The rise and fall of structured finance
Abstract
The financial crisis of the year 2007 and 2008 saw the world affected negatively with the economy affected adversely (Smith & Mendoza, 2011). Several scores have been directed to increased demand in the housing sector while other have resulted in concluding it was the failure of the financial regulation authorities. The severe effect caused the United States economy job market loss approximately 8 million workers as the inflation rate declined to near zero. The main purpose of the paper is to give clear insights of what caused the increase in the structured finance market and eventually its fall.
In this study, the researcher set out to show that only securities that exhibited confidence when underlying them and how they attracted investors by making them appear as high paying. The study will also go an extra mile in explaining the correlation that is existing in the financial market components. Applying the structured prototype in security of finance, the researcher uses CDO (collateralized debt obligation) in illustrating that issuance of capital structure increases the likelihood occurrence of under evaluating of underlying securities and evaluation of risks. The researcher obtains data from secondary sources and Wall Street Journals. The results obtained indicated that credit rating agencies over rated their credits against collateral securities leading to miscalculation and wrong presumptions that saw the economy rise and then come down tumbling (Smith & Mendoza, 2011).
Introduction
Structured Finance purpose is to pool in together economic assets generally referred to as financial assets such as the mortgages, bonds, loans and issuance of mortgages and prioritized structures claims that include the tranches given against the collateral pools. Several of the manufactured tranches are safe due to prioritization of the scheme that is applied to structuring claims compared to asset averages that are underlying the pools (Hai-jun & Hong-bing, 2010). The capability of the structured finance to transfer risk and reduce it to the minimum level resulted in the increased issuance of structured securities that were deemed very risky by investors attracting them to invest as they appeared to be risk-free components certified by agencies that rated these securities. In the recent studies that have been presented through the subsequent studies in the past have confirmed the investments were riskier when compared to other assets and how they were advertised (Mallon & Waisman, 2011).
The study, therefore, aims to explain the rise and the falling of the structured market process and how securitization made it possible to for trillions of dollars to be transformed to securities from the risky assets. The effect of creating the notion of the assets being free from risk made them be considered widely safer (Köhn, 2011). The argument that is to be brought out in the study is to show the components of structured finance that had to fuel the growth of the finance sector and at the same time look at the features that led to its downfall.
In this study, the researcher set out to show that only securities that exhibited confidence when underlying them and how they attracted investors by making them appear as high paying. The study will also go an extra mile in explaining the correlation that is existing in the financial market components. Applying the structured prototype in security of finance, the researcher uses CDO (collateralized debt obligation) in illustrating that issuance of capital structure increases the likelihood occurrence of under evaluating of underlying securities and evaluation of risks
To understand the rise and the fall of the structured markets we commence by looking at how the financial market operates in the literature review. The construction of the collateralized obligations indebt are to express on how tranching and risk pooling gives opening to credits that lead to increased claims (Zhuolei, 2009). The rating agencies also go through challenges that we seek to explore and it this study, parameters that are useful in modelling and the assumptions that are made in arriving at the best rating to finance that is correct.
Literature review
Several studies have been conducted on the structured market for over several years. These studies have provided deep insights into understanding the concept behind the operational mechanisms of the market. The common logic that stands out in these studies is the amplification of credit and collaterals in the security market. A study by Schaber (2009), set out to find the parameters that are significant in showing the variations in the structured finance and its associated risks. They realized the rating placed on the cause securities was AAA increasing the defaulting rate and its likelihood. The aspect of diversification of risks had to be factored in their studies as they sought to find out the available avenues the risks would have been minimized by exploring appropriate kinds of securities. They realized in their study that securities that had been created in the structured finance operations stood a less chance of persevering in the economic crisis when compared to other assets equal rating such as the traditional corporate securities. In addition, the senior tranches default risk have an adversely concentrated economic status, which requires the investors to demand for higher premia in risk of holding structured claims when compared to corporate bonds (Bodenstedt & Scheule, 2012).
