Using Assure for Private Investment Funds. Assure Joins Cap Table Coalition. All rights reserved. Assure is a registered trademark of Assure Services, Inc. What Is an SPV? Other key features include: Removing audit and financial statement obligations Allowing for unique waterfall provisions and economics specific to an investment Giving investors the opportunity to choose specific investments Allowing investors to pool capital to meet minimum investment requirements Providing the SPV sponsor with carried interest or other performance fees Raising an SPV is Like Building a House RISKS: Factors that determine a predictable outcome, such as neighborhood, size, location on a house.
These factors determine if your house will appreciate or depreciate. Risks are inherent and must be accepted. The risk of the asset acquired by an SPV is determined similarly by location, team, product-market fit, etc.
A house has basic must-haves, sheetrock, brick, windows, wiring, pipe, and labor. The expense of the structure will depend on the size, quality, and features of the house before you even move in. A special purpose vehicle SPV is a subsidiary company that is formed to undertake a specific business purpose or activity.
SPVs are commonly utilized in certain structured finance applications, such as asset securitization, joint ventures, property deals, or to isolate parent company assets, operations, or risks. While there are many legitimate uses for establishing SPVs, they have also played a role in several financial and accounting scandals. Special purpose vehicles have their own obligations, assets, and liabilities outside the parent company. SPVs can, for example, issue bonds to raise additional capital at more favorable borrowing rates than the parent could.
They also create a benefit by achieving off-balance sheet treatment for tax and financial reporting purposes for a parent company. The SPV itself acts as an affiliate of a parent corporation, which sells assets off of its own balance sheet to the SPV. The SPV becomes an indirect source of financing for the original corporation by attracting independent equity investors to help purchase debt obligations.
This is most useful for large credit risk items, such as subprime mortgage loans. Not all SPVs are structured the same way. Once the LLC purchases the risky assets from its parent company, it normally groups the assets into tranches and sells them to meet the specific credit risk preferences of different types of investors.
There are several reasons why SPVs are created. They provide protection for a parent company's assets and liabilities, as well as protection against bankruptcy and insolvency. These entities can also get an easy way to raise capital. SPVs also have more operational freedom because they aren't burdened with as many regulations as the parent company. Public-private partnerships are collaborations between a government agency and a privately owned company. Many private partners in public-private partnerships demand a special purpose vehicle as part of the arrangement.
This is especially true for capital-intensive endeavors, such as an infrastructure project. The private company might not want to take on too much financial exposure, so an SPV is created to absorb some of the risks. Securities and Exchange Commission. Andrew S.
Fastow, Defendant. Kenneth L. Lay, Jeffrey K. Skilling, Richard A. Causey ," Pages 2, 6, 9, Accessed Aug. Alternative Investments. Financial Statements. Actively scan device characteristics for identification. Use precise geolocation data. Select personalised content. Create a personalised content profile.
Measure ad performance. Without the return on its SPV equity the bank would be insolvent. But the SPV has done well so that neither defaults on its debt. The bank earns zero. Then lenders will participate in lending to the bank and the SPV, respectively, if the promised repayments are at least: 0. The value of the bank is given by 3.
Proposition 2 states that securitization would be feasible, i. However, accounting and regulatory rules prohibit such a commitment, even if it were possible. That is, a formal contract, which can be upheld in court and which is consistent with accounting and regulatory rules, effectively would not be consistent with the SPV being a QSPV, and hence the debt would not be off-balance sheet. The bankruptcy costs would not be minimized.
We now turn to the issue of whether a commitment is implicitly possible in a repeated context. However, the idea that repetition can expand the set of equilibria, when commitment is possible, is familiar from the work of Friedman , Green and Porter , and Rotemberg and Saloner , among others. The firms want to collude to maintain oligopolistic profits, but cannot formally commit to do so. Here the context is somewhat different. In a sense the two parties are colluding against the accountants and regulators.
The basic idea of repeating the SPV game is as follows. The SPV then defaults on its debt. If the bank does poorly, we assume that the bank can obtain more equity so that again there is E.
These issues are beyond the scope of this paper. Since this is positive, the bank has an incentive to renege. Obviously, the bank will not renege on subsidizing the SPV if the expected present value of the loss is greater than the one-shot gain to deviating. While this is the intuition for Implicit Recourse Equilibrium, it clearly depends on the beliefs of the investors and the bank. There may be many such equilibria, with very complicated, history dependent, punishment strategies.
