On the design of sovereign bond-backed securities

2021 ◽  
pp. 2150033
Author(s):  
Emilio Barucci ◽  
Damiano Brigo ◽  
Marco Francischello ◽  
Daniele Marazzina
Keyword(s):  
At Risk ◽  
Euro Area ◽  
Recovery Rate ◽  
Value At Risk ◽  
Dependence Structure ◽  
Loss Distribution ◽  
Government Bonds ◽  
Sovereign Bond ◽  
Yield Rate ◽  
Correlation Level

In this paper, we analyze Sovereign Bond-Backed Securities in the Euro area, concentrating our attention on the return of the different tranches and on their riskiness. We show that as the correlation level among States increases, the yield rate of senior tranches increases while the yield rate of junior tranches decreases. A similar effect is observed when introducing a block dependence structure with high correlation among States belonging to the same block. Introducing a nonzero recovery rate, as opposed to a null recovery rate, decreases the yield rate of senior tranches and increases the yield rate of junior tranches. We compute the loss distribution and the Value at Risk (VaR) associated with the market risk of retaining the different tranches of the bond. We also analyze the possibility of reaching a safe asset through pooling tranches of government bonds of different States. In summary, we show that the issue in reaching a comprehensive and safe offering through the securitization of government bonds is not the safety of senior tranches but the risk of the junior ones.

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%.

scholarly journals Market-Risk Optimization among the Developed and Emerging Markets with CVaR Measure and Copula Simulation

Risks ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 78
Author(s):  
Trabelsi ◽  
Tiwari
Keyword(s):  
At Risk ◽  
Stock Market ◽  
Value At Risk ◽  
Risk Measure ◽  
Generalized Pareto Distribution ◽  
Market Risk ◽  
Conditional Value At Risk ◽  
Dependence Structure ◽  
Stock Market Returns ◽  
Pareto Tails

In this paper, the generalized Pareto distribution (GPD) copula approach is utilized to solve the conditional value-at-risk (CVaR) portfolio problem. Particularly, this approach used (i) copula to model the complete linear and non-linear correlation dependence structure, (ii) Pareto tails to capture the estimates of the parametric Pareto lower tail, the non-parametric kernel-smoothed interior and the parametric Pareto upper tail and (iii) Value-at-Risk (VaR) to quantify risk measure. The simulated sample covers the G7, BRICS (association of Brazil, Russia, India, China and South Africa) and 14 popular emerging stock-market returns for the period between 1997 and 2018. Our results suggest that the efficient frontier with the minimizing CVaR measure and simulated copula returns combined outperforms the risk/return of domestic portfolios, such as the US stock market. This result improves international diversification at the global level. We also show that the Gaussian and t-copula simulated returns give very similar but not identical results. Furthermore, the copula simulation provides more accurate market-risk estimates than historical simulation. Finally, the results support the notion that G7 countries can provide an important opportunity for diversification. These results are important to investors and policymakers.

scholarly journals Calculando VaR Condicionais Usando Cópulas que Variam no Tempo

2005 ◽  
Vol 3 (2) ◽  
pp. 251 ◽  
Author(s):  
Beatriz Vaz de Melo Mendes
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Multivariate Garch ◽  
Dependence Structure ◽  
Type Model ◽  
Structure Changes ◽  
Multivariate Garch Model ◽  
Volatility Model ◽  
Modeling Strategy ◽  
Changes Over Time

It is now widespread the use of Value-at-Risk (VaR) as a canonical measure at risk. Most accurate VaR measures make use of some volatility model such as GARCH-type models. However, the pattern of volatility dynamic of a portfolio follows from the (univariate) behavior of the risk assets, as well as from the type and strength of the associations among them. Moreover, the dependence structure among the components may change conditionally t past observations. Some papers have attempted to model this characteristic by assuming a multivariate GARCH model, or by considering the conditional correlation coefficient, or by incorporating some possibility for switches in regimes. In this paper we address this problem using time-varying copulas. Our modeling strategy allows for the margins to follow some FIGARCH type model while the copula dependence structure changes over time.

scholarly journals Fitted Copula Statistical Models for Four African and Four Major Stock Markets

2021 ◽  
pp. 195-227
Author(s):  
Ngozi Fidelia Adum ◽  
Happiness Onyebuchi Obiora-Ilouno ◽  
Francis Chukwuemeka Eze
Keyword(s):  
At Risk ◽  
Akaike Information Criterion ◽  
Stock Markets ◽  
Value At Risk ◽  
Information Matrix ◽  
Information Criterion ◽  
Alternative Methods ◽  
Dependence Structure ◽  
P Value ◽  
Copula Models

The application of copula has become popular in recent years. The use of correlation as a dependence measure has several pitfalls and hence the application of regression prediction model using this correlation may not be an appropriate method. In financial markets, there is often a non-linear dependence between returns. Thus, alternative methods for capturing co-dependency should be considered, such as copula based ones. This paper studies the dependence structure between the four largest African stock markets in terms of market capitalization and other developed stock markets over the period 2003 to 2018 using copula models. The value at risk was used to determine the risk associated with the stock. The ten copula models were fitted to the log returns calculated from the data, two countries at a time of the twenty-eight pairs and examined. The Gumbel copula gives the best fit in terms of log-likelihood values, value of the Akaike information criterion, value of the Bayesian information criterion, value of the consistent Akaike information criterion, value of the corrected Akaike information criterion, value of the Hannan Quinn criterion and p-value of the information matrix equality of White. Estimates of value at risk with probability p for daily returns were computed using the best fitted copula model, from these value at risk, it is seen that SA/FTSE100 have the least risk while EGY/KEN has the highest risk. Prediction is given in terms of correlation and value at risk.

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.

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