Retail Mortgage Backed Securities, Commercial Asset Backed Securities and Corporate Bonds: a Credit Spread Comparison + LORIANA PELIZZON * University of Padua
ENRICO RETTORE University of Padua
EMANUELA SOTTANA Finanziaria Internazionale
Abstract
In this paper we investigate whether and how Retail Mortgage Backed Securities (RMBS), Commercial Asset Backed Securities (CABS) in the European market differ from other more traditional investment products, in particular in terms of the trade-off between risk and return. Therefore in this paper we compared credit spread on RMBS and CABS to credit spread on corporate bonds within the same rating class and find some explanation of the differences between them. We find that the market requires the same remuneration to invest in a bond of a single firm and in a bond on a portfolio with the same rating, if this rating is high. On the contrary, the market requires a different remuneration if the rating is low. Moreover, the spread is not statistically significant between Retail Mortgage Backed Securities and Commercial Asset backed securities when the rating is high, but is higher for Commercial Asset Backed Securities if the rating is low. Potential explanations are provided.
December 2002
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We gratefully acknowledge conversations with Stephen Schaefer, Rajiv Guha and Davide Menini. Dipartimento di Scienze Economiche, University of Padua, Via del Santo 33, Padova, Italy, Phone +39 049 8274054, Fax +390498274211,
[email protected] *
INTRODUCTION
Securitization is one of the most innovative financial techniques; it consists of a transformation of illiquid assets into negotiable securities. The structure of securitization may differ significantly from one issue to another, but the basis scheme is always the same: the originator sells part of the assets he has in his balance sheet to a Special Purpose Vehicle (SPV), which is a company created ad hoc in order to securitize the assets. Then the SPV issues bonds, the Asset-Backed Securities (ABS), in order to finance the asset purchase. Securitization has been spreading in Europe very fast since the last years: in 1996 ABS issues accounted for € 37.2 billions, while in 2001 they reached € 135.6 billions, almost doubled compared to 2000 level1 . A reason for this exponential growth can be found in the fact that many European countries introduced new securitization laws during the 1990s, giving a steady legal framework to this kind of transactions. In particular Italian ABS market has grown quickly and became the second largest in Europe, since the introduction of law 130/99 – namely the “Securitization Law”. The advantages that make securitization attractive to originators are several: capital relief, liquidity, assets-liabilities management, funding, for example. Moreover securitization can be a useful tool for transferring credit risk because the originator may reduce the geographical and industry concentration of his assets as well as concentration of individual exposure. Considering the spread of securitization and thus the growth of ABS market in Europe in the last few years, it seems interesting to investigate whether and how Retail Mortgage Backed Securities (RMBS), Commercial Asset Backed Securities (CABS) differ from other more traditional investment products, in particular in terms of the trade-off between risk and return. Therefore in this paper we compared credit spread on RMBS and CABS to credit spread on corporate bonds within the same rating class and we try find some explanation of the differences between them.
1
Data are from European Securitization Forum (2002).
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The paper is organised as follows: section 1 deals with securitization as a credit risk management tool; section 2 overviews economic literature about credit spread – both on corporate bonds and on Asset-Backed Securities. Section 3 presents the empirical analysis developed in this paper: first, data set is briefly described; secondly, a descriptive analysis is shown in order to underline the excess spread of RMBS and CABS compared to corporate bonds; then some possible explanations for the excess spread are discussed; later on results of the regression analysis is presented. Concluding remarks follows in section 4.
1. Credit risk management through securitization As said above, securitization can be used to manage credit risk. In fact selling assets – through a true sale – the originator can transfer credit risk, which is, thus, shared by other agents in the securitization process, especially investors and credit enhancers. In this way originator’s balance sheet is more diversified, since geographical, industry and individual exposure concentration is reduced. Moreover, using synthetic securitization, the originator can buy protection against credit risk and, at the same time, maintain the assets on his balance sheet. So securitization assures from risk fragmentation; it is worth to underlying that it is not convenient for the originator to behave in an opportunistic way. In fact, if he wanted to “clean-up” his balance sheet by selling only the worst assets and so damaging investors, he wo uldn’t have other chances to rise funds in the future since his reputation gets bad. So originator has an incentive to behave correctly to keep his reputation good. Standard and Poor’s (2002) recently published a study on Italian bank-originators, in which they assert that little risk is transferred outside the originating group. In fact it is common for the originator to retain the subordinated tranche to credit enhance the issue. This practice causes the entire risk to be concentrated in the subscribed junior notes, that are classified among the originator’s securities. This implies that credit risk is not transferred out through securitization; so, if one would use securitization as a credit risk management tool, he must pay attention to project a correct trade-off between credit risk transferring and credit enhancement providing 2 .
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Credit enhancement is useful because it enables originator to issue higher rated bonds and thus to fund at a lower cost (in terms of yield offered to investors).
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2. Credit spread The yield of a bond is influenced by several factors, among which a very important one is credit rating. Rating reflects the credit risk of the bond, so credit spread – defined as the difference between the yield on a (risky) bond and the yield on a risk-free bond (such as, for example, a T-bond) – is in large part determined by credit risk, but other factors may be included as well. Literature about credit spread on corporate bonds is plentiful; He, Hu and Lang (2000) studied the shape of credit spread curves and find that high-rated bonds have positive sloped curves, low-rated bonds show negative sloped curves, while spread curves for middle-rated bonds are hump-shaped with peak points. Jakson and Perraudin (1999) found evidence for securities issued by U.S. banks to offer higher spreads compared to bonds issued by other industrial and utilities U.S. companies. Elton, Agrawal and Mann (2001) found that credit spread is due to default and taxation premium and also to a premium that covers a “systematic risk”. This kind of risk is due to the fact that spreads on bonds are in some part influenced also by the same factors that affect stock prices. Collin- Dufresne, Goldstein and Martin (2001) used a linear regression to find out which factors determine changes of credit spread. Their most interesting result is that factor thought to be important variable to explain credit spread changes have little explanatory power, while there is a common factor which is able to explain the remaining spread; however they could not determine such factor. Annaert and De Ceuster (1999) analysed credit spread on European corporate bonds and found evidence for the presence of a liquidity premium in the yield of these securities. Huang and Huang (2002) estimated how much of credit spread is due to credit risk and found that little part of it is explained by credit risk for high-quality bonds, while credit risk is more important in explaining credit spread on junk bonds. There are two particularly interesting articles about credit spread determinants of bonds issued by a securitization transaction: Maris and Segal (2002) and Rothberg, Nothfat Gabriel (1989). The former used a linear regression to find the determinants of credit spread on CMBS 3 ; results identify several variables that are related to spreads. In particular interest rate volatility and a recession index capture the default probability on underlying mortgages and so influence spread on CMBSs; tranche size has a negative slope, providing some support for the notion that larger tranches are associated with lower liquidity premia; at the opposite, total
3
Commercial Mortgage Backed Securities.
