scholarly journals Modeling Portfolio Credit Risk Taking into Account the Default Correlations Using a Copula Approach: Implementation to an Italian Loan Portfolio

2020 ◽  
Vol 13 (6) ◽  
pp. 129
Author(s):  
Annalisa Di Clemente
Keyword(s):  
Credit Risk ◽  
Value At Risk ◽  
Risk Measure ◽  
Dependence Structure ◽  
Quantitative Methodology ◽  
Loan Portfolio ◽  
Recovery Rates ◽  
Asset Return ◽  
Portfolio Credit Risk ◽  
Copula Approach

This work aims to illustrate an advanced quantitative methodology for measuring the credit risk of a loan portfolio allowing for diversification effects. Also, this methodology can allocate the credit capital coherently to each counterparty in the portfolio. The analytical approach used for estimating the portfolio credit risk is a binomial type based on a Monte Carlo Simulation. This method takes into account the default correlations among the credit counterparties in the portfolio by following a copula approach and utilizing the asset return correlations of the obligors, as estimated by rigorous statistical methods. Moreover, this model considers the recovery rates as stochastic and dependent on each other and on the time until defaults. The methodology utilized for coherently allocating credit capital in the portfolio estimates the marginal contributions of each obligor to the overall risk of the loan portfolio in terms of Expected Shortfall (ES), a risk measure more coherent and conservative than the traditional measure of Value-at-Risk (VaR). Finally, this advanced analytical structure is implemented to a hypothetical, but typical, loan portfolio of an Italian commercial bank operating across the overall national country. The national loan portfolio is composed of 17 sub-portfolios, or geographic clusters of credit exposures to 10,500 non-financial firms (or corporates) belonging to each geo-cluster or sub-portfolio. The outcomes, in terms of correlations, portfolio risk measures and capital allocations obtained from this advanced analytical framework, are compared with the results found by implementing the Internal Rating Based (IRB) approach of Basel II and III. Our chief conclusion is that the IRB model is unable to capture the real credit risk of loan portfolios because it does not take into account the actual dependence structure among the default events, and between the recovery rates and the default events. We underline that the adoption of this regulatory model can produce a dangerous underestimation of the portfolio credit risk, especially when the economic uncertainty and the volatility of the financial markets increase.

The Credit Securitisation Process as a Tool of Portfolio Credit Risk Managing

2012 ◽  
pp. 5-28
Author(s):  
Di Clemente Annalisa
Keyword(s):  
Credit Risk ◽  
Simulation Analysis ◽  
Risk Mitigation ◽  
Probability Of Default ◽  
Loan Portfolio ◽  
Asset Return ◽  
Portfolio Credit Risk ◽  
The Impact ◽  
Monte Carlo Simulation Model

This study explores the role of the credit securitisation process in managing the credit risk amount of the banking loan portfolio, when the bank originator retains a residual equitylike class as illiquid first loss position (FLP). An Importance Sampling Monte Carlo simulation model has been implemented for estimating the portfolio credit risk amount, taking into account the portfolio credit risk mitigation effect provided by the credit securitisation process. This study identifies the credit asset pool able to produce the larger effect of credit risk reduction on the loan portfolio, when the asset pool is unloaded off the banking book. Moreover, this simulation analysis quantifies the extent of the portfolio credit risk mitigation, produced by the securitisation process of the asset pool previously identified. The impact of the securitisation activity has been also investigated when the probability of default and the asset return correlation of the obligors in portfolio are changing.

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 THE EFFECTS OF MACROECONOMIC CONDITIONS ON LOAN PORTFOLIO CREDIT RISK AND BANKING SYSTEM INTEREST INCOME

Ekonomika ◽  
2012 ◽  
Vol 91 (3) ◽  
pp. 85-100 ◽  
Author(s):  
Ričardas Mileris
Keyword(s):  
Credit Risk ◽  
Banking System ◽  
Classification And Regression Tree ◽  
Economic Environment ◽  
Statistical Dependence ◽  
Tree Model ◽  
Loan Portfolio ◽  
Portfolio Credit Risk ◽  
Macroeconomic Conditions ◽  
Interest Income

This article presents on analysis of macroeconomic conditions in the EU countries in relation loan portfolio to credit risk and banking system interest income. The changing economic environment of banks influences their risks and activity results, so it is important to find the macroeconomic indicators that can determine the changes in debtors’ credit risk and banks’ financial condition. The banking system performs very important functions in a country’s financial system, so for its stability it is important to be able to predict the financial results of the banking system in relation to changes in the economic environment. The new Basel III Agreement seeks to improve the financial sector’s resistance to the possible negative scenarios in the economy and motivates to develop the credit risk assessment models considering their dependence on business cycles. For this reason, the statistical dependence between the set of macroeconomic factors and the loan portfolio credit risk together with interest income were estimated in this research. A statistical classification and regression tree model was developed, which allows to predict the possible changes in the interest income of a country’s banks with the 82.7% accuracy.

