Reza Taleblou; Mohammad Mahdi Davoudi
Abstract
In this paper, in order to calculate portfolio market risk of 10 selected industries indices in Tehran Stock Exchange, two models of Value Risk (VaR) and Expected shortfall (ES) have been used. Different models of multivariate GARCH and various Coppola models have been used in order to estimate the volatility ...
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In this paper, in order to calculate portfolio market risk of 10 selected industries indices in Tehran Stock Exchange, two models of Value Risk (VaR) and Expected shortfall (ES) have been used. Different models of multivariate GARCH and various Coppola models have been used in order to estimate the volatility of the portfolio and nonlinear correlation of asset portfolio. Backtesting has been done by Kupiec, Christoffersen, Engle and Manganelli and McNeill and Ferry tests. Results show that the DCC-GARCH model by t-Student distribution compared to other competing models has the best results in estimating volatility of the asset portfolio. Also among all Copula models reviewed in this paper, t-student copula model has shown better results for estimating asset dependence. Finally, the results of backtesting of different models showed that both the DCC-GARCH model with t-Student distribution and DCC-GARCH-Copula with t-Student distribution have acceptable results in estimating VaR and ES. However, the Lopez and Blanco and Ihle tests showed that the DCC-GARCH model with t-Student distribution compared to the DCC-GARCH-Copula model with t-Student distribution gives a more accurate and efficient estimate of the VaR and ES of asset portfolios.
Amir Azamtarrahian; Saeed Asadi
Abstract
This paper studies credit risk management in banking industry and proposes a generic model for corporate loan portfolio loss distribution in economic downturns. Basel assumes a one-factor Gaussian copula for default correlations and introduces the regulatory capital on the ground of Vasicek process that ...
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This paper studies credit risk management in banking industry and proposes a generic model for corporate loan portfolio loss distribution in economic downturns. Basel assumes a one-factor Gaussian copula for default correlations and introduces the regulatory capital on the ground of Vasicek process that works acceptably well in normal economic situations but not in recessions. In this paper, one-factor t-student copula is used for dependence structure of probability of Defaults (PDs), and Basel has been extended by introducing correlated PDs and Recovery Rates (RRs) through Clayton copula and the required economic capital is calculated accordingly. Finally, our findings suggest that Expected Shortfall (ES) safeguards banks against losses beyond the VaR level and it is a better risk metric in economic downturns comparing to VaR.
Hossein Raghfar; Narges Ajorlo
Abstract
The purpose of this study is to calculate Value at Risk (VaR) of a selection of bank's currency portfolio, using GARCH-EVT-Copula (GEC) approach. Today's main challenge of a banking system is to calculate and quantify the risks that the system is encountered. There are numerous approaches ...
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The purpose of this study is to calculate Value at Risk (VaR) of a selection of bank's currency portfolio, using GARCH-EVT-Copula (GEC) approach. Today's main challenge of a banking system is to calculate and quantify the risks that the system is encountered. There are numerous approaches to calculate the risks. Usually these approaches assume a common known distribution for the assets portfolio and generally a normal distribution is utilized for the experimental models. Nevertheless, the distributions of the assets are fat-tailed distribution and consequently normal distribution assumption may lead to inaccurate estimation. This article does not assume a specific asset distribution. This study applies autoregressive threshold variances (GJR-GARCH) for intertemporal individual's asset variable returns distribution. It also utilizes extreme value theory or the fat-tailed distributions and Coppola functions for all asset returns in an asset portfolio. In this study VaR is estimated using variance-covariance and historical simulation methods. Finally, in order to test the reliability of the applied models Kopic method is used. The sample data of the bank's currency portfolio consists of the market daily figures of the US Dollar, Japan's Yen, Turkish Lire, Emirate Dirham, Korean Won, and Euro exchange rates from March 21, 2007 till April 19, 2012. The results showed that the estimated VaR using GEC model is higher than the one estimated using the other two methods. They also show that reliability and precision of Kopic test is higher than those of variance-covariance and historical simulation models.
mirhossein mousavi; Hossein Raghfar; Mansooreh Mohseni
Volume 18, Issue 54 , April 2013, , Pages 119-152
Abstract
The traditional approaches for estimating VAR assume that the joint distribution is well-known and the most commonly used normality of the joint distribution of the assets return. In reality, the financial asset return distribution has fatter tails than normal distributions. On the other hand, the use ...
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The traditional approaches for estimating VAR assume that the joint distribution is well-known and the most commonly used normality of the joint distribution of the assets return. In reality, the financial asset return distribution has fatter tails than normal distributions. On the other hand, the use of linear correlation to model the dependence structure shows many disadvantages. Therefore, the problem raised from normality could lead to an inadequate VaR estimate. In order to overcome these problems, this paper resorts to the copula theory which allows the joint distribution of the portfolio to be free from any normality and linear correlation. Combining copula and the forecast function of the GARCH model, this paper proposes a new method, called conditional copula-GARCH, to compute the VaR of portfolios. Examined data in this study includes daily price of selected portfolio, composed of 17 equities for 1082 days in Tehran stock exchange. Presented model compared with traditional methods (including the historical simulation method & variance_covariance method). the results show that conditional copula-GARCH model captures the VaR more successfully at 95% confidence.