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deal size are positive and significant, indicating that larger deals are associated with higher spread. The authors explain the last result with the fact that a larger transaction require larger spreads to attract a sufficient number of investors to place the issue. Moreover an important factor explaining spread on CMBS is a time dependent variable which has a negative and significant coefficient that indicates that CMBS credit spread decrease from year to year. This provides evidence to support the notion that a learning process is taking place and involves issuers, rating agencies and investors. In fact lack of confidence with analysis and evaluation of this kind of securities caused investors to overtimate the risk and then explains the higher yield spreads of CMBSs in the early 1990s. Rothberg, Nothaft and Gabriel (1989) assert that mortgage pass-through and Treasury securities may reflect differences in taxation and compensation for default, call and marketability risk on mortgages. Using a linear regression they found that interest rate volatility and the term structure of rates have grown in importance in recent years as exercise of the prepayment option has increased. They also found evidence that liquidity and credit concerns affect the pricing of pass-through securities.
3. Empirical Analysis 3.1 . Data description
Data used in this work for Asset-Backed securities are published by BNP Paribas, while data for corporate floating bonds are provided by Bloomberg. Both types of securities are issued in Europe and are indexed to 3 or 6 months Euribor and to 3 months Libor; rating categories are “AA”, “A”, “BBB”, “BB”, following S&P’s notation. Many ABSs are triple “A” rated, but it is difficult to find European corporate-floating bonds rated “AAA”, so in the regression analysis ABSs rated
“AAA” are excluded. The time period covered is 1999-2002 for
corporate bonds, while for ABS data previous to 2001 (second quarter) were not available. The full ABS sample includes 448 securities (101 are “AAA”- rated), while corporate bonds included in the analysis are 115 4 . For each bond we needed spread, size of the issue, maturity, issue date, rating, idex-rate, type of ABS. Following Maris and Segal (2002), data for each security are issue date (in particular 4
In the following regression (section 3.4, 3.5 and 3.6) some bonds are eliminated and some more are included, so it will be pointed out each time in the appropriate section the proper sample size.
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spread is the launch spread). The category “ABS” includes ABS in a strict sense (leases, credit cards, auto loans, etc.)5 , CMBS, RMBS, CDO, CLO and CBO.
3.2. Descriptive analysis
The first aim of this paper is to test whether the theoretical relation between spread and rating holds for our data set. In fact theory predicts that lower rated bonds should offer higher spreads since they are riskier than higher rated ones, which should, thus, have lower yields. Evidence supports this argument in literature 6 and also in our sample the relation holds: let us look at figure 1; the graphs depict rating on the horizontal axis and spread (in basis points) in the vertical axis.
Figure 1: Relation between average spreads and credit rating and comparison between average spreads for corporate bonds and ABSs.
500 400 300 200 100 0
COMPARISON BETWEEN AVERAGE SPREADS AVERAGE SPREAD (b.p.)
AVERAGE SPREADS (b.p.)
COMPARISON BETWEEN AVERAGE SPREADS ABS_3ME CORP_3ME
AAA
AA
A
BBB
200 150
ABS_3ML
100
CORP_3ML
50 0
BB
AAA
RATING
AVERAGE SPREADS (b.p.)
250 200 150 100 50 0
ABS_6ME CORP_6ME
AAA
AA
A
BBB
RATING
6
A
RATING
COMPARISON BETWEEN AVERAGE SPREADS
5
AA
When a distinction is needed, ABS in a strict sense are labelled “classical ABS”. For example see Sarig and Warga (1989); Maris and Segal (2002).
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BBB
The figure shows that the average spread for each rating category within the same index group grows as credit quality worsens; this holds for corporate bonds (blue bars) as well as for Asset-Backed Securities (yellow bars). The same relation holds also for the aggregated sample as well – that is without distinguishing between index-rate groups. Figure 1 is useful also to compare spreads on ABSs and on Corporate bonds holding for the rating class and index-rate. As one can easily notice, ABSs’ spreads (represented by the yellow bars) are always much higher tha n spreads on corporate bonds (depicted as the blue bars), holding for credit rating. We can say that there is an excess spread on ABSs compared to corporate bonds, which must be due to factors other than those that affect corporate bonds’ yield. Figure 2 depicts the excess spread – i.e. the difference between the spread on ABSs and the spread on corporate bonds. In the horizontal axis credit rating is reported, while the vertical axis shows credit spread in basis points. As we can see, the curve has a positive slope indicating that the difference in spreads grows at a higher pace when rating gets worse. So low-rated ABSs has a higher excess spread than higher-rated ones. An explanation may be the following: since a goal of securitization is to reach a higher rating than that of the originator (so that the originator can fund at a lower cost), senior tranches have the best underlying assets, while subordinated tranches are less attractive and so must offer a higher spread to be subscribed by investors.
Figure 2: ABSs excess spread.
EXCESS SPREAD (ABS-CORP) 400 300 200 100 0 AA
A
BBB
BB
We can now try to discuss some possible explanations for the excess spread on ABSs.