The idea is for the investors in the SPV to enforce support when needed by the threat of refusing to invest in SPV debt in the future if the sponsoring firm deviates from the implicit contract. In general, strategies can be path dependent in complicated ways See Abreu However, a simple approach is to restrict attention to punishments involving playing the no-SPV stage game equilibrium for some period of time, starting the period after a deviation has been detected.
We adopt this approach and assume investor and bank beliefs are consistent with this. For simplicity we will construct a simple example of an Implicit Recourse Equilibrium. Investors choose which type of debt, and how much, to buy. Otherwise the bank finances both projects on-balance sheet. At the end of a period, the state of the world is observed, but cannot be verified. At the start of any period, both the banks and investors know all the previous outcomes.
Also, consider a date at which the bank has always subsidized its SPV in the past. Over the next period the bank is worth VSC if it securitizes one project off-balance sheet and retains the other on balance sheet. If both projects are financed on-balance sheet, the bank is worth VH. Recall that agents discount at rate r. Consider a one- shot deviation by the bank. That is, the bank decides not to subsidize the SPV, when investors expect the bank to subsidize it.
But, the point is that there can exist equilibria where the costs of bankruptcy are avoided by using off-balance sheet financing. Summary and Empirical Implications The conclusion of the above analysis is that the value of SPVs lies in their ability to minimize expected bankruptcy costs; securitization arises to avoid bankruptcy costs.
By financing the firm in pieces, control rights to the business decisions are separated from the financing decisions. Off-balance sheet financing reduces the amount of assets that are subject to this expensive and lengthy process.
The relational contract depends upon repeated use of off-balance sheet financing. We showed that this repetition can lead to an equilibrium with implicit recourse. Such an equilibrium implements the outcome of the equilibrium with formal commitments, were such contracts possible.
The comparative static properties of the Implicit Recourse Equilibrium are based on the result that the equilibrium outcomes of the Implicit Recourse Equilibrium are the same as the commitment equilibrium.
The idea of a relational contract supporting the feasibility of SPVs leads to our first set of empirical tests, namely, that the trigger strategy can only provide intertemporal incentives for the sponsor insofar as the sponsor exists.
If the sponsor is so risky that there is a chance the sponsor will fail, and be unable to support the SPV, then investors will not purchase the SPV debt. The second hypothesis that we empirically investigate is suggested by Corollary 1.
Because the Implicit Recourse Equilibrium implements the outcome with formal commitment, Corollary 1 also describes the repeated equilibrium with implicit recourse. Corollary 1 says that the profitability of off-balance sheet financing is increasing in the bankruptcy cost, c, and increasing in the riskiness of the project i. In other words, riskier sponsors should securitize more, ceteris paribus. Bankruptcy costs are not observable, but the riskiness of the firm can be proxied for by its firm bond rating.
Data The rest of the paper examines some evidence that is supportive of our model. Our analysis suggests that the risk of a sponsoring firm should, because of implicit recourse, affect the risk of the ABS that are issued by its SPVs. As mentioned above, we first consider whether investors care about the strength of the sponsoring firm, above and beyond the characteristics of the ABS themselves. Second, we consider whether riskier firms are more likely to securitize in the first place.
To these ends we utilize a number of datasets. This covers essentially all credit-card ABS through mid The dataset includes a detailed summary of the structure of each ABS, including the size and maturity of each ABS tranche. It summarizes the credit enhancements behind each tranche, such as the existence of any letters of credit, cash collateral accounts, and reserve accounts. Further, the dataset includes some information about the asset collateral underlying each ABS, such as the age distribution of the credit-card accounts.
Also included is the month-by-month ex post performance of each note, in particular the excess spread and its components like the chargeoff rate. The sample used below includes only the A and B tranches, i. Although it is difficult to find pricing information on credit-card ABS, we obtained from Lehman Brothers a dataset containing the initial yields on a large subset of these bonds that were issued in , for both the A and B notes.
We obtained similar data from Asset Sales Reports for bonds that were issued before Before we use only the third quarter September data, since credit card securitizations were reported only in the third quarter during that period. The dataset provided the current amount of subordination using current balances. For our analysis below, we want the original amount of subordination at the time of issuance. We were able to estimate this given the original balance sizes of the A and B notes, as well as an estimate of the size of any C note.
The size of C notes is not directly publicly available, but we backed out their current size from the reported current amount of subordination behind the B notes. We used this to estimate the original amount of subordination behind the A and B notes. Accordingly our sample includes all the banks in the Call Report dataset for which we have a matching rating. In this section we analyze the determinants of the spread on the notes issued by the SPV to the capital markets.
