Credit quality of non-financial corporate bonds has deteriorated globally

2019 ◽  
Keyword(s):  
Corporate Bonds ◽  
Credit Quality

The Decline in Credit Quality of New-Issue Junk Bonds

1990 ◽  
Vol 46 (5) ◽  
pp. 53-62 ◽  
Author(s):  
Barrie A. Wigmore
Keyword(s):  
Junk Bonds ◽  
Credit Quality

scholarly journals A DYNAMIC MODEL OF CENTRAL COUNTERPARTY RISK

2018 ◽  
Vol 21 (08) ◽  
pp. 1850050
Author(s):  
TOMASZ R. BIELECKI ◽  
IGOR CIALENCO ◽  
SHIBI FENG
Keyword(s):  
Dynamic Model ◽  
Numerical Study ◽  
Risk Measures ◽  
Structure Model ◽  
Counterparty Risk ◽  
Dynamic Risk Measures ◽  
Credit Quality ◽  
Dynamic Risk ◽  
Risk Sensitive

We introduce a dynamic model of the default waterfall of derivatives central counterparties and propose a risk sensitive method for sizing the initial margin, and the default fund and its allocation among clearing members. Using a Markovian structure model of joint credit migrations, our evaluation of the default fund takes into account the joint credit quality of clearing members as they evolve over time. Another important aspect of the proposed methodology is the use of the time consistent dynamic risk measures for computation of the initial margin and the default fund. We carry out a comprehensive numerical study, where, in particular, we analyze the advantages of the proposed methodology and its comparison with the currently prevailing methods used in industry.

Macro Stress Testing of Indian Bank Groups

2017 ◽  
Vol 11 (4) ◽  
pp. 375-403
Author(s):  
Pami Dua ◽  
Hema Kapur
Keyword(s):  
Exchange Rate ◽  
Interest Rate ◽  
Private Sector ◽  
Stress Testing ◽  
Inflation Rate ◽  
Output Growth ◽  
Foreign Banks ◽  
Credit Quality ◽  
The Impact

This study examines how various bank groups operating in India have fared macro stress events and conduct macro stress testing (MST) to trace the impact of certain macroeconomic stress scenarios on the credit quality of five Indian bank groups, that is, the State Bank of India (SBI) and its associates (SBGs), nationalised banks (NBs), old private sector banks (OPBs), new private sector banks (NPBs) and foreign banks (FBs), using panel data from 1997 to 2014. Credit quality is modelled as a function of both macroeconomic variables (output growth, interest rate, inflation rate and exchange rate) and idiosyncratic variables (profitability and size indicator of bank business activity). The model is estimated by employing a panel cointegration approach, and the impact of adverse scenarios on the estimated credit quality is computed. Empirical findings show that credit quality is pro-cyclical in nature and rises in the event of a slowdown in the economy. In general, the credit quality of Indian bank groups is found to be inversely and significantly related to the economy’s growth rate, inflation rate, exchange rate and profits of banks and positively and significantly related to the interest rate. Shock analysis also reveals that a downturn in the economy through certain adverse scenarios has a significant adverse impact on the credit quality. The shocks are quickly propagated across banks with substantial heterogeneities present in different bank groups. Thus, macroeconomic policy measures promoting growth with price stability are expected to impact credit quality positively. Further, measures at the bank level can improve credit quality by enhancing their profitability. JEL Classifications: C32, C58, E170, G21

scholarly journals A primer on counterparty valuation adjustments in South Africa

2014 ◽  
Vol 17 (5) ◽  
pp. 584-600
Author(s):  
Gary Wayne Van Vuuren ◽  
Ja'nel Esterhuysen
Keyword(s):  
South Africa ◽  
Financial Crisis ◽  
Credit Risk ◽  
South African ◽  
Credit Spreads ◽  
Basel Iii ◽  
Credit Quality ◽  
Counterparty Credit Risk ◽  
Market Variables

Counterparty valuation adjustment (CVA) risk accounts for losses due to the deterioration in credit quality of derivative counterparties with large credit spreads. Of the losses attributed to counterparty credit risk incurred during the financial crisis of 2008-9 were due to CVA risk; the remaining third were due to actual defaults. Regulatory authorities have acknowledged and included this risk in the new Basel III rules. The capital implications of CVA risk in the South African milieu are explored, as well as the sensitivity of CVA risk components to market variables. Proposed methodologies for calculating changes in CVA are found to be unstable and unreliable at high average spread levels.