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3.3. Explanations for the excess spread
Rating agencies evaluate the credit risk of an issue and so classify bonds into different “risk classes”. Assuming that rating is reliable, two different securities within the same rating category should be exposed to credit risk in the same way. When one assigns rating to a corporate bond, he considers the variables that capture the capacity of the issuer to fulfil his commitments. However to evaluate an ABS issue it is necessary to deeply examine the structure of the transaction, because the credit quality of the bond does not (entirely) depends on the credit quality of the issuer but on the quality of the underlying assets. In particular economic and financial performances of the assets are analysed (geographical and industry diversification, cash- flows, tranche allocation, etc.); different stress scenarios are studied in order to calculate default probability and recovery rates; finally the legal structure and credit enhancement of the transaction are analysed. Even if we assume that rating exactly reflects the credit risk of a bond, to conclude that different types of securities with the same rating have the same price, we should assert that credit risk is the unique risk to which bonds are exposed. For this is not true, we must say that different types of securities with the same rating are not necessarily priced the same. So we can say that there is a part of spread that is the same for the two categories of bonds holding for credit rating (and this part of the spread is due to credit risk), while the remaining spread – the excess spread shown in figure 2 – is not caused by credit risk and must be explained introducing other factors. These factors can be summarised by (i) the difficulty of evaluating a securitization transaction; the reasons for this difficulty is the complexity of the transaction, (ii) the presence of some informational problems and (iii) the lack of experience of the market. In fact, securitization is difficult to evaluate since it is a complicated transaction that involves many parties and a large number of contracts (and thus several small “sub-transactions”). This implies that experience and knowledge is needed in order to understand securitization and to asses the right price to the securities issued. Moreover we can say that there is a informational problem: while information about issuer of corporate bonds is easily accessible and quite prompt to be interpreted, information about the credit quality of all the assets underlying a securitization transaction is not easy to collect and not simple to evaluate. In fact ABSs are collateralised by several assets (often thousands), so the subscriber should look for information about all these assets and then study how the cash flow are allocated throughout the subordinated tranches; on the opposite a bondholder only 8
has to gain information about the issuer of the securities, which is a simpler and less costly process. The difficulty to evaluate ABSs’ issues is also due to the relative lack of experience of the market: it takes time for experts to get confident with this kind of financial product and then to evaluate it in a somewhat “automatic” way. In fact, although the legal framework provides good guaranties to investors in order to avoid the presence of “structural risks”, there is not a adequate case histories to be sure that law provisions will be fulfilled. In other words, if one subscribes a corporate bond and the firm suffers default, law provisions and the juridical practice assures that bondholders are repaid before stockholders (and thus there is no uncertainty about what investors have to expect if such an event happens). But wha t does it happen if a large proportion of the borrowers underlying a securitization transaction go bankrupt? Legal and structural guaranties are clear in a theoretical level but in practice cases like this did not happen (at least not in a sufficient number) and so nobody can say whether the legal provisions will be enough to assure investors from the happening of such an event. So we can identify something like a “learning by doing” process, which needs time to be completed and which should make spreads ge t lower as time passes. A similar argument can be found in Maris and Segal (2002) about American CMBS: they argue that “investors (and rating agencies) were unfamiliar with analysing commercial mortgages and risks they posed. Lack of familiarity might have caused investors to overestimate the risks, and could explain why yield spreads were high in the early 1990s”.As investors gained confidence with the product they developed a greater ability to asses the risks and so required lower risk premia.
3.4. Empirical analysis of factors that influence the excess spread
We can now try to determine factors that affect the excess spread using the data set described in section 3.1. Securities employed in the regression analysis are 444, among which 347 are ABSs 7 and 97 are corporate bonds. Linear regression is estimated with the Ordinary Least Squares method using the spread as dependent variable, while using a number of j regressors as independent variables, as described below. In particular, following Maris and Segal (2002), we introduce variables that consider the size of the issue, the rating, the issue date.
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79 are “classical” ABSs; 141 are CDOs; 127 are RMBSs.
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The first variable is “CORP_ABS ”, which discriminates corporate bonds from ABSs; it is equal to 0 if the security is issued by a firm and it has value 1 if the note is issued by a securitization transaction. We expect this variable to have a positive coefficient, indicating that Asset-Backed Securities have higher spreads compared to corporate bonds within the same rating class and holding for the other factors. “T1”, “T2” and “T3” are dummy variables as well and are defined as follows: 1, if the bond is indexed to 3 months Euribor T1 = 0, otherwise 1, if the bond is indexed to 3 months Libor T2= 0, otherwise 1, if the bond is indexed to 6 months Euribor T3 = 0, otherwise These dummies eventually capture the differences in spread due to the different index rate of the bond. Rating is also represented by dummy variables, R1, R2, R3, R4: 1, if the bond is rated AA R1 = 0, otherwise 1, if the bond is rated A R2 = 0, otherwise 1, if the bond is rated BBB R3 = 0, otherwise 1, if the bond is rated BB R4 = 0, otherwise In order to avoid the singularity of the estimation matrix, we must exclude from the regression one variable of each dummy- group; for the rating group we chose to eliminate R4. R4 represents the lowest-rating securities in the sample (“BB”) and the coefficients of the remaining dummies must be interpreted as the average difference between spread on, respectively, the bonds rated “AA”, “A” and “BBB” and the spread on the dummy excluded; hence we expect the coefficients of R1, R2 and R3 to be negative and decreasing in their absolute value. We argue that the excess spread of Asset-Backed Securities depends on the rating of each bond, so we include in the regression other four variables in order to capture the differences in 10