Agencies that include Fitch, Standard and Poor's and Moody for over a century have been analyzing through gathering a wide spectrum of financial system components, economic and industry data to provide information necessary for assessing on worthiness of credit, which are independent of other entities. These studies have yielded rating scales that are varied including the AA, A, AAA, BBB amongst several others. Their studies have opened into other business models after they decided to engage in several industries (Hai-jun & Hong-bing, 2010). Majority of the study did focus on a single outlet a finance corporate that was only the aspect of credit worthiness as attached to instruments of finance related to a single company. These models in the past have been expanding to cover the present field of structured finance as opposed to historical roles of the credit rating only.
For a long-time, structured market for securities has been evolving from "a rated market" whereby the risk tranches had to be assessed using the agencies of credit rating. The issuers of products related to finance market were yearning to have their products being rated in the same category bonds to subject them to risk-free status and reduced constraints to attract investors would purchase those securities. The illusion created by having the securities being rated was that of comparing those existing with “single unit name” securities. The impact was the provision of access of bigger pools of probable buyers on what might have been considered as a complex derivative security market.
The past decade has seen repackaging of several kinds of risks that have enabled the creation the Triple-A-rated securities in large amounts providing yields observed to be competitive. In the United States alone, approximately 37,000 structured bonds were available by June 2007, making it the biggest in the top ratings (Schaber, 2009). The total number of all ratings in AAA-rating according to Zhuolei (2009), were 61 percent when compared to the 1 percent issues of the corporate. Through issuing AAA rated that attracted better yields and fewer interest rates did make the products eagerly looked for by several investors who rushed to buy them up. In the event, large sums of securities to represent a larger portion of the Wall Street while increasing the agency returns in the shortest time possible. In the previous year, issuance of structured finance had seen the Wall Street increase its revenues in tandem with Moody, which saw its revenues increase by 44 percent from the structured finance and its products. The increase did surpass their normal revenues of 32 percent from the traditional activities that included bonds (Lucas, 2007).
Things started to change in the year 2008 with collateral debt issuance totally slowed down. There were massive losses incurring from the write-downs from the Wall Street banks as they lacked an opportunity to avoid the impact. These banks had not foreseen the event, hence had not set out preventive mechanisms. In an overnight, the increased revenue arising from structured finance products for rating agencies vanished within a short time as the share prices declined to below 50 percent. This decline in the revenues from financial sectors such as banks, rating agencies and Wall Street seemed permanent as crisis loomed. The existing products and a bigger fraction of structured market had to be downgraded in their assets and rate spread through the asset-backed securities affecting them in different ways. A practical example is the underwriting of the 27 tranches out of the 30 collateralized debt from asset-backed Triple A-ratings by Merill Lynch into "junk" in the year 2007(Hai-jun & Hong-bing, 2010). The other agency affected was Moody that had to downgrade approximately 32% of security backed tranches on collateralized debts, as well as AAA-ratings, initially rated approximately to 14 percent (White, 2010). Approaching the midst of 2008, saw business being closed down in the structured finance activities. According to Barth (2009), Standard and Poor's president Sharma Devin expected the issue would remain the same (structured financial activities closed down) for several "years" to come.
Data and methodology
Since the research is about rising and falling of structured finance, the researcher used a research design of quantitative and descriptive research studies, which described the characteristics and the relationship of the sample selected and finally was used to make inferences structural finance market.
Data used in the analysis was obtained from credit agencies that include Agencies that include Fitch, Standard and Poor's and Moody. Secondary data was preferred as additional information was sought from Wall Street articles and journals.
The data used included that of CDO (junior, mezzanine and senior), CDO2 (junior, mezzanine and senior), collaterals and mortgage demands. The data was analyzed using SPSS and STATA and the findings provided in tables and graphs.
Findings and analysis
Rating structured finance assets challenges
The measure of the capability of entities and issuers to cover their financial obligations is measured through credit ratings. The measurement is in terms of the interest required for payment, the principle itself and the investor position in the market. The determinants of assessing include the agency responsible for the issuance and the personality of the entity on whose worthiness of credit is measured. The two help in assessing the likelihood of defaulting in the credit depending on the economic loss expected and the probability of observing conditional loss severity outcome upon failure of the individual to meet his or her obligations. Therefore, credit rating is imagined in other terms as the expected security measure of cash flows (Lucas, 2007). When implying bonds, BBB-rated securities are identified as the "investment grade" and in wider sense are imagined to be a wider representation of the moderate to low default risk levels. The other component of BB+ rated securities are identified as the "Speculative grade" and tend to be termed as default or near to default.
