Reputations and Credit Ratings - Evidence from Commercial Mortgage-Backed Securities

2017 ◽  
Author(s):  
Ramin Baghai ◽  
Bo Becker
Keyword(s):  
Credit Ratings ◽  
Mortgage Backed Securities

scholarly journals Competition and Strategic Incentives in the Market for Credit Ratings: Empirics of the Financial Crisis of 2007

2019 ◽  
Vol 109 (10) ◽  
pp. 3514-3555 ◽  
Author(s):  
Chenghuan Sean Chu ◽  
Marc Rysman
Keyword(s):  
Financial Crisis ◽  
Structural Model ◽  
Credit Ratings ◽  
Mortgage Backed Securities ◽  
Strategic Incentives

We study the market for ratings agencies in the commercial mortgage backed securities sector leading up to and including the financial crisis of 2007–2008. Using a structural model adapted from the auctions literature, we characterize the incentives of ratings agencies to distort ratings in favor of issuers. We find an important equilibrium distortion, which decreased after the crisis. We study several counterfactual experiments motivated by recent policymaking in this industry. (JEL D44, G01, G24)

scholarly journals Credit Ratings and the Evolution of the Mortgage-Backed Securities Market

2011 ◽  
Vol 101 (3) ◽  
pp. 131-135 ◽  
Author(s):  
Jie He ◽  
Jun Qian ◽  
Philip E Strahan
Keyword(s):  
Credit Ratings ◽  
Securities Market ◽  
Rating Agencies ◽  
Mortgage Backed Securities ◽  
And Performance

We compare the structure and performance of private (non-GSE) mortgage-backed securities sold by large issuers vs. those sold by small issuers over the period 2000–2006. Securities sold by large issuers have less subordination—a greater fraction of the deal receiving AAA rating—than those sold by small issuers. Prices for AAA-rated and non-AAA rated tranches sold by large issuers fell more when the market turned down than those sold by small issuers, and this difference was concentrated among tranches issued between 2004 and 2006. These results suggest that rating agencies grant favorable ratings to large issuers, especially during market booms.

Rethinking the Use of Credit Ratings in Capital Regulations: Evidence From the Insurance Industry

2020 ◽  
Author(s):  
Kathleen Weiss Hanley ◽  
Stanislava Nikolova
Keyword(s):  
Insurance Industry ◽  
Credit Ratings ◽  
Third Party ◽  
External Financing ◽  
Regulatory Capital ◽  
Mortgage Backed Securities ◽  
Alternative Methodology ◽  
Editorial Decision ◽  
Capital Regulations ◽  
Credit Losses

Abstract We analyze an initiative by insurance regulators to reform capital regulations for mortgage-backed securities (MBS) by replacing credit ratings with third-party estimates of expected credit losses and by considering an insurer’s exposure to future losses when determining regulatory capital. After implementation, insurers are less likely to sell distressed MBS, gains trade corporate bonds, and/or raise external financing. However, the new regime allows insurers to purchase more low-rated MBS at significant capital savings and insurers with greater capital savings are more likely to do so. Our analysis highlights the potential costs and benefits of an alternative methodology for determining regulatory capital. JEL (G11, G18, G22, G28, G32, G38). Received May 6, 2019; editorial decision April 4, 2020 by Editor Andrew Ellul.

Credit Ratings and Security Prices in the Subprime MBS Market

2011 ◽  
Vol 101 (3) ◽  
pp. 115-119 ◽  
Author(s):  
Adam Ashcraft ◽  
Paul Goldsmith-Pinkham ◽  
Peter Hull ◽  
James Vickery
Keyword(s):  
Financial Crisis ◽  
Predictive Power ◽  
Credit Ratings ◽  
Preliminary Evidence ◽  
Informational Content ◽  
Security Prices ◽  
Subprime Mortgage ◽  
Ongoing Research ◽  
Mortgage Backed Securities ◽  
Recent Financial Crisis

We present and discuss preliminary evidence suggesting that credit ratings significantly influenced prices for subprime mortgage-backed securities issued in the period leading up to the recent financial crisis. Ratings are closely correlated with prices even controlling for a rich set of security- and loan-level controls. This incremental variation in ratings has much less predictive power for security defaults, however, based on findings to date from our ongoing research, suggesting prices were excessively sensitive to ratings relative to their informational content.

scholarly journals DETERMINANTS OF COMMERCIAL MORTGAGE‐BACKED SECURITIES CREDIT RATINGS: AUSTRALIAN EVIDENCE

2008 ◽  
Vol 12 (2) ◽  
pp. 69-94 ◽  
Author(s):  
Bwembya Chikolwa ◽  
Felix Chan
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Credit Rating ◽  
Credit Ratings ◽  
Ordinal Regression ◽  
Alternative Methods ◽  
Rating Agencies ◽  
Standard And Poor's ◽  
Mortgage Backed Securities ◽  
Artificial Neural

Using artificial neural networks (ANN) and ordinal regression (OR) as alternative methods to predict Commercial Mortgage‐backed Securities (CMBS) credit ratings, we examine the role that various financial and industry‐based variables have on CMBS credit ratings issued by Standard and Poor's from 1999–2005. Our OR results show that rating agencies use only a subset of variables they describe or indicate as important to CMBS credit rating as some of the variables they use were statistically insignificant. Overall, ANN show superior results to OR in predicting CMBS credit ratings. Santrauka Sisteminant komercine hipoteka užtikrintų vertybinių popierių prekybos sandorius, svarbiausias tikslas – gauti aukštą kredito reitingą, nes tai daro poveikį pelningumui ir emitento sėkmei. Kredito reitingų agentūros teigia, kad jų vertinimai išreiškia kiekvienos agentūros nuomonę apie potencialią emitento nemokumo riziką ir daugiausia remiasi emitento gebėjimo bei noro grąžinti savo skolą analize, kurią atlieka komitetas, taigi tyrinėtojams jų reitingų kiekybiškai replikuoti nepavyktų. Tačiau tyrinėtojai replikavo obligacijų reitingus, remdamiesi prielaida, kad finansiniai koefi cientai turi daug informacijos apie įmonės kredito riziką. Prognozuodami komercine hipoteka užtikrintų vertybinių popierių reitingus, kaip alternatyvius metodus naudojame dirbtinius neuroninius tinklus ir ranginę regresiją. Ranginės regresijos rezultatai rodo, kad reitingų agentūros naudoja tik tą kintamųjų poaibį, kuriuos jos apibūdina arba nurodo kaip svarbius komercine hipoteka užtikrintų vertybinių popierių reitingui, nes kai kurie iš naudojamų kintamųjų statistiškai nereikšmingi. Apskritai dirbtinių neuroninių tinklų rezultatai, prognozuojant komercine hipoteka užtikrintų vertybinių popierių reitingus, geresni nei ranginės regresijos.

Sign in / Sign up

Export Citation Format

Share Document