excess spread due to the rating class of the security: CORPABS_R1, CORPABS_R2, CORPABS_R3, CORPABS_R4. They are constructed, respectively, as the variable “CORP_ABS” times R1, R2, R3, R4; excluding CORPABS_R4 from the regression, we expect CORPABS_R1-2-3, to be statistically significant and to have negative coefficient, decreasing in their absolute value, as for R1, R2, R3. Another explanatory variable is “SIZE”, i.e. the size of the issue expressed as thousands of Euro. The greater the size, the greater the liquidity in the market, so the lower the spread should be. So we expect the coefficient of “size” to be negative, as found in Maris and Segal (2002). Another explanatory variable is “MATURITY” which is defied as follows: for corporate bonds it represents the fraction of year which elapses from the issue date to the maturity date; for ABS it represents the Weighted Average Life (WAL). WAL is the average number each dollar of unpaid principal due on the mortgage remains outstanding; it is computed as the weighted average time to the receipt of all future cash flows weights the dollar amounts of the principal pay-downs. WAL is generally used by market operators while dealing with ABSs and other structured- finance products, hence also in this work WAL is used. The longer the term to maturity, the greater the uncertainty about the future, so the greater the riskiness is. Thus we expect a positive estimated coefficient for “maturity”. “NWMKT1-2-3-4-5” is a group of dummies which discriminate securities considering the issuing quarter of the year. NWMKT1 is equal to 1 if the security was issued in April, May or June 2001; 0 otherwise. NWMKT2 is equal to 1 if the issue date of the bond is July, August or September 2001; 0 otherwise. NWMKT3 equals 1 if the bond was issued in the forth quarter of 2001; 0 otherwise. NWMKT4 and NWMKT5 are equal to 1 if the bond was issued, respectively, in the first or second quarter of 2002; they are zero otherwise. In this way we can capture also non- linear relationships between time and spread. The variable is zero for all corporate bonds. NWMKT1 is not included in the regression in order to avoid the singularity of the estimation matrix, hence the coefficients of NWMKT2-3-4-5 must be interpreted as average spreads of bonds issued, respectively, in the third, forth quarter of 2001 and first or second quarter of 2002 in excess compared to the excluded category’s spread. The later a securities was issued, the lower the spread on it should be, because investors should have acquired a higher confidence with ABSs evaluation and so they should ask for lower spreads. Therefore we expect a negative coefficient for “Nwmkt1-5” (which means that, for example,
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a bond issued in the second quarter of 2002 should have lower spreads compared to securities issued on the second quarter of 2001 – that is the excluded category). The estimated equation is: SPREADi = β 0 + β1 ⋅ CORP _ ABSi + β 2 ⋅ T1i + β 3 ⋅ T 2 i + β4 ⋅ R1i + β5 ⋅ R2 i + β6 ⋅ R3i
(1)
+ β7 ⋅ CORPABS_ R1i + β 8 ⋅ CORPABS_ R2 i + β9 ⋅ CORPABS _ R3i + β10 ⋅ SIZEi + β11 ⋅ SCADENZAi + β12 ⋅ NWMKT 21 + β13 ⋅ NWMKT 3i + β14 ⋅ NWMKT4 i + β15 ⋅ NWMKT 5i + ε
where i indicates the i- th security. T3, R4, CORPABS_R4 and NWMKT1 are excluded from equation 1 to avoid the estimation matrix to become singular.
3.5. Results
Results are shown in table 1. The first column reports the explanatory variables. The second column shows the estimated coefficient for each variable. Standard error is reported in column three and column four exhibits the t-statistics (calculated as the ratio of the estimated coefficient and the related standard error). The last column displays the p-value; the last two rows report R2 and its adjusted version. We can first note that the regression explain almost 68% of the excess spread while remaining 32% is not captured by the variables. From table 1 we can first notice that R1, R2 and R3 are highly significant and their coefficients has the expected features: they are negative and decreasing in absolute value when rating worsens. So the results indicate that bonds rated “AA” have spreads 254 b.p. higher on average than bonds rated “BB”; similar arguments hold for R2 and R3. The coefficient of CORPABS_R1 represents the average excess spread between AA-rated Asset-Backed Securities and AA-rated corporate bonds compared to the category excluded (bonds rated “BB”), holding for all the other conditions; similarly, the coefficients of CORPABS_R2 and CORPABS_R3 represent the average spread, respectively, between Arated ABSs and A-rated corporate bonds and between BBB-rated Asset-Backed Securities and BBB-rated corporate bonds, holding for the other conditions. All these coefficients are statistically significant and have the expected signs. This does not necessarily imply that for all rating categories ABSs have higher spreads compared to corporate bonds; in order to
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verify whether the excess spread holds for all rating groups, we employed three coefficient tests. The null hypothesis are: TEST 1 → H0 : β 1 + β7 = 0; TEST 2 → H0 : β 1 + β8 = 0; TEST 3 → H0 : β 1 + β9 = 0. If we accept the null hypothesis for the first test, we can conclude that AA-rated ABSs have not higher average spreads than AA-rated corporate bonds. On the opposite rejecting H0 for test 1 implies to confirm the excess spread between ABSs and corporate bonds for AA-rating category. Similar arguments hold for test 2 and 3, that is for “A” and “BBB” groups, respectively. Therefore if we accept H0 for all the tests we can conclude that the overall average excess spread is entirely determined by the average excess spread on BB-rated securities. We reject H0 , at a 5% significance level, if the p-value is lower than 0.05. The results of the tests show that p-value is 0.378 for the first test; 0.066 for the second test; 0.000 for the third test. Therefore we can conclude that the excess spread does exists only for securities rated “BBB” (and for BB-rated bonds), but evidence suggest that for the groups “AA” and “A” Asset-Backed Securities have not higher spreads than corporate bonds. The size and the maturity of the bonds do not seem to influence the spread, since the coefficients of the two variable are not significant. Moreover T1 is not statistically significant, while T2 is weakly significant; its coefficient is negative, indicating that 3-months-Libor bonds have lower spreads compared to 6- month- Euribbor (represented by T3, i.e. the excluded dummy). The results do not provide evidence for a time dependence of the spreads: only the coefficients for NWMKT3 and NWMKT5 are significant 8 and the value of the coefficients of entire group does not show a clear positive or negative trend. This result contrast with the one obtained by Maris and Segal (2002) with US data. A potential explanation is that the European market is new and so only after five or ten years we could expect to observe this effect. It might be interesting to evidence how important each regressor is in determining the excess spread. To do so we can estimate again equation (1), excluding one variable and repeat this procedure as many times as the number of the variables. In such a way we can see how much the R2 reduces when a variable is not included in the regression; the lower the R2 , the more
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important the excluded variable is. Figure 3 shows the difference between R2 in the total regression and R2 in the j-1 regression. It is easy to notice that rating, corp_abs and corp_abs*R1-2-3 are the most important variables.
