Collateralized Debt Obligations Research Articles

Securitization Enhancing Liquidity in Kenya

Purpose: This paper aims to explain the benefits of securitization in Kenya’s economy that is to both the government, the private sector, and the Kenya capital market. The paper provides a road map on how the government and private sector can diversify their funding sources, lower borrowing costs, improve liquidity, and transfer risk. It will help offer clear policy direction to government policymakers in efforts geared towards the introduction of asset securitization in the country which will increase liquidity, economic growth, and expansion of capital in the country. The regulator of the financial market Capital Market Authority (CMA) will therefore formulate guidelines and regulatory requirements that can enhance the performance of the securitization industry. The paper will also be useful to corporate strategists of companies as they seek to optimize return considerations in driving shareholder wealth margins. The paper is expected to motivate and be a key reference for future research work in the securitization industry in Kenya. Securitization is one of the innovative products that may be used by government and private firms and investors to increase wealth.
 Methodology: The study adopted a desktop methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library
 Findings: Securitization will bring benefits in terms of growth in the economy and expansion of capital markets and hence wealth creation. Securitization involves the conversion of a pool of assets with a regular and predictable cash income such as mortgage repayments receivables, and credit card receivables e,t,c into a security or marketable instrument that allows the institution to transact a large number of its assets, which would otherwise not be attractive as individual. The originator who will be the financial institution or the government then creates a legal entity known as a Special Purpose Vehicle (SPV). The special purpose vehicle may be in the form of a Limited Liability Company, a trust, a partnership, or even a subsidiary of the Originator. These selected receivables are then transferred to the special purpose vehicle which then becomes the owner of these receivables. The securities to be issued by the special purpose vehicle are usually rated by a rating agency. This is due to the fact that such instruments are unsecured, which will require that investors are protected.
 Unique Contribution to Theory, Practice and Policy: Profit maximising theory and theory of innovation can be used to anchor future studies on securitization enhancing liquidity. If the Government or the financial institution wants to issue 'AAA' rated asset-backed securities or mortgage-backed securities, collateralized mortgage securities, or collateralized debt obligations (CDOs)then they must choose first-class loans not prone to prepayment and default risk where originators conduct their own rating, they select all the best asset bundles for investment. In order to get a favorable rating, the special purpose vehicles can provide credit enhancement to the securities.

Binomial Approximation to Locally Dependent Collateralized Debt Obligations

Binomial Approximation to Locally Dependent Collateralized Debt Obligations

Buffered and Reduced Multidimensional Distribution Functions and Their Application in Optimization

AbstractFor a random variable, superdistribution has emerged as a valuable probability concept. Similar to cumulative distribution function (CDF), it uniquely defines the random variable and can be evaluated with a simple one-dimensional minimization formula. This work leverages the structure of that formula to introduce buffered CDF (bCDF) and reduced CDF (rCDF) for random vectors. bCDF and rCDF are shown to be the minimal Schur-convex upper bound and the maximal Schur-concave lower bound of the multivariate CDF, respectively. Special structure of bCDF and rCDF is used to construct an algorithm for solving optimization problems with bCDF and rCDF in objective or constraints. The efficiency of the algorithm is demonstrated in a case study on optimization of a collateralized debt obligation with bCDF functions in constraints.

Poisson approximation for the expectation of call function with application in collateralized debt obligation

Let W be a sum of Bernoulli random variables which satisfies local dependence and hz be a call function. This function has many applications in finance. In this article, we give bounds of Poisson approximation for by using Stein-Chen’s method. Our results improve the previous results. Finally, we apply these results to approximate the mean of percentage loss for each tranche in the collateralized debt obligation (CDO) tranche pricing.

COMMENTARY: The Great Financial Crisis as Seen by a Long-Time Veteran and Participant

This article reflects on the author’s experience working in the mortgage-backed securities (MBS) sector during the 70s, 80s, and 90s on Wall Street, and the evolution of the market that eventually led to the 2007 mortgage meltdown and the 2008 financial crisis. MBS emerged as a promising type of investment, offering high returns and lower risks than products from other fixed-income sectors. However, the gradual erosion of “the mortgage gets paid first” assumption was somehow overlooked during the boom years, as demand increased, underwriting guidelines got looser, and new affordability products emerged. Meanwhile, credit managers and MBS experts combined to become deal managers, leading to a whole new sector of the markets, collateralized debt obligations (CDOs). The market for CDOs grew, and new buyers of subordinate MBS tranches contributed to a rapid expansion in the issuance of MBS backed by subprime and alt-A mortgage loans. The whole episode raises important questions about the responsibility of market participants and the role of regulations in the evolution of the MBS market.