Impact of Unfunded Pension Obligations on Credit Quality of State Governments

2013 ◽  
Vol 33 (3) ◽  
pp. 24-54 ◽  
Author(s):  
CHRISTINE R. MARTELL ◽  
SHARON N. KIOKO ◽  
TIMA MOLDOGAZIEV
Keyword(s):  
State Governments ◽  
Credit Quality

Financing environmentally-sustainable projects with green bonds

2019 ◽  
Vol 24 (6) ◽  
pp. 608-623 ◽  
Author(s):  
Elettra Agliardi ◽  
Rossella Agliardi
Keyword(s):  
Structural Model ◽  
Environmental Concern ◽  
Policy Implications ◽  
Environmentally Sustainable ◽  
Credit Quality ◽  
Bond Issuers ◽  
The Difference ◽  
Sustainable Investments ◽  
Sustainable Projects

AbstractA structural model for green bonds is developed to explain the formation and dynamics of green bond prices and to address the issue of the so-called ‘greenium’, that is, the difference between the yields on a conventional bond and a green bond with the same characteristics. We provide answers to the following questions: What are the determinants of the green bond value? Do green bonds enhance the credit quality of the issuer? Are green bonds a relatively cheap tool to fund sustainable investments? We also study the effect of investors' environmental concern on portfolio allocation. Our results have direct policy implications and suggest that an improvement in credit quality could ultimately lead to a lower cost of capital for green bond issuers and that governmental tax-based incentives and an increase in investors' green awareness play a significant role in scaling up the green bonds market.

scholarly journals Loss Allocation in Securitization Transactions

2012 ◽  
Vol 47 (5) ◽  
pp. 1125-1153 ◽  
Author(s):  
Günter Franke ◽  
Markus Herrmann ◽  
Thomas Weber
Keyword(s):  
Corporate Bonds ◽  
Predominant Role ◽  
Tobin's Q ◽  
Underlying Asset ◽  
Loss Allocation ◽  
Collateralized Debt Obligation ◽  
Collateralized Debt ◽  
Good Rating ◽  
Debt Obligation

AbstractThis paper analyzes the loss allocation to first, second, and third loss positions in European collateralized debt obligation transactions. The quality of the underlying asset pool plays a predominant role for the loss allocation. A lower asset pool quality induces the originator to take a higher first loss position, but, in a synthetic transaction, a smaller third loss position. The share of expected default losses, borne by the first loss position, is largely independent of asset pool quality but lower in securitizations of corporate loans than in those of corporate bonds. Originators with a good rating and low Tobin’s Q prefer synthetic transactions.

The execution quality of corporate bonds

2018 ◽  
Vol 130 (2) ◽  
pp. 308-326 ◽  
Author(s):  
Maureen O’ Hara ◽  
Yihui Wang ◽  
Xing (Alex) Zhou
Keyword(s):  
Corporate Bonds

scholarly journals Empirical Pricing of Chinese Defaultable Corporate Bonds Based on the Incomplete Information Model

2014 ◽  
Vol 2014 ◽  
pp. 1-5 ◽  
Author(s):  
Li Ping ◽  
Wang Xiaoxu
Keyword(s):  
Incomplete Information ◽  
Default Risk ◽  
Corporate Bonds ◽  
Information Model ◽  
Corporate Bond ◽  
Reduced Form ◽  
First Year ◽  
The Real ◽  
Form Model

The default of Suntech Power made the year 2013 in China “the first year of default” of bond markets. People are also clearly aware of the default risk of corporate bonds and find that fair pricing for defaultable corporate bonds is very important. In this paper we first give the pricing model based on incomplete information, then empirically price the Chinese corporate bond “11 super JGBS” from Merton’s model, reduced-form model, and incomplete information model, respectively, and then compare the obtained prices with the real prices. Results show that all the three models can reflect the trend of bond prices, but the incomplete information model fits the real prices best. In addition, the default probability obtained from the incomplete information model can discriminate the credit quality of listed companies.

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