Table 1: Estimated coefficients for equation (1). The regression has “SPREAD” (in basis points) as dependent variable, while the following variables are introduced as explanatory variables. “CORP_ABS” discriminates corporate bonds from ABSs; it is equal to 0 if the security is issued by a firm and it has value 1 if the note is issued by a securitization transaction. T1 is equal to 1 if the bond is idexed to 3-months Euribor; 0 otherwise. T2 is equal to 1 if the security is idexed to 3-months Libor; 0 otherwise. R1 identifies AA-rated bonds; R2 represents A-rated securities and R3 indeicates BBB-rated bonds. CORPABS_R1 is equal to CORP_ABS*R1; CORPABS_R2 is equal to CORP_ABS*R2; CORPABS_R3 is equal to CORP_ABS*R3. “NWMKT1-2-3-4-5” is a group of dummies which discriminate securities considering the issuing quarter of the year. NWMKT2 identifies bonds issued in the third quarter of 2001; NWMKT3 represents bonds issued in the forth quarter of 2001; NWMKT4 identifies securities issued in the first quarter of 2002; NWMKT5 indicates bonds issued in the second quarter of 2002. “SIZE” is the size of the issue expressed as thousands of Euro. “MATURITY” is defied as follows: for corporate bonds it represents the fraction of year which elapses from the issue date to the maturity date; for ABS it represents the Weighted Average Life (WAL).
Variable C CORP_ABS T1 T2 R1 R2 R3 CORPABS_R1 CORPABS_R2 CORPABS_R3 SIZE SCADENZA NWMKT2 NWMKT3 NWMKT4 NWMKT5 R-squared Adjusted Rsquared
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Coefficient 134.9580* 283.0333* -16.04991 -28.65601* -101.3001* -91.99868* -60.11504* -267.5954* -248.2788* -181.0191* -2.03E -05 0.460873 25.87085 35.55286* 15.93258 22.74627*
Std. Error 19.89508 24.36342 8.642011 11.52716 23.04386 23.85681 21.98258 27.20790 27.97245 26.53661 1.29E-05 0.859767 14.26337 12.04815 13.29380 12.04655 0.683204 0.672101
NWMKT5 is weakly significant.
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Prob. 0.0000 0.0000 0.0640 0.0133 0.0000 0.0001 0.0065 0.0000 0.0000 0.0000 0.1157 0.5922 0.0704 0.0033 0.2314 0.0597
Figure 3: Marginal contribute of each explanatory variable.
Nwmkt Maturity Size Corp_abs_ R1-2-3 Rating Index-rate corp_abs 0
0,02
0,04
0,06
0,08
0,1
3.6. The subsamples: classical ABSs, CDOs and RMBSs.
It might be worth to analyse what happens if we regress only selected types of Asset-Backed Securities and corporate bonds in order to understand whether the type of ABSs influence credit spread; this is exactly what we do in this section. Before presenting the regression and the results, it is worth to say that not all the types of Asset-Backed Securities are characterised by the same level of “newness”. In fact RMBS are the first type of securities issued by a securitization transaction in the United States in the mid 1970s, so investors should have acquired more experience and confidence with this kind of bonds and less with more recently created products of the structured finance. A similar argument holds for “classical” ABSs, such as the notes issued by securitization of leasing, auto loans, receivables, etc. So we now try to regress separately “classical” ABSs, CDOs and RMBSs with corporate bonds; finally we compare “classical” ABSs and RMBSs.
3.6.1 “Classical” ABSs
The first sub-sample analysed is “classical” ABSs. The observations include 176 bonds, among which 97 are corporate and the remaining 79 are Asset-Backed Securities. The estimated equation is equation (1), as the regression implemented in section 3.4. Thus considerations about the explanatory variables are the same as in section 3.4. Moreover similar consideration as in section 3.5 can be made for almost all the variables. In fact the
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greatest differences between table 1 and table 2 deal with the variables “NWMKT2-3-4-5”: none of these are statistically significant. Three tests similar to the ones exposed in section 3.5 are implemented for the sub-sample “classical” ABSs. The results are: p-value equal to 0.1132 for test one; p- value equal to 0.0176 for test 2; p-value equal to 0 for the third test. Therefore we can reject the null hypothesis for two of the rating categories – “A” and “BBB” – and conclude that evidence provides support for the existence of a positive excess spread between ABSs and corporate bonds for these two groups. On the other hand the first test makes us argue that “AA” group has not an excess spread between the two types of bonds.
Table 2: Estimated coefficients for equation (1) for the subsample “classical” ABSs. The regression has “SPREAD” (in basis points) as dependent variable, while the following variables are introduced as explanatory variables; only “classical” ABSs and corporate bonds are included in the analysis. “CORP_ABS” discriminates corporate bonds from ABSs; it is equal to 0 if the security is issued by a firm and it has value 1 if the note is issued by a securitization transaction. T1 is equal to 1 if the bond is idexed to 3-months Euribor; 0 otherwise. T2 is equal to 1 if the security is idexed to 3-months Libor; 0 otherwise. R1 identifies AArated bonds; R2 represents A-rated securities and R3 indicates BBB-rated bonds. CORPABS_R1 is equal to CORP_ABS*R1; CORPABS_R2 is equal to CORP_ABS*R2; CORPABS_R3 is equal to CORP_ABS*R3. “NWMKT1-2-3-4-5” is a group of dummies which discriminate securities considering the issuing quarter of the year. NWMKT2 identifies bonds issued in the third quarter of 2001; NWMKT3 represents bonds issued in the forth quarter of 2001; NWMKT4 identifies securities issued in the first quarter of 2002; NWMKT5 indicates bonds issued in the second quarter of 2002. “SIZE” is the size of the issue expressed as thousands of Euro. “MATURITY” is defied as follows: for corporate bonds it represents the fraction of year which elapses from the issue date to the maturity date; for ABS it represents the Weighted Average Life (WAL).
