Ten Years of Evidence: Was Fraud a Force in the Financial Crisis?

2021 ◽  
Vol 59 (4) ◽  
pp. 1293-1321
Author(s):  
John M. Griffin

This article synthesizes the large literature regarding the role of various players in residential mortgage-backed securities (RMBS) securitization at the center of the 2008–09 US housing and financial crisis. Underwriting banks facilitated wide-scale mortgage fraud by knowingly misreporting key loan characteristics underlying mortgage-backed securities (MBS). Under the cover of complexity, credit rating agencies catered to investment banks by issuing increasingly inflated ratings on both RMBS and collateralized debt obligations (CDOs). Originators who engaged in mortgage fraud gained market share, as did CDO managers who catered to underwriters by accepting the lowest-quality MBS collateral. Appraisal targeting and inflated appraisals were the norm. RMBS and CDO prices indicate that the marginal AAA investor was unaware of pervasive mortgage fraud and ratings inflation, but these factors were strongly related to future deal performance. The supply of fraudulent credit was not uniform, but clustered in certain geographic regions and zip codes. As these dubious originators extended credit to those who could not afford the loans, the credit expansion led to house price booms and subsequent crashes in these zip codes. Overall, a consistent narrative based on substantial research indicates that conflicts of interest, misreporting, and fraud were focal features of the financial crisis. (JEL G01, G21, G28, K42, R30)

2014 ◽  
Vol 18 (5) ◽  
pp. 383-401
Author(s):  
Iain Hardie ◽  
Donald Mackenzie

This article analyses collateralized debt obligations (CDOs), complex securities that were at the heart of the recent financial crisis. The difficulties of analysing these securities are considered, and it is argued that the increasing complexity of CDOs that repackaged mortgage-backed securities outpaced the returns available to investors, and therefore the resources available to pay for the analysis required to value the securities adequately within the timeframe available. CDOs therefore faced the problem of computational intractability. Such an outcome was, the article argues, inevitable in financial innovation that sought to create ever-higher returns from the fixed returns on a pool of assets. CDOs created what the article labels a lemon-squeezing problem. Implications for regulatory responses to the crisis are briefly explored.


2017 ◽  
Vol 14 (2) ◽  
pp. 82-87
Author(s):  
Eleonora Isaia ◽  
Marina Damilano

Reputational concerns should discipline credit rating agencies (CRAs), eliminate any conflicts of interest, and motivate them to provide unbiased ratings. However, the recent financial crisis confirms models of CRAs’ behavior that predict inflated ratings for complex products and during booms. We test whether CRAs suffered a reputational damage for this behavior. We find strong support in the data for our hypothesis. The stock price reaction to rating revisions is significantly lower after the financial crisis, particularly in the financial sector. In multivariate tests, we find that the stock price reaction is lower, on average, in the post-crisis period by 2.3%.


Author(s):  
Şenay Ağca ◽  
Saiyid S. Islam

Securitized debt markets play a vital role in financial markets in risk-sharing and creating alternative financing sources, which provide benefits for both borrower and lenders. This chapter describes the main characteristics of securitized debt and securitized debt instruments. Major securitized debt instruments are mortgage-backed securities (MBSs) including residential mortgage-backed securities (RMBSs) and commercial mortgage-backed securities (CMBSs) as well as asset backed commercial paper (ABCP) and collateralized debt obligations (CDOs). The characteristics of these securities, their associated benefits and uses, and the risk factors that determine the performance of securitized debt instruments are covered. The evolution and size of these securitized markets is also discussed. Overall, the chapter indicates that securitized markets help originators in transferring risks and monetizing illiquid assets and aid investors by providing an efficient mechanism for portfolio diversification and ability to better adjust their investments to their risk preferences.


