An Empirical Analysis of Asset-Backed Securitization

In this study we provide empirical evidence demonstrating a relationship between the nature of the assets and the primary market spread. The model also provides predictions on how other pricing characteristics affect spread, since little is known about how and why spreads of asset-backed securities are influenced by loan tranche characteristics. We find that default and recovery risk characteristics represent the most important group in explaining loan spread variability. Within this group, the credit rating dummies are the most important variables to determine loan spread at issue. Nonetheless, credit rating is not a sufficient statistic for the determination of spreads. We find that the nature of the assets has a substantial impact on the spread across all samples, indicating that primary market spread with backing assets that cannot easily be replaced is significantly higher relative to issues with assets that can easily be obtained. Of the remaining characteristics, only marketability explains a significant portion of the spreads’ variability. In addition, variations of the specifications were estimated in order to asses the robustness of the conclusions concerning the determinants of loan spreads.asset securitization; asset-backed securitisation; bank lending; default risk; risk management; leveraged financing

ABS, MBS and CDO compared: an empirical analysis

The capital market in which asset-backed securities are issued and traded is composed of three main categories: ABS, MBS and CDOs. We were able to examine a total number of 3,466 loans (worth €548.85 billion) of which 1,102 (worth €163.90 billion) have been classified as ABS. MBS issues represent 1,782 issues (worth €320.83 billion), and 582 are CDO issues (worth €64.12 billion). We have investigated how common pricing factors compare for the main classes of securities. Due to the differences in the assets related to these securities, the relevant pricing factors for these securities should differ, too. Taking these three classes as a whole, we have documented that the assets attached as collateral for the securities differ between security classes, but that there are also important univariate differences to consider. We found that most of the common pricing characteristics between ABS, MBS and CDO differ significantly. Furthermore, applying the same pricing estimation model to each security class revealed that most of the common pricing characteristics associated with these classes have a different impact on the primary market spread exhibited by the value of the coefficients. The regression analyses we performed suggest that ABS, MBS and CDOs are in fact different instruments, as implied by the differences in impact of the pricing factors on the loan spread between these security classes.asset securitization; asset-backed securitisation; bank lending; default risk; risk management; spreads; leveraged financing

ABS, MBS and CDO compared: an empirical analysis

The capital market in which the asset-backed securities are issued and traded is composed of three main categories: ABS, MBS and CDOs. We were able to examine a total number of 3,951 loans (worth €730.25 billion) of which 1,129 (worth €208.94 billion) have been classified as ABS. MBS issues represent 2,224 issues (worth €459.32 billion) and 598 are CDO issues (worth €61.99 billion). We have investigated how common pricing factors compare for the main classes of securities. Due to the differences in the assets related to these securities, the relevant pricing factors for these securities should differ, too. Taking these three classes as a whole, we have documented that the assets attached as collateral for the securities differ between security classes, but that there are also important univariate differences to consider. We found that most of the common pricing characteristics between ABS, MBS and CDO differ significantly. Furthermore, applying the same pricing estimation model to each security class revealed that most of the common pricing characteristics associated with these classes have a different impact on the primary market spread exhibited by the value of the coefficients. The regression analyses we performed demonstrated econometrically that ABS, MBS, and CDOs are in fact different financial instruments. Top 10 Downloaded Papers for Theory: Pricing and Top 10 Downloaded Papers for Journal of Monetary Economics

ABS, MBS and CDO compared: an empirical analysis

The capital market in which asset-backed securities are issued and traded is composed of three main categories: ABS, MBS and CDOs. We were able to examine a total number of 3,466 loans (worth €548.85 billion) of which 1,102 (worth €163.90 billion) have been classified as ABS. MBS issues represent 1,782 issues (worth €320.83 billion), and 582 are CDO issues (worth €64.12 billion). We have investigated how common pricing factors compare for the main classes of securities. Due to the differences in the assets related to these securities, the relevant pricing factors for these securities should differ, too. Taking these three classes as a whole, we have documented that the assets attached as collateral for the securities differ between security classes, but that there are also important univariate differences to consider. We found that most of the common pricing characteristics between ABS, MBS and CDO differ significantly. Furthermore, applying the same pricing estimation model to each security class revealed that most of the common pricing characteristics associated with these classes have a different impact on the primary market spread exhibited by the value of the coefficients. The regression analyses we performed suggest that ABS, MBS and CDOs are in fact different instruments, as implied by the differences in impact of the pricing factors on the loan spread between these security classes