Variable Coefficient Std. Error C 131.0300* 18.35444 CORP_ABS 467.3207* 59.52855 T1 -15.24489 11.60228 T2 -39.69134* 17.49371 R1 -98.26075* 19.02326 R2 -88.97596* 19.29172 R3 -60.49496* 17.43731 CORPABS_R1 -432.3629* 59.85071 CORPABS_R2 -415.3444* 59.13890 CORPABS_R3 -340.4186* 59.92905 SIZE -2.03E -0 1.16E-05 MATURITY 1.589274 1.805145 NWMKT2 28.87435 29.83386 NWMKT3 -1.134483 17.88864 NWMKT4 31.82091 28.45909 NWMKT5 -20.90140 19.92264 R-squared 0.614526 Adjusted R-squared 0.578387
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Prob. 0.0000 0.0000 0.1907 0.0246 0.0000 0.0000 0.0007 0.0000 0.0000 0.0000 0.0802 0.3800 0.3346 0.9495 0.2652 0.2957
3.6.2 RMBSs
In this section 127 RMBSs and 97 corporate bonds are analysed; the regression equation is the same used in section 3.4 and 3.6.1. The results, reported in table 3, are similar as well; in particular none of the variables “NWMKT” is statistically significant. “Size”, “Maturity”, T1 and T2 are not significant while “CORP_ABS”, “R1-2-3” and “CORPRMBS_R1-2-3” are highly significant. Interpretation of the coefficients of these variables is similar to that exposed in section 3.5. Table 3: Estimated coefficients for equation (1) for the subsample RMBSs. The regression has “SPREAD” (in basis points) as dependent variable, while the following variables are introduced as explanatory variables; only RMBSs and corporate bonds are included in the analysis. “CORP_ABS” discriminates corporate bonds from ABSs; it is equal to 0 if the security is issued by a firm and it has value 1 if the note is issued by a securitization transaction. T1 is equal to 1 if the bond is idexed to 3-months Euribor; 0 otherwise. T2 is equal to 1 if the security is idexed to 3-months Libor; 0 otherwise. R1 identifies AArated bonds; R2 represents A-rated securities and R3 indicates BBB-rated bonds. CORPABS_R1 is equal to CORP_ABS*R1; CORPABS_R2 is equal to CORP_ABS*R2; CORPABS_R3 is equal to CORP_ABS*R3. “NWMKT1-2-3-4-5” is a group of dummies which discriminate securities considering the issuing quarter of the year. NWMKT2 identifies bonds issued in the third quarter of 2001; NWMKT3 represents bonds issued in the forth quarter of 2001; NWMKT4 identifies securities issued in the first quarter of 2002; NWMKT5 indicates bonds issued in the second quarter of 2002. “SIZE”is the size of the issue expressed as thousands of Euro. “MATURITY” is defied as follows: for corporate bonds it represents the fraction of year which elapses from the issue date to the maturity date; for ABS it represents the Weighted Average Life (WAL).
Variable C CORP_RMBS T1 T2 R1 R2 R3 CORPRMBS_R1 CORPRMBS_R2 CORPRMBS_R3 SIZE SCADENZA NWMKT2 NWMKT3 NWMKT4 NWMKT5 R-squared Adjusted R-squared
Coefficient 104.5666 214.2087 15.61416 24.55106 -117.2402 -105.1654 -64.21690 -195.3918 -178.8856 -137.5288 6.22E-06 1.123577 18.60655 17.19208 19.13442 11.52927
17
Std. Error 19.81821 29.53675 16.29520 18.15593 19.66516 20.19932 18.33223 31.22313 31.92824 30.10416 2.49E-05 0.884523 17.22704 15.48876 17.16347 15.77723 0.618956 0.591477
Prob. 0.0000 0.0000 0.3391 0.1778 0.0000 0.0000 0.0006 0.0000 0.0000 0.0000 0.8025 0.2054 0.2814 0.2683 0.2662 0.4658
The coefficient tests were carried out in the way already explained in the previous sections. Pvalues for the first and second tests are, respectively, 0.3159 and 0.0698, which make us accept the null hypothesis. The third test is characterised by a p- value equal to 0.00002, implying that the null hypothesis is rejected. So we can again conclude that the existence of the excess spread is verified only for lower-rated bonds. 3.6.3 CDOs
The last sub-sample that has been analysed in this paper is “CDOs”. The sample includes 141 CDOs and 97 corporate bonds. The estimation output is summarised in table 4. As we can easily notice, the results are the same as the previous analysis except for the regressors “NWMKT”. In fact three of the four variables in the group are significant, providing evidence for a relation between spread and time. However, as underlined in section 3.5, the coefficients of these variables does not show a clear trend either positive nor negative. The coefficient tests again support the idea that the difference in spread between securities issued in a securitization transaction and corporate bonds does exists only for low-rated securities 9 .
