University of TehranFinancial Research Journal1024-815319120170421Risk Evaluation of Banking Index with Volatility Estimation through Stochastic Volatility Model: A Semiparametric Bayesian ApproachRisk Evaluation of Banking Index with Volatility Estimation through Stochastic Volatility Model: A Semiparametric Bayesian Approach81966422910.22059/jfr.2017.207047.1006201FARasoulSajjadAssistant Prof., Faculty of Engineering, University of Science and Culture, Tehran, IranZahraAbtahiMSc. in Financial Engineering, University of Science and Culture, Tehran, IranJournal Article20160528Estimation of the return distribution has a crucial role in Risk measurement and since the precision of risk measures depends on the precision of the return distribution, truly estimation of return distribution has attracted a huge attention. Although using Stochastic Volatility models with parametric assumptions for estimation and illustration of the volatilities has been common in research, these assumptions usually result in careless estimations. So in the following research a semiparametric approach has been used for estimation of the volatility by using a normal mixture dirichlet process. In this paper the distribution of the logarithm of the squared returns of banking index of Tehran Stock Exchange has been estimated by using mixtures of normal family and employing an MCMC algorithm. Finally, the results has been compared to the Basic stochastic volatility model. The results show that when the return distribution is skewed, estimates of volatility using the model can differ dramatically from those using a Normal return distribution. Furthermore, when return distribution is similar to a normal distribution, the results of this model are similar to the results of the parametric model.Estimation of the return distribution has a crucial role in Risk measurement and since the precision of risk measures depends on the precision of the return distribution, truly estimation of return distribution has attracted a huge attention. Although using Stochastic Volatility models with parametric assumptions for estimation and illustration of the volatilities has been common in research, these assumptions usually result in careless estimations. So in the following research a semiparametric approach has been used for estimation of the volatility by using a normal mixture dirichlet process. In this paper the distribution of the logarithm of the squared returns of banking index of Tehran Stock Exchange has been estimated by using mixtures of normal family and employing an MCMC algorithm. Finally, the results has been compared to the Basic stochastic volatility model. The results show that when the return distribution is skewed, estimates of volatility using the model can differ dramatically from those using a Normal return distribution. Furthermore, when return distribution is similar to a normal distribution, the results of this model are similar to the results of the parametric model.https://jfr.ut.ac.ir/article_64229_19bfd53d0374ab2500ec19856b7dfc61.pdf