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 A Discussion on Recent Risk Measures with Application to Credit Risk: Calculating Risk Contributions and Identifying Risk Concentrations

Risks ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 142 ◽  
Author(s):  
Matthias Fischer ◽  
Thorsten Moser ◽  
Marius Pfeuffer
Keyword(s):  
Credit Risk ◽  
Value At Risk ◽  
Risk Measures ◽  
Risk Measure ◽  
Theory And Practice ◽  
Credit Risk Management ◽  
It Implementation ◽  
Benchmark Portfolio ◽  
Credit Portfolio ◽  
First Time

In both financial theory and practice, Value-at-risk (VaR) has become the predominant risk measure in the last two decades. Nevertheless, there is a lively and controverse on-going discussion about possible alternatives. Against this background, our first objective is to provide a current overview of related competitors with the focus on credit risk management which includes definition, references, striking properties and classification. The second part is dedicated to the measurement of risk concentrations of credit portfolios. Typically, credit portfolio models are used to calculate the overall risk (measure) of a portfolio. Subsequently, Euler’s allocation scheme is applied to break the portfolio risk down to single counterparties (or different subportfolios) in order to identify risk concentrations. We first carry together the Euler formulae for the risk measures under consideration. In two cases (Median Shortfall and Range-VaR), explicit formulae are presented for the first time. Afterwards, we present a comprehensive study for a benchmark portfolio according to Duellmann and Masschelein (2007) and nine different risk measures in conjunction with the Euler allocation. It is empirically shown that—in principle—all risk measures are capable of identifying both sectoral and single-name concentration. However, both complexity of IT implementation and sensitivity of the risk figures w.r.t. changes of portfolio quality vary across the specific risk measures.

scholarly journals On Partial Stochastic Comparisons Based on Tail Values at Risk

Mathematics ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. 1181
Author(s):  
Alfonso J. Bello ◽  
Julio Mulero ◽  
Miguel A. Sordo ◽  
Alfonso Suárez-Llorens
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Risk Measure ◽  
Real Data ◽  
Stochastic Orders ◽  
Stochastic Comparisons ◽  
Increasing Convex Order ◽  
Asset Return ◽  
Confidence Levels ◽  
Return Distributions

The tail value at risk at level p, with p ∈ ( 0 , 1 ) , is a risk measure that captures the tail risk of losses and asset return distributions beyond the p quantile. Given two distributions, it can be used to decide which is riskier. When the tail values at risk of both distributions agree, whenever the probability level p ∈ ( 0 , 1 ) , about which of them is riskier, then the distributions are ordered in terms of the increasing convex order. The price to pay for such a unanimous agreement is that it is possible that two distributions cannot be compared despite our intuition that one is less risky than the other. In this paper, we introduce a family of stochastic orders, indexed by confidence levels p 0 ∈ ( 0 , 1 ) , that require agreement of tail values at risk only for levels p > p 0 . We study its main properties and compare it with other families of stochastic orders that have been proposed in the literature to compare tail risks. We illustrate the results with a real data example.

scholarly journals Systemic Risk Modeling with Lévy Copulas

2021 ◽  
Vol 14 (6) ◽  
pp. 251
Author(s):  
Yuhao Liu ◽  
Petar M. Djurić ◽  
Young Shin Kim ◽  
Svetlozar T. Rachev ◽  
James Glimm
Keyword(s):  
At Risk ◽  
Systemic Risk ◽  
Value At Risk ◽  
Joint Distribution ◽  
Risk Measure ◽  
Tail Dependence ◽  
Risk Modeling ◽  
Jones Index ◽  
Out Of Sample ◽  
Copula Approach

We investigate a systemic risk measure known as CoVaR that represents the value-at-risk (VaR) of a financial system conditional on an institution being under distress. For characterizing and estimating CoVaR, we use the copula approach and introduce the normal tempered stable (NTS) copula based on the Lévy process. We also propose a novel backtesting method for CoVaR by a joint distribution correction. We test the proposed NTS model on the daily S&P 500 index and Dow Jones index with in-sample and out-of-sample tests. The results show that the NTS copula outperforms traditional copulas in the accuracy of both tail dependence and marginal processes modeling.

Lending Booms

2019 ◽  
pp. 116-131
Author(s):  
Hyun Song Shin
Keyword(s):  
At Risk ◽  
Credit Risk ◽  
Value At Risk ◽  
Risk Model ◽  
House Prices ◽  
Loan Portfolio ◽  
Balance Sheets ◽  
Credit Risks ◽  
Credit Risk Model

A system of interlinked balance sheets of intermediaries that follow the Value-at-risk rule has the feature that an increase in house prices transmits valuation changes through the value of debt instruments. The analysis uses the Vasicek credit risk model for the diversification of individual credit risks in the loan portfolio. Leverage and wholesale funding is key to understanding lending booms.

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