Pricing Synthetic CDOs Using a Three Regime Random-Factor-Loading Model

This paper uses a unique data set of more than 1000 synthetic Collateralized Debt Obligations (CDOs) deals to describe typical structures, their pricing and performance with the aim of identifying the factors behind the spectacular collapse of this important segment of structured credit market in late 2008. The data suggests that mark-to-market losses on many synthetic CDO tranches were much more significant than in case of simpler, lower-rated products despite the former experiencing little or no impairment of the notional. The losses were driven instead by the concentration of relatively limited number of defaults in a short period of time, suggesting that pre-crisis pricing must have seriously underestimated such risk of default clustering. In view of the post-crisis pick-up in synthetic CDO issuance, the paper attempts to heed this lesson and offer a simple factor model of default correlation in the spirit of Marshall–Olkin that is naturally suited to capturing the temporal dimension of default dependencies that have been crucial for synthetic CDOs investors. The model allows building a rich dependence structure capable of consistently fitting standardized iTraxx and CDX index tranches, which makes it ideal for pricing bespoke CDOs.

The objective of this paper is to help a bank originator of a collateralized debt obligation (CDO) to build a maximally profitable CDO. We consider an optimization framework for structuring CDOs. The objective is to select attachment/ detachment points and underlying instruments in the CDO pool. In addition to “standard” CDOs we study so-called “step-up” CDOs. In a standard CDO contract the attachment/detachment points are constant over the life of a CDO. In a step-up CDO the attachment/detachment points may change over time. We show that step-up CDOs can save about 25–35% of tranche spread payments (ie, profitability of CDOs can be boosted by about 25–35%). Several optimization models are developed from the bank originator perspective. We consider a synthetic CDO where the goal is to minimize payments for the credit risk protection (premium leg), while maintaining a specific credit rating (assuring the credit spread) of each tranche and maintaining the total incoming credit default swap spread payments. The case study is based on the time-to-default scenarios for obligors (instruments) generated by the Standard & Poor’s CDO Evaluator. The Portfolio Safeguard package by AORDA was used to optimize the performance of several CDOs based on example data.

Since June 2007, the creditworthiness of structured finance products has deteriorated rapidly. The number of downgrades in November 2007 alone exceeded 2,000 and many downgrades were severe, with 500 tranches downgraded more than 10 notches. Massive downgrades continued in 2008. More than 11,000 of the downgrades affected securities that were rated AAA. This paper studies the credit rating crisis of 2007-2008 and in particular describes the collapse of the credit ratings of ABS CDOs. Using data on ABS CDOs we provide suggestive evidence that ratings shopping may have played a role in the current crisis. We find that tranches rated solely by one agency, and by S&P in particular, were more likely to be downgraded by January 2008. Further, tranches rated solely by one agency are more likely to suffer more severe downgrades.(This abstract was borrowed from another version of this item.)

Will the Leveraged Loan Market Trigger a Financial Pandemic? Understanding Cov-Lite Loans, CLOs and EBITDA Add-Backs

Chapter 2 - The Goldman Sachs Swaps Shop: An Examination of Synthetic Short Selling through Credit Default Swaps and Implications of Securities and Exchange Commission v. Goldman Sachs & Co., et al.

Collateralized debt obligations (CDOs) were issued in 1987 by bankers at Drexel Burnham Lambert Inc. A decade later, CDOs became the leading power on the credit derivative markets, on which the value of derivative assets was derived from the value of other assets. However, unlike options and credit swamps, CDOs are not real, which means that they are constructed, and sometimes even the construction of their construction. CDOs were made to satisfy different types of investors, at one end, there is low-risk with low-income, and at the other, high-risk with high-income. By 2007, following the bubble burst on the US real estate market, losses on the CDO market started to expand. By 2008, the crisis on the CDO market turned into what we call today 'the global financial crisis.' CDOs are 'in the heart' of the crisis, and even wider. Our attempt is to reveal the mechanism of collateralized debt obligations (CDOs) and the way in which CDOs expanded the negative effects of the present global financial crisis.

This paper introduced an analytical solution and improved one-factor Gaussian copula models to the pricing of tranches of a Collateralized debt obligations (CDO) portfolio. Prices of CDO tranches are calculated and compared using the analytical model and different one-factor Gaussian copula models including a two-category heterogeneous model and a completely heterogeneous model that uses individual rate parameter and correlation coefficient for each reference entity in a CDO portfolio. When correlation among reference entities is low, the price calculated from the analytical model matches very well with the one-factor Gaussian copula models. However, as the correlation among reference entities increases, prices calculated using both the analytical solution and the homogeneous or two-category one-factor Gaussian copula models significantly deviate from the completely heterogeneous one-factor Gaussian copula model. This result verifies our belief that uniform parameters cannot completely capture all the heterogeneities in a CDO portfolio. Completely heterogeneous one-factor Gaussian copula model using individual rate parameters and correlation coefficients for each reference entities provides more reliable and accurate prices for structured securities.

