The Systemic Risk Implications of Using Credit Ratings Versus Quantitative Measures to Limit Bond Portfolio Risk

2019 ◽  
Author(s):  
Gunter Löffler
Keyword(s):  
Systemic Risk ◽  
Credit Ratings ◽  
Portfolio Risk ◽  
Bond Portfolio

Contagion effect on bond portfolio risk measures in a hybrid credit risk model

2014 ◽  
Vol 11 (2) ◽  
pp. 131-139 ◽  
Author(s):  
Mathieu Boudreault ◽  
Geneviève Gauthier ◽  
Tommy Thomassin
Keyword(s):  
Credit Risk ◽  
Risk Model ◽  
Risk Measures ◽  
Contagion Effect ◽  
Portfolio Risk ◽  
Bond Portfolio ◽  
Credit Risk Model

scholarly journals Financial Network Connectedness and Systemic Risk During the COVID-19 Pandemic

2021 ◽  
Author(s):  
Mike K. P. So ◽  
Lupe S. H. Chan ◽  
Amanda M. Y. Chu
Keyword(s):  
Financial Markets ◽  
Systemic Risk ◽  
World Economy ◽  
Network Density ◽  
Portfolio Risk ◽  
Huge Number ◽  
Financial Network ◽  
Rolling Window ◽  
Causality Tests ◽  
Network Connectedness

AbstractThe COVID-19 pandemic causes a huge number of infections. The outbreak of COVID-19 has not only caused substantial healthcare impacts, but also affected the world economy and financial markets. In this paper, we study the effect of the COVID-19 pandemic on financial market connectedness and systemic risk. Specifically, we test dynamically whether the network density of pandemic networks constructed by the number of COVID-19 confirmed cases is a leading indicator of the financial network density and portfolio risk. Using rolling-window Granger-causality tests, we find strong evidence that the pandemic network density leads the financial network density and portfolio risk from February to April 2020. The findings suggest that the COVID-19 pandemic may exert significant impact on the systemic risk in financial markets.

Assessment of Credit Risk Models on Rule 144A Corporate Bonds

2018 ◽  
Keyword(s):  
Credit Risk ◽  
Default Risk ◽  
Credit Ratings ◽  
The United States ◽  
Convertible Bonds ◽  
Discrete Models ◽  
Corporate Bond ◽  
Risk Models ◽  
Bond Portfolio ◽  
Portfolio Managers

Accurate assessment of credit risk can improve the performance of bond portfolio managers. Using credit ratings and market-based credit risk models from S&P and Bloomberg, we investigate the performance of four credit risk models in the Rule 144A corporate bond markets in the United States over the 1990–2015 period. The authors divide their sample into straight bonds and convertible bonds and find that (1) when it comes to straight bonds, discrete models such as S&P’s credit ratings and Bloomberg ratings determine yields more accurately than the continuous market-based models of S&P and Bloomberg; (2) with regard to convertible bonds, a convertible option has a stronger effect than credit ratings in determining yields, and only Bloomberg default risk ratings, not S&P credit ratings, determine the yields; (3) for convertible bonds, the continuous market-based models of S&P and Bloomberg affect yields more significantly than discrete models; and (4) when it comes to predicting actual defaults, Bloomberg models are superior to S&P’s models, and the Bloomberg discrete model has more power than its continuous counterpart.

scholarly journals PENGUKURAN RISIKO KREDIT DAN PENGUKURAN KINERJA DARI PORTOFOLIO OBLIGASI

2018 ◽  
Vol 7 (1) ◽  
pp. 43-53
Author(s):  
Bimbi Ardhana Rizky ◽  
Sudarno Sudarno ◽  
Diah Safitri
Keyword(s):  
Credit Risk ◽  
Loss Probability ◽  
Portfolio Performance ◽  
Portfolio Risk ◽  
Credit Risks ◽  
Bond Portfolio ◽  
Mean Variance ◽  
Return Value ◽  
Transition Rating

Except getting coupon as a profit, there is loss probability in bond investment that is credit risks investment. One way to measure the credit risk of a bond is to use the credit metrics method. It uses the ratings of the bond issuer company and the transition rating issued by the rating company for its calculations. Mean Variance Efficient Portfolio (MVEP) can be used to make an optimal portfolio so that risk can be obtained to a minimum. An assessment of portfolio performance is needed  to increase confidence to invest. Sharpe index can measure portfolio performance based on return value of bond. In this case, study has been conduct in two bonds which are Obligasi Berkelanjutan I Bank BTN Tahap II Tahun 2013 and Obligasi Berkelanjutan I PLN Tahap I Tahun 2013 Seri B. The optimum portfolio formed results 67,96% proportion for the first bond and 32,04% for the second bond. For the result, and there is Rp239,4235(billion) of portfolio risk formed. And there is 0,212496for Sharpe index performance assessment portfolio. Keywords: Bond, portfolio, credit risk, credit metrics, Mean Variance Efficient Portfolio, Sharpe index

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