Borgman and Flannery also analyze asset-backed security spreads, over the period They find that credit card ABS require a lower market spread if the sponsoring firm is a bank or if the sponsor includes guarantees as a form of credit enhancement. The unit of observation is a transaction, that is a note issuance, either the A note or the B note. We examine the cross sectional determinants of the spreads. As discussed above, to test for the existence of a relational contract allowing for recourse, we examine whether other factors affect the ratings of the notes, in particular whether the strength of the sponsor matters, as estimated by its senior unsecured credit rating at the time of issuance.
Time is a vector of year dummies that control for time varying risk premia as well as all other macroeconomic factors, including the tremendous growth in the ABS market over the sample period. Structurei represents the structure of tranche i at the time of issuance, such as the degree of subordination and other credit enhancements supporting it, and 28 Since small banks are less likely to be rated, matches are most common for the larger banks. By distinguishing the A- and B-notes, the analysis implicitly controls for any clientele effects.
Trustj is a vector of trust dummies. The trust dummies control for all trust fixed effects. Since many sponsors have multiple trusts, the dummies also essentially control for sponsor fixed effects.
Our initial sample includes only the A notes, but later we add the B notes, with Structure then including an indicator for the B notes Junior.
Table 2 presents summary statistics for the key variables used in the analysis, for the sample of A notes. The sample runs from Over that time the average A-note spread was just under 50 basis points b. Column 1 includes only the year dummies omitting and the sponsor ratings as well as the trust fixed effects.
Nonetheless, the adjusted R2 is already relatively large. The year dummies are significant, with spreads peaking in the early s, perhaps due to the recession.
The sponsor ratings at the bottom of the table are of primary interest. Relative to the omitted AA-rated sponsors, the effects of riskier sponsor ratings is positive and monotonic.
The coefficient RatingB for the riskiest Baa and Ba sponsors is statistically significant. Thus investors do indeed require higher yields for bonds issued by the trusts of riskier sponsors.
That is, even though the A notes all have the same bond ratings, the strength of the sponsor also matters, consistent with our model.
This effect is also economically significant. The riskiest sponsors must pay an additional 46 b. This is a relatively strong result given the trust dummies which control for all average and time-invariant effects. But the bond ratings are discretized, not continuous-valued, so there can be some differences in risk even among bonds with the same ratings. Hence we also directly control for the potential risk factors observable by investors.
The next columns start by adding controls for the structure of the A notes. Recall that the trust dummies already controlled for all time-invariant trust effects. These dummies are always jointly significant unreported. For instance, some trusts might get locked into an older trust-structure technology that is considered riskier.
Column 2 explicitly controls for the amount of direct subordination behind each A note. LowSub represents the quartile of notes with the smallest amount of subordination i.
It has a significant positive coefficient. Thus, the notes with less enhancement have to offer investors higher yields to compensate. Nonetheless, the coefficients on the ratings variables change very little. The results indicate that longer maturity and fixed-rate notes pay significantly higher spreads. This could mean that the size of the subordination might be a function of, among other things, maturity and whether the deal is fixed rate. Despite these effects, again the coefficients on the ratings do not change much.
Given the other covariates, these additional enhancements are individually and jointly insignificant. Though as indicated in Table 2, only CCAs are frequently used. But the sponsor ratings remain significant. Nonetheless our conclusions below persist under the larger sample available if we do not control for LowSub.
Again, these are variables that the rating agencies take into account when approving the bond structure with a given rating, so their effects could already have been taken into account.
Since older accounts tend to have lower probabilities of default, this should reflect a safer portfolio. Riskier portfolios, whether unseasoned or with higher charge-off rates, must pay higher spreads. While Chargeoff is an ex post chargeoff rate, the conclusions are the same on instrumenting for it using the balance-weighted average chargeoff rate in the trust from the month before the issuance of each note in the sample.
All regressions now include an indicator variable Junior for the B notes. In column 1 , this indicator is significantly positive, as expected given the greater risk of the B notes. They must pay on average 29 b. The coefficient on the riskiest sponsors, RatingB, remains significant and large at 42 b.
Thus the extra yield that must be paid by risky sponsors is even larger than the extra yield that must be paid by B notes. In column 2 , LowSub indicates the A notes with the lowest quartile of subordination, and LowSubJr indicates the B notes with the lowest quartile of subordination. The latter variable is significant and drives out the direct effect of the Junior indicator , implying that 34 For an account-level analysis of the determinants of default probabilities, see e.
