The credit risk–return puzzle: Impact of credit rating announcements in Australia and Japan

2015 ◽  
Vol 35 ◽  
pp. 37-55 ◽  
Author(s):  
Emawtee Bissoondoyal-Bheenick ◽  
Robert Brooks
Keyword(s):  
Credit Risk ◽  
Credit Rating ◽  
Risk Return

scholarly journals Credit risk return puzzle

2019 ◽  
Author(s):  
Faurani Santi Singagerda
Keyword(s):  
Credit Risk ◽  
Stock Price ◽  
Stock Return ◽  
Credit Rating ◽  
Credit Ratings ◽  
Negative Relationship ◽  
Risk Return ◽  
The Difference ◽  
Rating Changes

Credit risk and stock return with the utilization of credit ratings from Moody’s to represent credit risk in Indonesia, China, Japan, and Singapore. Panel data from January 2001 to December 2015.Credit Risk-Return Puzzle exists in both developed and developing the market for long-term credit ratings, proven by the negative relationship between stock return and credit ratings. On the other hand Credit, Risk-Return Puzzle does not exist in the case of credit rating changes in terms of direction but does show some signs of existence through the difference in magnitude, as different reasons underlying credit rating changes such as leverage changes can change the direction of stock price movement.

scholarly journals Credit Risk-Return Puzzle: Asian Countries Representative

2017 ◽  
Vol 1 (1) ◽  
pp. 26
Author(s):  
Faurani Santi Singagerda ◽  
Tulus Suryanto ◽  
Jesscia Christina Sudjana
Keyword(s):  
Credit Risk ◽  
Stock Price ◽  
Stock Return ◽  
Credit Rating ◽  
Credit Ratings ◽  
Negative Relationship ◽  
Risk Return ◽  
Rating Changes ◽  
Time Invariant

<p>This  research  investigates  the  presence  of  Credit  Risk-Return  Puzzle  on  Indonesia,  China, Japan and Singapore,  by analyzing  the relationship  between  credit risk and stock return with the utilization of credit ratings from Moody’s to represent credit risk. The data comprises of monthly data from January 2001 to December 2015, compiled in an unbalanced panel and then regressed with the Hausman-Taylor  Estimator  due to the presence  of time-invariant  variables  such as countries  and country classifications within the dataset.</p><p>The results from this research show that Credit Risk-Return Puzzle exists in both developed and  developing  market for long-term  credit ratings, proven by the negative  relationship  between  stock return and credit ratings. On the other hand Credit Risk-Return Puzzle does not exist in the case of credit rating changes in terms of direction but do show some signs of existence through difference in magnitude,  as  different  reasons  underlying  credit  rating  changes  such  as  leverage  changes  can change the direction of stock price movement.</p><p> </p><p><strong>K</strong><strong>ey</strong><strong>w</strong><strong>ords</strong><strong>: </strong>credit risk-return puzzle, credit rating announcements, credit risk, impact of rating changes, decoupling-recoupling hypothesis</p>

Bank Credit Risk Analysis and Bank Credit Rating

2020 ◽  
pp. 275-348
Author(s):  
Terence M. Yhip ◽  
Bijan M. D. Alagheband
Keyword(s):  
Risk Analysis ◽  
Credit Risk ◽  
Credit Rating ◽  
Bank Credit ◽  
Credit Risk Analysis

scholarly journals The assessment of companies for external and internal purposes

2012 ◽  
Vol 50 (No. 3) ◽  
pp. 105-109
Author(s):  
H. Sůvová
Keyword(s):  
Risk Assessment ◽  
Credit Risk ◽  
Balanced Scorecard ◽  
Credit Rating ◽  
Single Track ◽  
Good Tool ◽  
The Third ◽  
Financial Perspective ◽  
Quality Award ◽  
Holistic Assessment

This article presents holistic concepts of companies&rsquo; assessments intended for two basic groups of users: internal and external. Companies&rsquo; assessments concentrated only on financial perspective are very single-track and already obsolete and therefore, further perspectives are used to complete companies&rsquo; assessments. Among concepts intended for internal assessments, the so-called balanced scorecard approach has developed since late nineties. This concept helps in company&rsquo;s strategic management. Moreover, there is a&nbsp;concept of EFQM Excellence model introduced at the beginning of nineties for assessing applications for the European Quality Award, but has become widely used for company assessment and management. The third mentioned concept is intended for credit risk assessment is credit rating. The development of methodology of the holistic assessment of Czech farm businesses may be a&nbsp;good tool for different external and internal users.