Finance, Fiction, and Loss

Finance, Fiction, and Loss

Credit Ratings of Collateralized Debt Obligations in Microfinance

Credit Ratings of Collateralized Debt Obligations in Microfinance

Subprime Crisis: Cause, Effect and Response

After the dot-com bust, the U.S. experienced growth in the housing industry: oversupply of capital, low-interest rates, and deregulated financial system. Financial firms, led by banks, rode on a robust macroeconomic atmosphere. The freedom brought by financial deregulation was taken out of context among the firms in the financial system, resulting in the establishment and widespread use of collateralized debt obligations (CDOs) and mortgage-backed securities (MBS) (CDO); these were unconventional investments for the general populace. In retrospect, the U.S. government limited banks’ participation in private-labeled asset-backed securities to tame interest rates, liquidity, and credit risks only to be loosened the following years by Congress through amendments to the existing laws. Deregulation of the banking system encouraged banks and thrift banks to engage in real estate businesses, which paved the way for the rise of the subprime market. The subprime market grew alongside the prime market due to banks’ circumvention of borrowers' creditworthiness. The domino effect began from the failure of the subprime borrowers to repay. Vertically integrated banking giants felt the heavy blow of loan default, which caused their insolvency. Since then, the whole financial system has collapsed entirely. An increase in job losses, a rise in homelessness, business closures, and U.S. negative growth ensued. Through the TARP program, the Dodd-Frank Wall Street Reform and Consumer Protection Act, and quantitative easing.

Copula sensitivity analysis for portfolio credit derivatives

Modeling dependence among input random variables is often critical for performance evaluation of stochastic systems, and copulas provide one approach to model such dependence. In the financial industry, the dependence among default times of different assets drives the pricing of portfolio credit derivatives, including basket default swaps and collateralized debt obligations. Copula sensitivities provide information about how changes in the dependence level affect the output. We study sensitivity analysis of elliptical copulas and Archimedean copulas using infinitesimal perturbation analysis, and an unbiased estimators is derived using conditional Monte Carlo (CMC) to address discontinuities that arise in portfolio credit derivatives. In simulation experiments, the new estimators have smaller variance than other applicable methods.

The Big Short's Incoherence, Documentary Aesthetics, and Use of Direct Address

<i>The Big Short</i>'s Incoherence, Documentary Aesthetics, and Use of Direct Address

Application of RQMC for CDO Pricing with Stochastic Correlations under Nonhomogeneous Assumptions

In consideration of that the correlation between any two assets of the asset pool is always stochastic in the actual market and that collateralized debt obligation (CDO) pricing models under nonhomogeneous assumptions have no semianalytic solutions, we designed a numerical algorithm based on randomized quasi-Monte Carlo (RQMC) simulation method for CDO pricing with stochastic correlations under nonhomogeneous assumptions and took Gaussian factor copula model as an example to conduct experiments. The simulation results of RQMC and Monte Carlo (MC) method were compared from the perspective of variance changes. The results showed that this numerical algorithm was feasible, efficient, and stable for CDO pricing with stochastic correlation under nonhomogeneous assumptions. This numerical algorithm is expected to be extended to other factor Copula models for CDO pricing with stochastic correlations under nonhomogeneous assumptions.

A General Numerical Algorithm for CDO Pricing Based on Single Factor Copula Framework and Nonhomogeneous Assumptions

In view of the fact that different factor Copula models are only applicable to different practical problems in collateralized debt obligations (CDO) market and that there is no semianalytical solution under nonhomogeneous assumptions to CDO pricing model, we designed a general numerical algorithm which was based on the framework of single factor Copula model and randomized quasi-Monte Carlo (RQMC) simulation method. We took two single factor Copula models as examples to conduct empirical study, in which the simulation results of RQMC and Monte Carlo (MC) simulation method were compared and analyzed based on variance changes. The result showed that the algorithm in this paper was not only applicable to general single factor Copula model but also very stable. So, it was a general and efficient numerical method to solve the problem of CDO pricing under nonhomogeneous assumptions.