Gross and Souleles For a portfolio-level analysis, see Musto and Souleles The original age data reflects the age of the accounts across the entire trust as of a given time. To estimate the age distribution of accounts underlying a given note at the time of issuance, we subtracted the time since closing. This assumes that the composition of the assets did not change too much between the time of closing and the time of reporting.
Otherwise, it is credit card balances relative to total assets from the Call Report data. This suggests that the latter effect might not reflect just a correlation between the assets in the trust and the assets on- balance sheet, since presumably the credit card assets in the trust are more highly correlated with the credit card assets on-balance sheet, compared to other on-balance sheet assets.
The rest of the analysis is analogous to that in Table 3, and the conclusions are the same. Even controlling for the ABS structure and underlying assets, the ratings of the sponsors remain significant, both statistically and economically. This supports our theoretical conclusion that the strength of the sponsor matters, because of the possibility of implicit recourse commitment.
To reiterate, the trigger strategy at the root of the relational contract concerning recourse requires that the sponsor exist, that is, have not defaulted. The results are consistent with the investors in the ABS markets pricing the risk that the sponsor disappears and cannot support its SPVs. Empirical Tests: Which Firms Securitize? In this section we turn to testing whether riskier firms securitize more than others. Since our model is of course highly stylized we analyze more generally the determinants of securitization.
The denominator can also be interpreted as managed assets, although we do not have information on the full extent of off-balance sheet assets including non-credit card assets under management.
Our conclusions are similar on not including the securitized loans in the denominator. Xi,t controls for various bank characteristics over time. These control for scale effects, including costs that might arise in setting up and maintaining securitization trusts. Given the bank effects, the ratings variable will capture only within-bank variation, i. To highlight the changes in the credit card ABS market over time, the second panel shows the same statistics for the end of the sample period the first half of Comparing the panels shows the large growth in the market over the period.
The average bank rating declined over the sample period, though this happened for both the banks that securitized and those that did not. Further, at any given time there is substantial cross-sectional variation across banks in the incidence and amount of securitization and in their ratings. The raw data suggest potential scale effects, with the big securitizers often being the bigger banks.
Calomiris and Mason discuss the relation between securitization and capital ratios. Most of these were small banks in the early s that did not securitize only 1 of these banks securitized. As a result, they tended to be automatically dropped from the fixed effects estimation or otherwise, their effect was imprecisely estimated due to their small sample size.
For instance, many banks were downgraded or upgraded at various times. Also, some banks securitized in only a few years apparently just trying it out , whereas others securitized frequently but in varying amounts over time.
The main results are in Table 6. Given the other covariates, in this specification the probability of securitizing is not monotonic in Assets; after initially increasing with Assets, it later declines. This could mean that having a large portfolio of credit cards provides economies of scale in securitizing. Also, the probability of securitizing is not monotonic in CapRatio but increases for large CapRatio. Relative to the omitted AA ratings, the middle RatingA banks are somewhat less likely to securitize.
Nonetheless, the riskiest RatingB are indeed much more likely to securitize. The conclusions are similar to those in the previous column. In both of these specifications, and those that follow, the pseudo and adjusted R2 statistics are relatively large. The remaining columns control for bank fixed effects. Column 3 uses the fixed effects logit estimator.
More importantly, both RatingA and RatingB have significant positive effects, with a larger effect for the latter. Relative to banks with AA ratings, those with B ratings have about a 3.
Overall we conclude that there is some evidence that riskier firms are more likely to securitize, consistent with our model, though the effect is not always monotonic, depending on the specification. Summary The empirical results are consistent with the theory proposed above, namely that an implicit contractual relationship between SPV sponsors and capital markets investors reduces bankruptcy costs. The prediction of the model that firms with high expected bankruptcy costs would be the largest users of off-balance sheet financing was also generally confirmed.
Conclusion Off-balance sheet financing is a pervasive phenomenon. It allows sponsoring firms to finance themselves by separating control rights over assets from financing. The operating entity, that is, the sponsoring firm, maintains control rights over the assets that generate cash flows. The assets projects can be financed by selling the cash flows to an SPV that has no need for control rights, because the cash flows have already been generated.
We have argued that this arrangement is efficient because there is no need to absorb dead-weight bankruptcy costs with respect to cash 41 We also tried various extensions.
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