Part II The Financial Crisis of 2007–2011, 6 Market and Regulatory Failure

2018 ◽  
Author(s):  
Mccormick Roger ◽  
Stears Chris
Keyword(s):  
Risk Management ◽  
Financial Crisis ◽  
Credit Risk ◽  
Financial Market ◽  
Credit Rating ◽  
Third Party ◽  
Credit Rating Agencies ◽  
Rating Agencies ◽  
Market Participants ◽  
Regulatory Failure

This chapter first discusses the origins of the financial crisis, highlighting practice of ‘packaging and selling’ credit risk by financial market participants that led up to the crisis. It argues that although, in retrospect, many aspects of that practice look very bad indeed, the idea that banks might originate a credit exposure and then transfer the credit risk attached to it to a third party was, before the financial crisis, considered to be part and parcel of sound risk management. The discussion then turns to credit-rating agencies. Analysis of the financial crisis and ‘what went wrong’ has shown that rating agencies were too generous with their rating of many of the structured products that contributed to the collapse.

scholarly journals INFLUENCE OF GOVERNMENT CREDIT RISK ON PPP PROJECTS IN OPERATION STAGE

2021 ◽  
Vol 25 (3) ◽  
pp. 216-227
Author(s):  
Yining Zhou ◽  
Jicai Liu
Keyword(s):  
Credit Risk ◽  
Credit Rating ◽  
Negative Impact ◽  
Optimal Choice ◽  
Default Probability ◽  
Effort Level ◽  
Contractual Relationship ◽  
Allocation Ratio ◽  
Government Credit ◽  
The Government

In PPP projects, insufficient risk management may lead to the breakdown of partnerships and even project failures. Among them, the government credit risk is regarded as unbearable risk and a key risk affecting PPP projects because of its high frequency and impact. Therefore, based on the contractual relationship between both sides, a principal-agent model for the optimal choice of investors and the government under the government default probability is constructed. This paper explored the quantity relationship of the government credit risk and the project utility through analysing the effect of government default probability perceived by both parties on the investor’s optimal effort level and government allocation ratio. The results demonstrate that the government credit risk will decrease the effort level of investors and have a negative impact on the utility of the project. Furthermore, the government’s modification of the contract allocation ratio based on its own credit rating can offset the negative impact of its credit risk on the effectiveness of the project. But this regulatory effect is limited. The findings effectively provide some insights and theoretical basis for solving the negative effects of government credit risk.

scholarly journals Empirical evolution of credit risk over a decade in IBEX. 35 companies and its relationship with the qualification of its ratings

2020 ◽  
Vol 16 (2) ◽  
pp. 61
Author(s):  
Josep Patau
Keyword(s):  
Credit Risk ◽  
Credit Rating ◽  
Clear Correlation ◽  
Crisis Period ◽  
Rating Agencies ◽  
Credit Rating Agency ◽  
Rating Agency ◽  
Determining Factors ◽  
Commercial Loans ◽  
The One