The results on the probabilities of 10-year default on the corporate bonds are provided below expressing different issuance ratings when rates of default are annualized. It can be observed that from the data that there are ten rating categories that are distinct from the estimates of debt obligations. These categories do range from AAA to BBB- with the default annualized rates varying from the percentage of .02 and .75. It is difficult to distinguish between the exact securities associated with the investment since the narrow range limits the precision of prediction (Lucas, 2007). To identify the appropriate securities grade demands a precise estimation of the security's default probability. On the other hand, the rating categories in the grade range under speculation is from BB+ towards C with the range of 1.07 – 29.96%.
It can be observed that where the single naming of the business in the past did develop their expertise. Assessing of securities was independent hence making the rating agencies to become agnostic on the rate to which defaults would have to be correlated. In order to assign structural finance ratings the agencies had to deal with challenges that were bigger in defining the whole distribution joints on payoffs to the underlying collateral pools (Zhuolei, 2009). Further, collateral debt requirements depend on diversified abilities to meet enhancement in credit. In rating the structural finance products arises from the debt obligations that are magnified to give imprecision when determining default rates and the correlations in the default. Another issue is the model errors that were used that had potential default dependencies and misspecifications.

The rating problems were further enhanced by the capital structures application in manufacturing of the CDO (collateralized debt obligations) tranches that were identified as the CDO2. Structuring and restructuring occasionally did lead to small errors on the underlying securities that did change the rating of the securities.
Debt obligation sensitivity had to be measured with CDO2 as its progeny expressed to parameter errors and estimates, the researcher had to conduct an exercise in simulation. The first step is get payoff associated with collateralized debts to 40 pools with everyone having bonds totaling 10o bonds ranging in a 5-year probability default of 5% and rates of recovery being on face value as 50% (Barth, 2009). The calculated annualized default rates above did provide did give guidelines on the steps to follow. In each hypothetical collateral pool, a bond has to obtain investment grade of "Just below" in the rating of the BB+. The last simulation is the pairwise fixing of bonds on the correlations of .20 in a unique pool of collateral in the assumption not all the bond defaulting associations in each pool correlate. The methodology as mentioned before assumes the industry model is making losses on its portfolio.
The results obtained are tabulated below under baseline parameters.

On the top, it indicates, expected payoffs of collaterals underlying do not depend on the correlations of the default. While the default increase in the correlation from its base of 0.20, the default risk is indicated to be less diversified compared to the expectations and the shift in risk is towards the senior claims as compared to junior claims. On the other hand, payoff expected on the junior tranche increases from the value baseline as mezzanine tranche do fall. The decline in the payoff falls up to a level observed as 0.8 and the loss incurred by the tranche is 10 percent on its baseline mark (White, 2010). Then there is a marked rise experienced from the perfect correlation from the defaulters as the risk is transferred to senior tranches. Due to the perfect correlation, each defaulter is subjected to similar 5 percent default chances in the next assigned five years on the securities of the underlying investment.
The above results can be expressed in a graph showing mezzanine shift amplification through the second generation in structure CDO2 as

The observation made is that increase in the default correlation pairwise bonds of the underlying collateral from a 20% - 60%, the expected mezzanine payoff on the investment grade security CDO2 drops by 25%, a noted staggering rate.
The second figure examines error estimates and the default rates that are associated with securities. Probability does increase as observed when the baseline correlation value is set at .20 compared to its 5% estimate, as the payoff expected on the collateral decreases in a monotonic manner. The impact is shifted to tranches associated with the debt obligations of collateralized. The rate of sensitivity arising from the errors in tranche in the estimation is identified their seniority tranches (Hai-jun & Hong-bing, 2010).
In the simulation, further analysis had to be performed to express how the changes affect the ratings on the assets underlying default correlation and probabilities of default. The information is presented in the table below.

