Showing 4 results for Value at Risk
Hossein Asgharpur, Firouz Fallahi, Naser Sanoubar, Ali Rezazadeh,
Volume 5, Issue 17 (10-2014)
Abstract
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.
Bagher Adabi Firouzjaee, Mohsen Mehrara, Shapour Mohammadi,
Volume 7, Issue 23 (3-2016)
Abstract
Value at risk (VaR) is one of the most important risk measures for computing risk which is entered in financial framework in past two decades. In general there are three approaches including parametric, nonparametric and semi-parametric is used for estimating of VaR. this paper present a new method that is named window simulation which is classified in nonparametric approach. Processing of VaR calculation in window simulation method based on reproduction of data such as Monte Carlo simulation. But, in this new method, data production is done in basis of distance and similarity measures. Considering generated distribution quantile, VaR is estimated. Next, VaR of Tehran Stock Exchange indices are computed by this method. Also the accuracy of estimated VaR is evaluated by backtesting statistics. Empirical results indicate that based on window method, the best outcome is associated to measures of Euclidean, DTW, Kolmogorov-Smirnov, square χ^2 , distance-similar and cosine respectively.
Mohammad Sayadi, Nasim Karimi,
Volume 10, Issue 38 (12-2019)
Abstract
The main objective of this study is modeling the dependency structure between the returns of oil markets, exchange rate and stocks of chemical products in Iran. For this purpose, the theory of Vine Copula functions is used to investigate the dependency structure. In addition to consider a linear relationship between financial markets in Iran, the nonlinear dependency structure of these markets is also estimated, and their dependence on their upper or lower tails is determined. The study period includes daily data (5 working days) from December 2008 to July 2017. Modeling of marginal distributions of GJR-GARCH models has been used. Then, using the Copula-GARCH approach, the structure of dependency between returns and the calculating of the Value at Risk (VaR) of crude oil, exchange rate and stock of the chemical product group returns have been investigated. Finally, the required back-test is performed on the basis of the loss function. The study findings show that both pairs of modeling returns are related to the same upper and lower tails. In addition, there is a same structural dependency on the distribution of the vine copula between the indexes of chemical products and the nominal exchange rate on the condition of the price of crude oil, which indicates the spillover between markets. Due to that spillover effect is the main source of financial risk, the structural dependence on the basis of vine copula functions makes accurate and reliable calculation of portfolio risk based on the VaR criterion.
Seyed Ali Naseri, Farkhondeh Jabal Ameli, Sajad Barkhordary Dorbash,
Volume 11, Issue 41 (10-2020)
Abstract
Systemic risk arises from simultaneous movement or correlations between market segments; Thus, systemic risk occurs when there is a high correlation between the risks and crises of different market segments or institutions operating in the economy, or when the risks of different segments in a market segment or a country are related to other segments and other countries. This paper presents a measure of systemic risk calculation to effectively describe the systemic importance of each financial institution in a system. The DCC-GARCH methodology with normal and t-student distributions has been used to examine the correlation of time-varying banks. The results of this section show that the application of DCC-GARCH-student-t model is preferable to DCC-GARCH-normal model. In order to investigate the presence of leverage effect, GJR-GARCH model was used and the results of estimation showed the presence of asymmetry and the absence of leverage effect in the data. In the study of dynamic conditional correlation between selected banks, it is also observed that α_C ,β_C are not significant for both estimation cases. Therefore, in both cases, it is estimated based on the normal distribution and t-student α_C=β_C=0 and the conditional correlation becomes constant. Based on the results of shapley value and in order to allocate the total risk between the banks in the sample, Parsian, Mellat, EN, Tejarat and Saderat banks have the most systemic importance for the period of June 17, 2009 to May 7, 2019.
