Bank reputation and securitization quality:European evidence

We examine the link between issuer bank reputation and the performance of mortgage-backed securities (MBS) in the European market. We find that MBS sold by reputable issuer banks are collateralised by higher quality asset pools with lower delinquency rates and are less likely to be downgraded. However, during boom periods – characterized by declining credit standards, MBS originated by reputable issuer banks tend to be collateralised by lower quality assets, compared to normal periods

Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

Trustee reputation in securitization:When does it matter?

We consider the role of trustees –who are nominated to protect the interests of investors– in securitization pricing and whether investors rely on them to mitigate risks. In particular, we examine the effect of trustee reputation on initial yield spreads of European mortgage-backed security (MBS) issuances between 1999 and the first half of 2007. We find that engaging reputable trustees led to lower spreads during the credit boom period prior to the 2007-2009 financial crisis. Our findings suggest that trustees’ reputation was considered by investors to be more important when risk assessment became more challenging

Access to consumer credit in the UK

This paper investigates household access to consumer credit in the UK using information on 58,642 households between 2001 and 2009. Employing a treatment-effects model and propensity score matching, we find that non-white households are less likely to have financing compared to white households. We also find that even if they obtain financing, the intensity of borrowing is lower than for white households. Overall, non-white households seem to be in a weaker position to access consumer credit in the UK

Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in sample estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: non-standard errors. To study them, we let 164 teams test six hypotheses on the same sample. We find that non-standard errors are sizeable, on par with standard errors. Their size (i) co-varies only weakly with team merits, reproducibility, or peer rating, (ii) declines significantly after peer-feedback, and (iii) is underestimated by participants