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:: Volume 5, Issue 17 (12-2014) ::
2014, 5(17): 87-122 Back to browse issues page
Stock Optimal Portfolio Selection in a VaR Framework: Comparison the MS-GARCH and Bootstrapping Methods
Hossein Asgharpur , Firouz Fallahi , Naser Sanoubar , Ali Rezazadeh
Abstract:   (6031 Views)
The main goal of this research is to calculate VaR index with parametric Markov-Switching GARCH approach for accepted companies in Tehran Stock Exchange and also selecting the optimal portfolio of their stocks. To calculate the index, data and information of weekly stock price of 10 representative firms during the period 2008-2014 has been used which account for 332 working weeks.
The results from estimation of VaR and determination of optimal stock portfolio in the non-linear programming framework showed that optimal portfolio of food-industry companies stock, in the context of VaR has higher returns and risk in the first regime (Boom period) compared to the second regime ( recession period). On the other hand, it has had lower weight in both stock portfolios that had lower average returns compared to the rest of the stocks and compared to the stocks which had lower VaR relative to other stocks that has higher weights.
The Kupiec and Lopez back testing using 10 future week data, showed that both of approaches is valid but the parametric approach has better rank. Therefore the optimal portfolios of stocks under parametric VaR will be accepted as final optimal portfolio.
Keywords: Stock Optimal Portfolio, Value at Risk, Food Industry Companies, parametric and nonparametric methods
     
Type of Study: Applicable | Subject: پولی و مالی
Received: 2013/10/19 | Accepted: 2014/09/8 | Published: 2014/12/6
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Asgharpur H, Fallahi F, Sanoubar N, Rezazadeh A. Stock Optimal Portfolio Selection in a VaR Framework: Comparison the MS-GARCH and Bootstrapping Methods. Journal title 2014; 5 (17) :87-122
URL: http://jfm.khu.ac.ir/article-1-858-en.html


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Volume 5, Issue 17 (12-2014) Back to browse issues page
فصلنامه تحقیقات مدلسازی اقتصادی Journal of Economic Modeling Research
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