2015 ◽  
Vol 29 (2) ◽  
pp. 81-106 ◽  
Author(s):  
Robert McDonald ◽  
Anna Paulson

The near-failure on September 16, 2008, of American International Group (AIG) was an iconic moment in the financial crisis. Two large bets on real estate made with funding vulnerable to bank-run-like dynamics pushed AIG to the brink of bankruptcy. AIG used securities lending to transform insurance company assets into residential mortgage-backed securities and collateralized debt obligations, ultimately losing at least $21 billion and threatening the solvency of the life insurance companies. AIG also sold insurance on multisector collateralized debt obligations, backed by real estate assets, ultimately losing more than $30 billion. These activities were apparently motivated by a belief that AIG's real estate bets would not suffer defaults and were “money-good.” We find that these securities have in fact suffered write-downs and that the stark “money-good” claim can be rejected. Ultimately, both liquidity and solvency were issues for AIG.


Author(s):  
Darrell Duffie

Over-the-counter (OTC) markets for derivatives, collateralized debt obligations, and repurchase agreements played a significant role in the global financial crisis. Rather than being traded through a centralized institution such as a stock exchange, OTC trades are negotiated privately between market participants who may be unaware of prices that are currently available elsewhere in the market. In these relatively opaque markets, investors can be in the dark about the most attractive available terms and who might be offering them. This opaqueness exacerbated the financial crisis, as regulators and market participants were unable to quickly assess the risks and pricing of these instruments. This book offers a concise introduction to OTC markets by explaining key conceptual issues and modeling techniques, and by providing readers with a foundation for more advanced subjects in this field. The book covers the basic methods for modeling search and random matching in economies with many agents. It gives an overview of asset pricing in OTC markets with symmetric and asymmetric information, showing how information percolates through these markets as investors encounter each other over time. The book also features appendixes containing methodologies supporting the more theory-oriented of the chapters, making this the most self-contained introduction to OTC markets available.


Author(s):  
Nancy Reichman ◽  
Ophir Sefiha

This article compares the efforts to govern performance enhancement technologies in two seemingly different competitive arenas—financial markets and professional cycling—where the pressures to outperform are enormous. In the two decades prior to the 2007 financial crisis, new derivative financial commodities such as collateralized debt obligations (CDOs) were created to “juice up” investment returns for extraordinary profits. Over roughly the same period, the development and use of blood boosting drugs and technologies brought so-called doping by cyclists to new levels of complexity and sophistication with extraordinary race results and new levels of commercialization of the sport. The efforts to “turbocharge” their respective competitive spaces took place within complicated regimes of self-regulation that had strikingly dissimilar narratives about performance enhancement and, consequently, different technologies for control. Looking across these seemingly disparate cases draws our attention to how regulation fits into the assemblage of competition and prospects for reform.


Author(s):  
Mark H. A. Davis

Credit risk is the risk that your counterparty might default on future obligations. There are a small number of credit rating agencies operating globally that assign a credit rating to each company under consideration. ‘Credit risk’ explains credit risk modelling and analysis, including credit default swaps, multi-asset credit risk, and collateralized debt obligations. Credit risk models are divided into two main categories: ‘structural form’ and ‘reduced form’. A pervasive problem in credit risk modelling is that while some parameters can be backed out by the calibration process, there are usually others about which the available data is insufficient for us to do anything more than take an educated guess.


Economies ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 154
Author(s):  
Lorena Caridad y López del Río ◽  
María de los Baños García-Moreno García ◽  
José Rafael Caro-Barrera ◽  
Manuel Adolfo Pérez-Priego ◽  
Daniel Caridad y López del Río

Long-term ratings of companies are obtained from public data plus some additional nondisclosed information. A model based on data from firms’ public accounts is proposed to directly obtain these ratings, showing fairly close similitude with published results from Credit Rating Agencies. The rating models used to assess the creditworthiness of a firm may involve some possible conflicts of interest, as companies pay for most of the rating process and are, thus, clients of the rating firms. Such loss of faith among investors and criticism toward the rating agencies were especially severe during the financial crisis in 2008. To overcome this issue, several alternatives are addressed; in particular, the focus is on elaborating a rating model for Moody’s long-term companies’ ratings for industrial and retailing firms that could be useful as an external check of published rates. Statistical and artificial intelligence methods are used to obtain direct prediction of awarded rates in these sectors, without aggregating adjacent classes, which is usual in previous literature. This approach achieves an easy-to-replicate methodology for real rating forecasts based only on public available data, without incurring the costs associated with the rating process, while achieving a higher accuracy. With additional sampling information, these models can be extended to other sectors.


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