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 ...
Read More
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.
Reza Taleblou; mohammad mahdi davoudi
Abstract
In recent years, by using extreme value theory (EVT), researchers have estimated the market risk for rare events (crises) more accurately. This paper examines the different methods of measuring market risk at different levels of reliability. According to the assumptions of the EVT methods, measuring ...
Read More
In recent years, by using extreme value theory (EVT), researchers have estimated the market risk for rare events (crises) more accurately. This paper examines the different methods of measuring market risk at different levels of reliability. According to the assumptions of the EVT methods, measuring the effects of the financial crises on the value of assets requires a lot of time-series observations. Therefore, this paper has used four indices: total index, industry index, the first market index and the second market index of the Tehran stock exchange. The backtesting results showed that among the various methods, semi-parametric approach or the EVT approach in comparison with parametric (EWMA, MA, GARCH) and nonparametric approaches (Historical simulation) is more efficient and has a higher level of reliability. Also HS method shows acceptable results at high confidence level, while in calculating the value at risk in the 0.90 and 0.95 confidence levels, parametric methods (EWMA, MA, GARCH) provide more reliable results. Also the richness of the dynamics of GARCH and EWMA models are much more than the other models. In the next step by incorporating various models, the three models EWMA-EVT, GARCH-EVT and AWHS were made. Backtesting these three patterns showed that, AWHS and EWMA-EVT have provided the best results among various patterns, and have provided acceptable adequacy in estimating the value at risk at all levels of reliability. However; GARCH-EVT model shows acceptable results only in 0.999 reliability level.
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 ...
Read More
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.
Shiva Zamani; Majid Alifar
Volume 19, Issue 59 , July 2014, , Pages 183-210
Abstract
In this paper we formulate the volatility of the Metal Index, by an ARJI-GARCH model, with reference to the effect of the volatility of dollar exchange rate. We express the jump in the dollar exchange rate by an Autoregressive Conditional Jump Intensity (ARJI) model, and then use the output to model ...
Read More
In this paper we formulate the volatility of the Metal Index, by an ARJI-GARCH model, with reference to the effect of the volatility of dollar exchange rate. We express the jump in the dollar exchange rate by an Autoregressive Conditional Jump Intensity (ARJI) model, and then use the output to model the volatility of Tehran Base Metal Index by GARCH. To test the model, we calculate VaR using the ARJI-GARCH volatility, and the GARCH volatilities without considering the dollar exchange rate volatility or the jump in it. We calculate the referred VaR also by an exponentially time weighted historical simulation method. We test the accuracy and preciseness of the resulted VaRs by the associated statistical tests, and conclude that the ARJI-GARCH model is well suited for forecasting volatility in this context
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 ...
Read More
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.
Naser Khiabani; Maryam Sarooghi
Volume 16, Issue 47 , July 2011, , Pages 53-73
Abstract
This paper studies four ARCH type models including ARCH, GARCH, EGARCH and TGARCH at Value at Risk (VaR) estimation. The four models were applied to daily Tehran stock market data to assess each model in estimating one day Value at Risk at various confidence intervals. Our findings suggest that for the ...
Read More
This paper studies four ARCH type models including ARCH, GARCH, EGARCH and TGARCH at Value at Risk (VaR) estimation. The four models were applied to daily Tehran stock market data to assess each model in estimating one day Value at Risk at various confidence intervals. Our findings suggest that for the daily return of Tehran Stock market, which exhibit fat-tailed and leptokurtic features, the VaR estimates generated by the GARCH-T models have good accuracy at high confidence levels.
Zahra Nasrollahi; Mina Shahviri,
Volume 15, Issue 44 , October 2010, , Pages 199-230
Abstract
Management of Foreign exchange reserves is important for every country. This matter is also of particular interest for Iran as an Oil exporting developing country. This paper designs an optimal portfolio for that part of foreign exchange incomes which is used for investment. Using the data on foreign ...
Read More
Management of Foreign exchange reserves is important for every country. This matter is also of particular interest for Iran as an Oil exporting developing country. This paper designs an optimal portfolio for that part of foreign exchange incomes which is used for investment. Using the data on foreign exchange daily returns, for the period 2000-2008, and applying univariate and multivariate Garch models, we estimate a model which maximizes expected returns subject to a Value-at-Risk constraint. The results are examined using Backtesting, and then the most acceptable model is suggested. The results that the multivariate GARCH model is the most efficient method for selecting the foreign exchange optimal portfolio in Iran.
Javad Torkamani; Ali Hosseini
Volume 8, Issue 29 , February 2007, , Pages 75-92
Abstract
The main objective of this paper is to determine the optimum portfolio of the Tehran Stock Exchange with respect to the Value at Risk (VaR) index. Daily data are on the shares of 30 active companies traded in the Tehran Stock Exchange with daily expected return above 0.4 percent in 2004. Optimum portfolio ...
Read More
The main objective of this paper is to determine the optimum portfolio of the Tehran Stock Exchange with respect to the Value at Risk (VaR) index. Daily data are on the shares of 30 active companies traded in the Tehran Stock Exchange with daily expected return above 0.4 percent in 2004. Optimum portfolio is selected subject to the investors' budget, risk and VaR confidence levels. Results reveal that the higher confidence level of VaR requires more diversified portfolio. Therefore, beginner investors and those with higher degree of risk aversion should diversify their budget among shares of various companies. Also, the level of investment affects the combination of the selected portfolio. Results also show that the Risk-Return trade off are in favor of risk averse investors and the change in time length period can also change the optimal portfolio.