Table 4: Estimated coefficients for equation (1) for the subsample CDOs. The regression has “SPREAD” (in basis points) as dependent variable, while the following variables are introduced as explanatory variables; only CDOs and corporate bonds are included in the analysis. “CORP_ABS” discriminates corporate bonds from ABSs; it is equal to 0 if the security is issued by a firm and it has value 1 if the note is issued by a securitization transaction. T1 is equal to 1 if the bond is idexed to 3-months Euribor; 0 otherwise. T2 is equal to 1 if the security is idexed to 3-months Libor; 0 otherwise. R1 identifies AA-rated bonds; R2 represents A-rated securities and R3 indicates BBB-rated bonds. CORPABS_R1 is equal to CORP_ABS*R1; CORPABS_R2 is equal to CORP_ABS*R2; CORPABS_R3 is equal to CORP_ABS*R3. “NWMKT1-2-3-4-5” is a group of dummies which discriminate securities considering the issuing quarter of the year. NWMKT2 identifies bonds issued in the third quarter of 2001; NWMKT3 represents bonds issued in the forth quarter of 2001; NWMKT4 identifies securities issued in the first quarter of 2002; NWMKT5 indicates bonds issued in the second quarter of 2002. “SIZE” is the size of the issue expressed as thousands of Euro. “MATURITY” is defied as follows: for corporate bonds it represents the fraction of year which elapses from the issue date to the maturity date; for ABS it represents the Weighted A verage Life (WAL).
Variable C CORP_CDO T1 T2 R1
Coefficient 113.2030 253.5689 12.38504 -12.99174 -105.1435
9
Std. Error Prob. 26.26348 0.0000 33.71327 0.0000 13.41699 0.3570 24.81054 0.6011 27.11888 0.0001
In fact the first and second tests have a p-value equal to 0.5404, while the p-value of the third one is nearly zero (0.0009).
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R2 R3 CORPCDO_R1 CORPCDO_R2 CORPCDO_R3 SIZE SCADENZA NWMKT2 NWMKT3 NWMKT4 NWMKT5 R-squared Adjusted R-squared
-97.75966 -64.40704 -272.0706 -234.3959 -158.2171 -1.66E -05 1.138019 58.97878 91.84043 40.00582 64.34544
27.23533 24.88104 33.59340 35.03763 32.97694 3.30E-05 2.132919 29.42327 25.70135 25.73275 24.70764 0.721617 0.702807
0.0004 0.0103 0.0000 0.0000 0.0000 0.6144 0.5942 0.0462 0.0004 0.1215 0.0098
3.6.4 RMBS and CABS: a comparision
In the previous sections corporate bonds were compared to securities issued by securitizatio n transactions; we would like now to investigate whether and how an excess spread exists between RMBSs and “classical” ABSs and/or between RMBSs and CDOs. In fact, as said above, RMBSs were the first type of Asset-Backed Securities to be issued, so we might ague that RMBSs have lower spreads than “classical” ABSs and in particular lower spreads than CDOs – this would confirm that the “newness” of the security does matter for the spread required by the market. Hence, the first analysis comprises 127 RMBSs and 79 “classical” ABSs. The explanatory variables are the same as in equation (1), however “RMBS_ABS” is defined in a somewhat different way: it is equal to zero if the bond is a RMBS, while it is equal to 1 if the security is a “classical” ABS. In such a way the variable discriminates RMBSs from “classical” ABSs and its coefficient gives a measure of the average excess spread – if it exists – between the two types of securities. Estimation output is reported by table 5. Results suggest that there is an excess spread between RMBSs and “classical” ABSs, since the coefficient of RMBS_ABS is positive and statistically significant. Moreover RMBSABS_R1-2-3 are significant as well, therefore, as in the previous sections, three coefficient tests are run. The results make us accept the null hypothesis in all the case, meaning that for the rating groups “AA”, “A”, and “BBB” the excess spread is not statistically significant. We can, thus, conclude that the excess spread only exists for lower rated bonds – labelled “BB” – which are the excluded category and are incorporated in the coefficient of RMBS_ABS. NWMKT2-3-4-5 are not statistically 19
significant, implying that spread does not seem to be affected by the issue date of the bond. Surprisingly “SIZE” is weakly significant and has a negative estimated coefficient; this means that the market requires slightly lower spreads for bonds issued in a large size.
Table 5: Estimated coefficients for equation (1) for RMBSs and “classical” ABSs. The regression has “SPREAD” (in basis points) as dependent variable, while the following variables are introduced as explanatory variables; only RMBSs and “classical” ABSs are included in the analysis. “CORP_ABS” discriminates RMBSs from “classical” ABSs; it is equal to 0 if the security is issued by a firm and it has value 1 if the note is issued by a securitization transaction. T1 is equal to 1 if the bond is idexed to 3months Euribor; 0 otherwise. T2 is equal to 1 if the security is idexed to 3-months Libor; 0 otherwise. R1 identifies AA-rated bonds; R2 represents A-rated securities and R3 indicates BBB-rated bonds. CORPABS_R1 is equal to CORP_ABS*R1; CORPABS_R2 is equal to CORP_ABS*R2; CORPABS_R3 is equal to CORP_ABS*R3. “NWMKT1-2-3-4-5” is a group of dummies which discriminate securities considering the issuing quarter of the year. NWMKT2 identifies bonds issued in the third quarter of 2001; NWMKT3 represents bonds issued in the forth quarter of 2001; NWMKT4 identifies securities issued in the first quarter of 2002; NWMKT5 indicates bonds issued in the second quarter of 2002. “SIZE” is the size of the issue expressed as thousands of Euro. “MATURITY” is defied as follows: for corporate bonds it represents the fraction of year which elapses from the issue date to the maturity date; for ABS it represents the Weighted Average Life (WAL). Variable C RMBS_ABS T1 T2 R1 R2 R3 RMBSABS_R1 RMBSABS_R2 RMBSABS_R3 SIZE MATURITY NWMKT2 NWMKT3 NWMKT4 NWMKT5
Coefficient 371.3593* 215.9488* -43.97834* -19.75294 -311.0533* -278.3029* -199.2846* -196.7430* -205.1178* -187.8437* -2.59E -05* 1.022973 15.96922 11.14841 24.08966* 6.075512
R-squared Adjusted R-squared
Std. Error 23.69816 52.98593 11.02857 14.39495 20.35141 20.42743 19.86948 53.73863 53.23982 53.98761 1.07E-05 0.755400 12.51811 10.05103 12.37170 10.67574
Prob. 0.0000 0.0001 0.0001 0.1716 0.0000 0.0000 0.0000 0.0003 0.0002 0.0006 0.0161 0.1773 0.2036 0.2688 0.0530 0.5700
0.733454 0.712410
In the second analysis 127 RMBSs are compared to 141 CDOs. The estimated equation is the same as in the previous sections – equation (1) – , however the variable “RMBS_CDO” is defined in a way similar to “RMBS_ABS”: it is equal to zero if the bond is a RMBS, while it is equal to 1 if the security is a CDO. Table 6 shows that the estimated coefficient for “RMBS_CDO” is positive and statistically significant, indicating that CDOs have higher 20
spreads than RMBSs. Moreover, since “RMBSCDO_R1-2-3” are not significant, meaning that the distinction between the rating class is not important. Only “NWMKT3” and “NWMKT5” are significant in the group and the coefficients do not suggest a linear trend or relation between time and spread.