Hedging of credit derivatives, especially the Collateralized Debt Obligations (CDOs), is the prerequisite of risk management in financial market. Since both spread risk and default risk exist, the models in existing literature resort to the incomplete-market theory to derive the hedging strategies. From another point of view, the construction of hedging strategies of CDO might be regarded as the process of forecasting the changes in value of CDO by the changes in value of hedging instruments. Based on this idea, this paper proposes an alternative hedging approach via the combined forecasting and regression techniques, where the two individual forecasting models are Gaussian copula model and local intensity model, used to hedge against spread risk and default risk, respectively. Finally, the dynamic hedge ratios of CDO tranches with CDS index are derived. A numerical analysis is carried out and the hedge ratios obtained by the new models are compared with those from actual market spreads. It is shown that the model derived in this paper not only provides hedging strategies which agree with the market hedge ratios but that can be effectively implemented as well.

The goal of this paper is to develop the dynamic alpha (α)-stable method for collateralized debt obligation (CDO) pricing based on the α-stable distributions, which will resolve the two issues caused by using traditional static factor copula method in the practice, which means when pricing CDOs, the traditional static factor Copula method does not only exhibit the correlation smile phenomenon which is not inconsistent with the model's assumption, but also cannot be used in pricing CDOs or credit portfolio derivatives for the underlying portfolio with different maturities. As the applications, we present calibration and empirical numerical results for iTraxx Europe Tranches quotes from the market data on March 30, 2007. Thus, our new method under the framework of the dynamic α-stable model is the way for CDO pricing in the practice, and should be useful for the risk management in the practice too.

Depending on the circumstances, U.S. equity investments in collateralized debt obligations (CDOs) may be governed by the Passive Foreign Investment Company (PFIC) rules, the Controlled Foreign Company (CFC) rules, or both. The PFIC and CFC tax regimes are exceptionally nuanced and, consequently, proper tax planning depends on a careful examination of the individual circumstances of the investor and analysis of the risks of the investment. This article focuses on tax aspects of equity investments in CDOs by taxable U.S. persons and the strategies that may effectively be used to maximize certain U.S. tax benefits. The discussion focuses on the characteristics of a typical offshore CDO structure, on advantages that U.S. investors in CDO equity may obtain by making a Qualified Electing Fund election (QEF election), the circumstances in which a QEF election is not advisable, and the interplay of the PFIC and CFC rules.

Chapter 5 - Stock Markets, Derivatives Markets, and Foreign Exchange Markets

ABSTRACTAnalyzing 916 collateralized debt obligations (CDOs), we find that a top credit rating agency frequently made positive adjustments beyond its main model that amounted to increasingly larger AAA tranche sizes. These adjustments are difficult to explain by likely determinants, but exhibit a clear pattern: CDOs with smaller model‐implied AAA sizes receive larger adjustments. CDOs with larger adjustments experience more severe subsequent downgrading. Additionally, prior to April 2007, 91.2% of AAA‐rated CDOs only comply with the credit rating agency's own AA default rate standard. Accounting for adjustments and the criterion deviation indicates that on average AAA tranches were structured to BBB support levels.

2 - The credit derivatives market

The three drivers in modeling CDOs (collateralized debt obligations) are the probability of default, the recovery rate, and correlation of the underlying pool of credits. In general, the probability of default is by far the most relevant factor and correlation, the least. Unfortunately, an unwarranted amount of attention is currently given to correlation. Worse yet, most models are driven by (or based on) asset correlation assumptions when what is really relevant is the default correlation. There is no assurance that controlling the asset correlation can produce the desired default correlation.

The main objective of this study is to analyze the interrelationship between financial distress, earnings management, and audit quality of listed firms in Ghana. The study sampled 16 non-financial firms listed on the Ghana Stock Exchange from 2010 to 2022. Secondary data source was utilized in the study which were the annual reports of these listed firms. The research employed a quantitative approach, using random effect regression. The study found that financial distress had a statistically significant positive relationship with earnings management in Ghanaian firms. Audit quality exhibited a significant negative relationship with earnings management, indicating that higher audit quality can lead to lower earnings management. It was also found that audit quality did not moderate the relationship between financial distress and earnings management. To mitigate the inclination toward earnings management in times of financial distress, firms should strengthen their internal control systems and corporate governance practices. This could involve establishing stricter oversight mechanisms, such as forming an independent audit committee that regularly reviews financial reporting processes and outcomes. This study is the first study to analyze the interrelationship between financial distress, earnings management, and audit quality of listed firms in Ghana.