Despite the payoff of tranches in the senior section are robust, they deceive because of the precise investment rating partitioning hence giving the implication that similar precipitation is exhibited even in the modest changes. An example is the observation made when senior tranche in debt requirements falls to A+ upon the probability default reaching 10% and investment boundary of BBB- at 20% (Bodenstedt & Scheule, 2012).
The table further indicates that CDO2 of the second generation, exhibit higher sensitivity when there are parameters of the baseline change. Very little changes in correlations and probabilities of default can influence expected payoffs in a big way as is expected in the CDO2 tranches.
The structured market rise and its fall
The above study aimed to express how the components of structured market made investment in the structured finance market attractive. The promised increased return on imagined risk-free collaterals and lower interest rates seemed a potential investment opportunity to entities that rushed to acquire a large number of shares in the financial securities industry. The events explain the United States issuance of finance products leading to its diverse economic growth immensely with institutions recording higher revenues. According to Barth (2009), the U.S economy at the start of the year 2005 had approximately $35 billion finance products under structured finance. The economy entered its best record of $100 billion in 2007. However, by the end of 2008, the financial products experienced a big drop that was ever recorded. This drop down was later identified as the global financial crisis of 2007-2008 as the financial products dropped quarterly to less than $5 billion (White, 2010).
The arguments brought about in the dramatic rise in the sector is the increased mistakes of on the assumption of tranching and pooling in defaulting of risks and underlying assets with their correlations. The other reason was the capability of concentrating senior tranches risks identified to be risky. The event happened coincidentally when the economy was doing well and was recovering from the 1992 crisis. The favorable economy provided better avenues that minimized the suspicion that investors would have questioned on the best deals they were being compared to the robustness of the economy. Structured finance securities including the AAA-ratings promised high returns when compared to other securities of its equivalent like corporate bonds. The attributed nature of the securities augmented with their advantages drove investors rushing to invest expecting higher returns. On the contrary, the returns were minimal when compared to its underlying security risks. The credit ratings did provide a biassed viewing referred to as downward when place on the actual risks as the ratings had to be done based on the naivety of the rating agencies responsible for credit. The other aspect that led to increase and collapse of the structure was failure to account for extreme exposures arising from the financial components to the decreasing general economic conditions, referred to as systematic risk (Smith & Mendoza, 2011). The exaggerated little yields made investors be compensated highly compared to minimum available tranches (junior). The increased demand for the collateral assets that were underlined as securities on assets resulted in an unparalleled decline in the costs of borrowing to corporations and mortgage owners stimulating the real estate escalation.
Some investors were curios and did realize that the assets had been overvalued considering the underlying assets. However, there was disregard due to available incentives as defaulters in the subprime mortgage increased in number. As expressed in findings, default probabilities in default errors do affect all tranches negatively while the highest impact being on the junior tranches (Bodenstedt & Scheule, 2012). The errors had to be magnified by the CDO2 structures that are commonly identified in this market, impairing even the senior tranches.
Several individual tried to pass blames on each other as the rating agencies could not agree on their miscalculation and over rating credit ratings. They could not agree to their assumption of the modest errors and the probabilities and correlations they made. Their failures and lack of knowledge have been tied to bank capital and their regulators (Barth, 2009).
Conclusion
The crisis of 2007 – 2008 has the highest possibility of being attributed to credit crunches as those purchasing the products of structured finance market had to stop their purchases (Smith & Mendoza, 2011). The triggering event is the sudden change of the subprime activities that incurred increased losses raising concerns in the finance market. Some scholars have argued that the crisis will have to work out itself while others remain skeptical by holding the view that the problems were structural and could not be avoided.
The assumptions could be true based on the analysis performed and the findings relying on the three assumptions of correlation on the defaults, default recovery and probability and lastly payoffs on the economic status. In the crisis, it was obvious that investors in the finance market experienced some biases expressed in the above three assumptions. These events have opened investors' eyes since they are no longer interested in investing securities they lack knowledge (White, 2010). It is significant that investors understand structures securities and single name to place themselves accordingly when it comes to risk exposure. Securities that arise from pooling are severely affected by the economic status and face serious catastrophe when economy faces recession.

References
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