Table 6: Estimated coefficients for equation (1) for RMBSs and CDOs. The regression has “SPREAD” (in basis points) as dependent variable, while the following variables are introduced as explanatory variables; only RMBSs and CDOs are included in the analysis. “CORP_ABS” discriminates RMBSs from “classical” ABSs; it is equal to 0 if the security is issued by a firm and it has value 1 if the note is issued by a securitization transaction. T1 is equal to 1 if the bond is idexed to 3-months Euribor; 0 otherwise. T2 is equal to 1 if the security is idexed to 3-months Libor; 0 otherwise. R1 identifies AA-rated bonds; R2 represents A-rated securities and R3 indicates BBB-rated bonds. CORPABS_R1 is equal to CORP_ABS*R1; CORPABS_R2 is equal to CORP_ABS*R2; CORPABS_R3 is equal to CORP_ABS*R3. “NWMKT1-2-3-4-5” is a group of dummies which discriminate securities considering the issuing quarter of the year. NWMKT2 identifies bonds issued in the third quarter of 2001; NWMKT3 represents bonds issued in the forth quarter of 2001; NWMKT4 identifies securities issued in the first quarter of 2002; NWMKT5 indicates bonds issued in the second quarter of 2002. “SIZE” is the size of the issue expressed as thousands of Euro. “MATURITY” is defied as follows: for corporate bonds it represents the fraction of year which elapses from the issue date to the maturity date; for ABS it represents the Weighted Average Life (WAL).
Variable C RMBS_CDO T1 T2 R1 R2 R3 RMBSCDO_R1 RMBSCDO_R2 RMBSCDO_R3 SIZE MATURITY NWMKT2 NWMKT3 NWMKT4 NWMKT5
Coefficient 305.3464 91.25229 8.924266 41.61667 -318.6903 -281.3745 -208.8080 -54.75642 -48.20427 -11.50572 -7.81E -05 1.010831 29.97517 53.29757 23.10410 33.68463
R-squared Adjusted R-squared
Std. Error 35.12639 33.32196 13.43523 20.40530 30.98450 31.07735 30.19034 36.86192 37.60038 36.43251 7.99E-05 1.075773 16.94129 15.32262 15.89752 14.92815
t-Statistic 8.692791 2.738504 0.664244 2.039503 -10.28547 -9.054007 -6.916384 -1.485447 -1.282016 -0.315809 -0.977630 0.939632 1.769355 3.478359 1.453314 2.256451
0.715290 0.698343
4. Conclusion
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Prob. 0.0000 0.0066 0.5071 0.0424 0.0000 0.0000 0.0000 0.1387 0.2010 0.7524 0.3292 0.3483 0.0780 0.0006 0.1474 0.0249
In this paper credit spread on corporate bonds and on Asset-Backed Securities are compared. A descriptive analysis shows that ABSs have higher spread than corporate bonds; we called “excess spread” the difference between spread on ABSs and spread on corporate bonds. Further analysis was implemented, using linear regression method. However the presence of excess spread is confirmed only for low rated securities, while for the categories “AA” and “A” Asset-Backed Securities do not seem to have statistically significant higher spreads than bonds issued by firms. This means that the market requires the same remuneration to invest in a bond of a single firm and in a bond on a portfolio with the same rating, if this rating is high. On the contrary, the market requires a different remuneration if the rating is low. The reasons for this are many and in particular: (i) the difficulty of evaluating a securitization transaction; (ii) the presence of informational problems and (iii) the lack of experience of the market. In particular, the asymmetries of information generated by a portfolio are different than the one generated by a single firm. In the paper we considered both (i) RMBS and (ii) CABS and this phenomenon is confirmed for the retail as for the commercial portfolios. A potential interpretation of this result is that the market do not consider as different assets Retail MBS or Commercial ABS if the rating is high. However, our analysis shows that the market requires a higher return on Commercial ABS with respect to RMBS when the rating is low. The reason of this result could be the higher development of RMBS market and the higher expertise on risk measurement of retail portfolios. Moreover we argued that part of the excess spread could be due to the fact that ABSs are a new product in the European market, so investors could require a higher spread because they are not confident with this kind of product. The required spread should lower as investors gain familiarity with ABSs; a similar argument is found by Maris and Segal (2002). However the relation between spread and time is not supported by empirical evidence provided in this paper. Even if we regress spread separately for each ABSs type, we find no evidence of a time dependence for spread. To be more precise, most of the variables that should capture the timefactor for the sub-sample “CDOs” are significant, but a clear relation between time and spread cannot be found. So we must conclude that evidence does not support the idea that required spread on ABSs gets lower as the “learning process” is going to complete, in other words we are not able to assert that investors ask for lower spreads on ABSs when they become more familiar with this product.
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BNP PARIBAS (2002) “European ABS 2001 Second Quarter Review”, Report
BNP PARIBAS (2002) “European ABS 2001 Third Quarter Review”, Report
BNP PARIBAS (2002) “European ABS 2002 First Quarter Review”, Report
BNP PARIBAS (2002) “European ABS 2002 Second Quarter Review”, Report
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