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http://hdl.handle.net/10603/128473
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DC Field | Value | Language |
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dc.date.accessioned | 2017-01-27T04:42:05Z | - |
dc.date.available | 2017-01-27T04:42:05Z | - |
dc.identifier.uri | http://hdl.handle.net/10603/128473 | - |
dc.description.abstract | It is difficult to over-emphasize the importance of credit risk. Its use is manifest in the explosive newlinegrowth of sophisticated credit instruments such as Credit Default Swaps (CDS). Its abuse can newlinewreak havoc as we saw in the sub-prime crisis and its aftermath. One of the most important newlineindicators of credit risk is the credit ratings, which are provided by the major rating agencies. newlineThe rating assigned to any debt instrument reflects the assessment by the Credit Rating Agency newlineof the creditworthiness of the issuer. Recent years have seen growing importance of both ratings newlineand the Credit Rating Agencies (CRAs) as the latter serve the entire gamut of regulators, issuers newlineand investors. Some academic work has been done in the Indian context, to investigate, examine newlineand suggest improvements in the process and methodology employed by CRAs. Academicians newlineand practitioners have also proposed several credit risk models to predict default on debt newlineobligations by borrowers. These credit risk models can be broadly classified as structural newline(market-based) models and statistical (reduced-form) models. newlineThis research study has two broad objectives: (1) to analyze the methodologies and practices newlineadopted by the Credit Rating Agencies (CRAs) in India for assigning ratings to corporate debt newlineinstruments; and (2) this study attempts to combine financial variables and the market-based newlinedefault drivers in a hybrid form to predict corporate default for public limited companies in newlineIndia. The proposed model is a blend of structural (market-based) and statistical (reduced-form) newlinemodels. The present research work has been carried out on the manufacturing industries by newlineclassifying companies into seven sectors, based on the Prowess CMIE classification. | - |
dc.language | English | - |
dc.rights | university | - |
dc.title | Corporate debt ratings an analysis of methodologies and practices by select credit rating agencies in India | - |
dc.creator.researcher | Vandana Gupta | - |
dc.contributor.guide | Mittal R K and Bhalla V K | - |
dc.publisher.place | Delhi | - |
dc.publisher.university | Guru Gobind Singh Indraprastha University | - |
dc.publisher.institution | University School of Management Studies | - |
dc.date.registered | n.d. | - |
dc.date.completed | 2014 | - |
dc.date.awarded | n.d. | - |
dc.format.accompanyingmaterial | CD | - |
dc.source.university | University | - |
dc.type.degree | Ph.D. | - |
Appears in Departments: | University School of Management Studies |
Files in This Item:
File | Description | Size | Format | |
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01_coverpage.pdf | Attached File | 13.35 kB | Adobe PDF | View/Open |
02_certificate.pdf | 38.38 kB | Adobe PDF | View/Open | |
03_acknowledgement.pdf | 39.34 kB | Adobe PDF | View/Open | |
04_abstract.pdf | 36.43 kB | Adobe PDF | View/Open | |
05_toc.pdf | 38.75 kB | Adobe PDF | View/Open | |
06_figures.pdf | 40.03 kB | Adobe PDF | View/Open | |
07_tables.pdf | 46.6 kB | Adobe PDF | View/Open | |
08_list of appendices.pdf | 24.01 kB | Adobe PDF | View/Open | |
09_abbreviation.pdf | 41.69 kB | Adobe PDF | View/Open | |
10_chapter_01.pdf | 126.22 kB | Adobe PDF | View/Open | |
11_chapter_02.pdf | 250.8 kB | Adobe PDF | View/Open | |
12_chapter_03.pdf | 311.33 kB | Adobe PDF | View/Open | |
13_chapter_04.pdf | 450.86 kB | Adobe PDF | View/Open | |
14_chapter_05.pdf | 1.57 MB | Adobe PDF | View/Open | |
15_chapter_06.pdf | 125.09 kB | Adobe PDF | View/Open | |
16_references.pdf | 64.47 kB | Adobe PDF | View/Open | |
17_appendices.pdf | 205.36 kB | Adobe PDF | View/Open | |
18_resume.pdf | 15.27 kB | Adobe PDF | View/Open |
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