Object: The present work responds to two objectives. On the one hand, it describes the evolution of the main economic-financial indicators that influence credit risk (insolvency) for a sample of 10 Spanish companies listed on the IBEX 35. This analysis is studied for a comparative period of 10 years, which coincides with a pre-crisis stage (2002-2005) and an economic post-crisis phase (2012-2015). On the other hand, it corroborates the relationship between the analysed insolvency and the rating or credit-risk rating published for these companies by an internationally recognized credit rating agency, Standard & Poor's (S & P).Design / methodology: A sample of 10 companies and a 10-year period including the years 2002-2005 (pre-crisis) and the years 2012-2015 (post-crisis) are chosen, omitting the Spanish economic crisis that occurred in the year 2008. For the study of its evolution, 6 ratios obtained from the scientific literature that relate to credit risk and its effects on investments and company results are calculated. Finally, the correlations of these variables with the ratings of credit risk assessment by the rating agency S & P are measured. Descriptive statistics will assign value and graphics to this ten-year evolution, and with the incorporation of a factorial analysis, the correlation between the ratios and the S & P rating will be determined. The statistical analysis explains this correlation to a greater extent.Contributions / results: The results show a clear increase in the value of the impairment variable due to credit risk ten years later that directly affects the results of the companies, despite these companies having significantly reduced their investments in commercial loans pending collection and drastically reduced the period means of collection of clients. In turn, there is a clear correlation between the insolvency studied and the variables used by the S & P rating agency for the assessment of credit risk.Added value / conclusions: The empirical study concludes that there is a correspondence between insolvency and the rating given by an internationally prestigious rating agency (S & P) for the sample of 10 companies studied. Three variables – customer balance-accounts receivable, investments and the net amount of turnover – are determining factors explaining this correlation, and these three variables are the same ones that decisively influence both the pre-crisis period and the post-crisis period 10 years apart. The rating agencies weigh the insolvency variable in their analyses.

scholarly journals Dynamic Mapping of Probability of Default and Credit Ratings of Russian Banks

2020 ◽  
Vol 14 (4) ◽  
pp. 31-46
Author(s):  
Ella Khromova
Keyword(s):  
Credit Risk ◽  
Credit Rating ◽  
Credit Ratings ◽  
Quantitative Measure ◽  
Optimal Time ◽  
Investment Horizon ◽  
Investment Strategies ◽  
Probability Of Default ◽  
Rating Agencies ◽  
Dynamic Mapping

Investors are interested in a quantitative measure of banks’ credit risk. This paper maps the credit ratings of Russian banks to default probabilities for different time horizons by constructing an empirical dynamic calibration scale. As such, we construct a dynamic scale of credit risk calibration to the probability of default (PD).Our study is based on a random sample of 395 Russian banks (86 of which defaulted) for the period of 2007-2017. The scale proposed by this paper has three features which distinguish it from existing scales: dynamic nature (quarterly probability of default estimates), compatibility with all rating agencies (base scale credit ratings), and a focus on Russian banks.Our results indicate that banks with high ratings are more stable just after the rating assignment, while a speculative bank’s probability of default decreases over time. Hence, we conclude that investors should account for not only the current rating grade of a bank, but also how long ago it was assigned. As a result, a rising capital strategy was formulated: the better a bank’s credit rating, the shorter the investment horizon should be and the closer the date of investment should be to the rating assignment date in order to minimise credit risk.The scientific novelty of this paper arises from the process of calibration of a rating grade to dynamic PD in order to evaluate the optimal time horizon of investments into a bank in each rating class. In practical terms, investors may use this scale not only to obtain a desired credit rating, but also to identify quantitative measure of credit risk, which will help to plan investment strategies and to calculate expected losses.

scholarly journals Bank Credit Risk Rating Process: Is There a Change With the 2007-09 Crisis?

2021 ◽  
Vol 12 (5) ◽  
pp. 41
Author(s):  
Emna Damak
Keyword(s):  
Credit Risk ◽  
Credit Rating ◽  
Lessons Learned ◽  
Bank Credit ◽  
Rating Agencies ◽  
Risk Rating ◽  
Rating Process ◽  
Before And After ◽  
Relevant Variables ◽  
Rating Changes

The purpose of this article is to study empirically the bank credit risk rating (BCRR) process over time using 89 banks from 27 EMENA countries rated by S&P’s simultaneously before and after 2007-09 crises. We made this comparison based on the CAMELS model with a proposed ‘S’ to BCRR. We use "ordered logit" regression for the rating classes and we complete our analysis by “linear multiple” regression for the rating grades. The results show that the rating changes in 2012 are mainly a methodology revision consequence of the entire rating process changes, including the weight of components, the important factors and the relevant variables in order to take into account some of the lessons learned from this global crisis. They also show a consistence between the BCRR's revealed and practiced methodologies revised by the credit rating agencies (CRAs).

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