An Empirical Analysis of Asset-Backed Securitization

In this study we provide empirical evidence demonstrating a relationship between the nature of the assets and the primary market spread. The model also provides predictions on how other pricing characteristics affect spread, since little is known about how and why spreads of asset-backed securities are influenced by loan tranche characteristics. We find that default and recovery risk characteristics represent the most important group in explaining loan spread variability. Within this group, the credit rating dummies are the most important variables to determine loan spread at issue. Nonetheless, credit rating is not a sufficient statistic for the determination of spreads. We find that the nature of the assets has a substantial impact on the spread across all samples, indicating that primary market spread with backing assets that cannot easily be replaced is significantly higher relative to issues with assets that can easily be obtained. Of the remaining characteristics, only marketability explains a significant portion of the spreads’ variability. In addition, variations of the specifications were estimated in order to asses the robustness of the conclusions concerning the determinants of loan spreads

An Empirical Analysis of Asset-Backed Securitization

In this study we provide empirical evidence demonstrating a relationship between the nature of the assets and the primary market spread. The model also provides predictions on how other pricing characteristics affect spread, since little is known about how and why spreads of asset-backed securities are influenced by loan tranche characteristics. We find that default and recovery risk characteristics represent the most important group in explaining loan spread variability. Within this group, the credit rating dummies are the most important variables to determine loan spread at issue. Nonetheless, credit rating is not a sufficient statistic for the determination of spreads. We find that the nature of the assets has a substantial impact on the spread across all samples, indicating that primary market spread with backing assets that cannot easily be replaced is significantly higher relative to issues with assets that can easily be obtained. Of the remaining characteristics, only marketability explains a significant portion of the spreads’ variability. In addition, variations of the specifications were estimated in order to asses the robustness of the conclusions concerning the determinants of loan spreads

ABS, MBS and CDO compared: an empirical analysis

The capital market in which asset-backed securities are issued and traded is composed of three main categories: ABS, MBS and CDOs. We were able to examine a total number of 3,466 loans (worth €548.85 billion) of which 1,102 (worth €163.90 billion) have been classified as ABS. MBS issues represent 1,782 issues (worth €320.83 billion), and 582 are CDO issues (worth €64.12 billion). We have investigated how common pricing factors compare for the main classes of securities. Due to the differences in the assets related to these securities, the relevant pricing factors for these securities should differ, too. Taking these three classes as a whole, we have documented that the assets attached as collateral for the securities differ between security classes, but that there are also important univariate differences to consider. We found that most of the common pricing characteristics between ABS, MBS and CDO differ significantly. Furthermore, applying the same pricing estimation model to each security class revealed that most of the common pricing characteristics associated with these classes have a different impact on the primary market spread exhibited by the value of the coefficients. The regression analyses we performed suggest that ABS, MBS and CDOs are in fact different instruments, as implied by the differences in impact of the pricing factors on the loan spread between these security classes

Instance-level explanations for fraud detection (poster)

Fraud detection is a difficult problem that can benefit from predictive modeling. However, the verification of a prediction is challenging; for a single insurance policy, the model only provides a prediction score. We present a case study where we reflect on different instance-level model explanation techniques to aid a fraud detection team in their work. To this end, we designed two novel dashboards combining various state-of-the-art explanation techniques. These enable the domain expert to analyze and understand predictions, dramatically speeding up the process of filtering potential fraud cases