who act optimization in it can be guide the organization toward successfully. In this research we investigate the relationship Biorhythmic cycles that influence on decision making of investing managers on investment funds in Iran s capital market. The biorhythm components include physical cycle, Intellectual cycle and six sense and the components if error financial decision are compare, commitment, information bias, cognation bias And rare effect. The research method is descriptive and from the aim standpoint it is applicable. The sample estimate by Cochran formula and the questionnaire distributed between them randomly. Reliability and validity controlled, and the reliability based on Cronbach alpha is 0,88. The hypothesis test by spearman correlation and based on it there is significant relationship between physical cycle, six sense and error financial decision. Finally to answer the research question we use the structural equation to show the relationship between error financial decision and biorhythm cycles that showed biorhythm cycle effect on error financial decision is 0,33. for ranking the component of error financial decision we used the Friedman test. In the last we offer some suggestion for researchers, organization, Investment Funds and Managers of Investment funds.

Regime switching models can recognize the tendencies in financial markets and as a result the sudden change of investor behavior and price dynamics. The present study investigates the regime switching behavior of Tehran Stock Exchange using the daily data on the rate of return of stock index price in a period from 1998 to 2014 For this purpose, we use univariate autoregressive process with change in regimes via threshold methods (TAR). The results –using a self exciting threshold auto regression model (SETAR) indicate that the threshold of stock return rate in Iran is negative and the majority of the cases under study are in the high stock return regime. The results from determining the amount of zero predetermined threshold show that Iran Stock Market was in depression for 45.2% and in boom for 54.8%. The average of stock return of boom regime was .08 and of depression regime was .004.

Stock market investments always have been risky because stock returns are volatile. The studies have ever been done on modeling and forecasting stock market volatility has mainly applied the maximum likelihood method and little attention has been paid to the Bayesian estimation method. The reason was that it was assumed that the maximum likelihood method does the best fitting with small volumes of samples. This study tries to estimate GARCH model parameters using Bayesian approach and MCMC algorithm to compare it with maximum likelihood alternative using the daily TEPIX index of Tehran Stock Exchange over the period April 6, 1999 April 20, 2014. For this purpose, the data is divided into three subsamples. The results indicates that; in small samples; the maximum likelihood method is less efficient than Bayesian method but as the sample size increases the efficiency and forecasting accuracy converge in both methods so that distribution function of the parameters is asymptotically asymmetric in small samples and converges to symmetric asymptotic distribution as the sample size increases.

The main objective of this study is to examine the effect of real effective exchange rate misalignment on Iranian non-oil exports over 1982 - 2013.For this purpose, the equilibrium real effective exchange rate has been estimated by BEER approach. The relevant literatures indicate that oil price, fiscal policy indicator, foreign capital flow, trade openness, and terms of trade are main determinants of the equilibrium exchange rate. Using Johansen test indicates that there exist a long run relationship between real exchange rate and other variables. By using predicted value of real effective exchange rate and observe exchange rate the misalignment of real effective exchange rate is calculated. The empirical result of the misalignment indicates that in 2006, the misalignment reached to highest point, 18.67 percent which presents the overvalued Iranian currency. Moreover, the results of estimation show that all explanatory variables in the long term have a significant effect on non-oil exports. In this regard, GDP has a positive impact; the misalignment of the real effective exchange rate, real exchange rate and the terms of trade have a negative and significant effect on the non-oil exports.