The European banks’ role in the financial crisis of 2007-8: a critical assessment

ABSTRACT Since the outbreak of the financial crisis in 2007, opinion has been divided over whether its root cause was weak regulation and bank funding problems or the pressure of investor demand for US safe assets. New research on the European banks’ role in the crisis may finally help to resolve the issue. Far from being peripheral players in the crisis, European banks were deeply implicated in its causal origins as evidenced by their activities in the two US debt markets that were at the heart of the crisis: those for collateralised debt obligations (CDOs) and for asset backed commercial paper (ABCP). These activities would seem to lend weight to the conclusion that regulation and bank funding issues were the key causal variables in the financial crisis. However, it is a conclusion only made possible by ignoring the pre-crisis connection between the federal funds rate and the rate of ABCP demand from the institutional money market mutual funds (MMMFs). This paper argues that when this connection is closely examined, it turns out that the evidence surrounding the European banks’ role in the financial crisis gives greater weight to the safe asset demand explanation of the crisis.

A contagion process with self-exciting jumps in credit risk applications

The modeling of the probability of joint default or total number of defaults among the firms is one of the crucial problems to mitigate the credit risk since the default correlations significantly affect the portfolio loss distribution and hence play a significant role in allocating capital for solvency purposes. In this article, we derive a closed-form expression for the default probability of a single firm and probability of the total number of defaults by time $t$ in a homogeneous portfolio. We use a contagion process to model the arrival of credit events causing the default and develop a framework that allows firms to have resistance against default unlike the standard intensity-based models. We assume the point process driving the credit events is composed of a systematic and an idiosyncratic component, whose intensities are independently specified by a mean-reverting affine jump-diffusion process with self-exciting jumps. The proposed framework is competent of capturing the feedback effect. We further demonstrate how the proposed framework can be used to price synthetic collateralized debt obligation (CDO). Finally, we present the sensitivity analysis to demonstrate the effect of different parameters governing the contagion effect on the spread of tranches and the expected loss of the CDO.

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

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)

Quantum computation for pricing the collateralized debt obligations

Collateralized debt obligation (CDO) has been one of the most commonly used structured financial products and is intensively studied in quantitative finance. By setting the asset pool into different tranches, it effectively works out and redistributes credit risks and returns to meet the risk preferences for different tranche investors. The copula models of various kinds are normally used for pricing CDOs, and the Monte Carlo simulations are required to get their numerical solution. Here we implement two typical CDO models, the single-factor Gaussian copula model and normal inverse Gaussian copula model, and by applying the conditional independence approach, we manage to load each model of distribution in quantum circuits. We then apply quantum amplitude estimation as an alternative to Monte Carlo simulation for CDO pricing. We demonstrate the quantum computation results using IBM Qiskit. Our work addresses a useful task in finance instrument pricing, significantly broadening the application scope for quantum computing in finance.

Commentary: Credit Rating Failures in the Aftermath of the Mortgage Meltdown

Credit ratings from the major rating agencies failed to signal the true risk content of residential mortgage-backed securities (RMBS), collateralized debt obligations (CDOs), and commercial mortgage-backed securities (CMBS) issued from 2005 through 2007. This article compares the failures across rating agencies and asset classes, using data filed by the rating agencies with the Securities and Exchange Commission (SEC). The data confirm the terrible performance of RMBS ratings. The likely causes include a combination of the breakdown of mortgage industry lending practices and the concurrent deterioration of rating agency practices. The data show that CDO ratings perform somewhat better than RMBS ratings. That result is perhaps surprising and likely reflects the confounding effects of the SEC’s definitions of its reporting categories. The data also show bad performance for CMBS ratings although the underlying causes differ from those driving the results for RMBS and CDOs. Moreover, the data show high levels of rating withdrawals across all asset classes, raising the question of possible underreporting of defaults. <b>Key Findings</b> ▪ Rating agency data reported to the SEC confirm the terrible performance of RMBS ratings, with triple-A-rated RMBS displaying 10-year default rates of 30% to 35% as of year-end 2017. The likely causes include a combination of the breakdown of mortgage industry lending and securitization practices and the concurrent deterioration of rating agency practices. ▪ The data show that CDO ratings performed better than RMBS ratings, likely because the data include ratings for many synthetic corporate CDOs. Other studies indicate that performance is significantly worse for CDOs backed by structured finance assets. ▪ CMBS ratings also performed poorly. Although CMBS rated triple-A displayed 10-year default rates of only 2% to 3%, those rated double-A had default rates of 12% to 19% while single-A-rated CMBS default rates were 23% to 32%. ▪ The data document high levels of unexplained rating withdrawals on structured finance securities. The effect of such withdrawals may be to artificially depress the reported default rates.

