scholarly journals Large portfolio risk management and optimal portfolio allocation with dynamic elliptical copulas

2018 ◽  
Vol 6 (1) ◽  
pp. 19-46 ◽  
Author(s):  
Xisong Jin ◽  
Thorsten Lehnert
Keyword(s):  
Risk Management ◽  
Value At Risk ◽  
Degrees Of Freedom ◽  
Portfolio Allocation ◽  
Optimal Portfolio ◽  
Gaussian Copula ◽  
High Dimensional ◽  
Dependence Structure ◽  
Portfolio Risk ◽  
Portfolio Risk Management

Abstract Previous research has focused on the importance of modeling the multivariate distribution for optimal portfolio allocation and active risk management. However, existing dynamic models are not easily applied to high-dimensional problems due to the curse of dimensionality. In this paper, we extend the framework of the Dynamic Conditional Correlation/Equicorrelation and an extreme value approach into a series of Dynamic Conditional Elliptical Copulas. We investigate risk measures such as Value at Risk (VaR) and Expected Shortfall (ES) for passive portfolios and dynamic optimal portfolios using Mean-Variance and ES criteria for a sample of US stocks over a period of 10 years. Our results suggest that (1) Modeling the marginal distribution is important for dynamic high-dimensional multivariate models. (2) Neglecting the dynamic dependence in the copula causes over-aggressive risk management. (3) The DCC/DECO Gaussian copula and t-copula work very well for both VaR and ES. (4) Grouped t-copulas and t-copulas with dynamic degrees of freedom further match the fat tail. (5) Correctly modeling the dependence structure makes an improvement in portfolio optimization with respect to tail risk. (6) Models driven by multivariate t innovations with exogenously given degrees of freedom provide a flexible and applicable alternative for optimal portfolio risk management.

scholarly journals Estimating Risk of Natural Gas Portfolios by Using GARCH-EVT-Copula Model

2015 ◽  
Vol 2015 ◽  
pp. 1-7
Author(s):  
Jiechen Tang ◽  
Chao Zhou ◽  
Xinyu Yuan ◽  
Songsak Sriboonchitta
Keyword(s):  
At Risk ◽  
Natural Gas ◽  
Value At Risk ◽  
Extreme Value Distribution ◽  
Gaussian Copula ◽  
Conditional Value At Risk ◽  
Dependence Structure ◽  
Copula Model ◽  
Portfolio Risk ◽  
Multivariate Gaussian

This paper concentrates on estimating the risk of Title Transfer Facility (TTF) Hub natural gas portfolios by using the GARCH-EVT-copula model. We first use the univariate ARMA-GARCH model to model each natural gas return series. Second, the extreme value distribution (EVT) is fitted to the tails of the residuals to model marginal residual distributions. Third, multivariate Gaussian copula and Studentt-copula are employed to describe the natural gas portfolio risk dependence structure. Finally, we simulate N portfolios and estimate value at risk (VaR) and conditional value at risk (CVaR). Our empirical results show that, for an equally weighted portfolio of five natural gases, the VaR and CVaR values obtained from the Studentt-copula are larger than those obtained from the Gaussian copula. Moreover, when minimizing the portfolio risk, the optimal natural gas portfolio weights are found to be similar across the multivariate Gaussian copula and Studentt-copula and different confidence levels.

Portfolio Risk Management Analysis

2010 ◽  
pp. 453-464
Author(s):  
Kandi Brown ◽  
William L. Hall ◽  
Robert Barrett ◽  
Patrick Gobb
Keyword(s):  
Risk Management ◽  
Portfolio Risk ◽  
Portfolio Risk Management

scholarly journals ESTIMASI NILAI CONDITIONAL VALUE AT RISK MENGGUNAKAN FUNGSI GAUSSIAN COPULA

2015 ◽  
Vol 4 (4) ◽  
pp. 188
Author(s):  
HERLINA HIDAYATI ◽  
KOMANG DHARMAWAN ◽  
I WAYAN SUMARJAYA
Keyword(s):  
At Risk ◽  
Confidence Level ◽  
Value At Risk ◽  
Risk Measure ◽  
Copula Function ◽  
Gaussian Copula ◽  
Nonlinear Dependence ◽  
Conditional Value At Risk ◽  
Dependence Structure ◽  
Measurement Tools

Copula is already widely used in financial assets, especially in risk management. It is due to the ability of copula, to capture the nonlinear dependence structure on multivariate assets. In addition, using copula function doesn’t require the assumption of normal distribution. There fore it is suitable to be applied to financial data. To manage a risk the necessary measurement tools can help mitigate the risks. One measure that can be used to measure risk is Value at Risk (VaR). Although VaR is very popular, it has several weaknesses. To overcome the weakness in VaR, an alternative risk measure called CVaR can be used. The porpose of this study is to estimate CVaR using Gaussian copula. The data we used are the closing price of Facebook and Twitter stocks. The results from the calculation using 90%  confidence level showed that the risk that may be experienced is at 4,7%, for 95% confidence level it is at 6,1%, and for 99% confidence level it is at 10,6%.

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