How financial products organize spatial networks: Analyzing collateralized debt obligations and collateralized loan obligations as “networked products”

During the 2010s, collateralized loan obligations rapidly became a trillion-dollar industry, mirroring the growth profile and peak value of its cousin—collateralized debt obligations—in the 2000s. Yet, despite similarities in product form and growth trajectory, surprisingly little is known about how these markets evolved spatially and relationally. This paper fills that knowledge gap by asking two questions: how did each network adapt to achieve scale at speed across different jurisdictions; and to what extent does the spatial and relational organization of today's collateralized loan obligation structuration network, mirror that of collateralized debt obligations pre-crisis? To answer those questions, we draw on the global financial networks approach, developing our own concept of the networked product to explore the agentic qualities of collateralized debt obligations and collateralized loan obligations—specifically how their technical and regulatory “needs” shape the roles and jurisdictions enrolled in a global financial network. We use social network analysis to map and analyze the evolving spatial and relational organization that nurtured this growth, drawing on data harvested from offering circulars. We find that collateralized debt obligations spread from the US to Europe through a process of transduplication—that similar role-based network relations were reproduced from one regulatory regime to another. We also find a strong correlation between pre-crisis collateralized debt obligation- and post-crisis collateralized loan obligation-global financial networks in both US$- and €-denominations, with often the same network participants involved in each. We conclude by reflecting on the prosaic way financial markets for ostensibly complex products reproduce and the capacity for network stabilities to produce market instabilities.

Dysfunctional Markets: A Spray of Prey Perspective

We revisit the theory of financial crises using a predator-prey metaphor, highlighting the relationship between greed, risk aversion and debt accumulation and aggregating concepts from economics, finance and psychology. We argue that regulations that are implemented inefficiently, with weak enforcement or at the wrong time can have deleterious effects on the market, worsening the ailment they initially intended to correct and leaving a spray of prey in their wake. To illustrate our hypothesis, we examine the role of regulations in the years leading up to and during the Global Financial Crisis (GFC) in the United States, when the Federal Reserve tried to restrain the over-heated housing market propelled by the predatory mortgage frenzy and the increased use of securitization in risk-hiding financial tools such as Collateralized Debt Obligations (CDOs). Our results indicate that deleterious government interventions may act as a chemotherapy of sorts, causing harm followed by a slow recovery. This understanding can help governments draft better regulations to lower market frictions and better protect investors.

FACTOR COPULA MODEL FOR PORTFOLIO CREDIT RISK

A critical aspect in the valuation and risk management of multi-name credit derivatives is the modeling of the dependence among sources of credit risk. The dependence modeling poses difficulties in the pricing of a multi-name credit derivatives, in the estimation of the value-at-risk of a portfolio, or in the pricing of some other basket credit derivative as the description not only on the default arrival in an individual reference entity but on the default dependence among entities in the portfolio should be considered. Although the elliptical models have been widely used due to their mathematical tractability, the dependence modeling using the multi-dimensional Lévy process has shown growing interest among researchers despite its complexity. In this paper, we introduce one factor copula model for portfolio credit risk based on Normal Tempered Stable (NTS) distribution and calibrate the model through 5-year synthetic Collateralized Debt Obligation (CDO) tranche spreads under a large homogeneous portfolio approximation. The calibration results show that the one factor copula model based on NTS distribution is more flexible and provides a dependence structure fitting market CDO tranche spreads. As one of the major applications of the dependence modeling in credit risk, this model shares the advantage of the Gaussian one factor model, and all extensions and implementation methods used for it can be utilized.