Gregor N. F. Weiss

Mitigating Adverse Selection in P2P Lending – Empirical Evidence from Prosper.com

This paper presents novel empirical evidence on the success of efforts by P2P lending platforms to limit adverse selection using a unique sample of 5,385 credit transactions on the internet platform Prosper.com. The results of our regressions on the probability of a credit bid’s successful funding show that all variables for which a significant influence on the probability of funding success could be found describe information which is verified by Prosper.com. Conversely, all non-verified variables do not possess any significant influence on the dependent variable thus confirming our hypothesis that the screening of potential borrowers is a major instrument in mitigating adverse selection in P2P lending and preventing the online market to collapse. Moreover, we find evidence confirming the proposition that the screening of potential borrowers by groups can help mitigate adverse selection. Group membership, however, does also seem to have a positive influence on the probability of a credit bid expiring, a novel finding of this study.

Copula Parameter Estimation – Numerical Considerations and Implications for Risk Management

The purpose of this paper is to present a comprehensive simulation study on the finite sample properties of minimum-distance and maximum-likelihood estimators for bivariate and multivariate parametric copulas. For five popular parametric copulas, classical maximum-likelihood is compared to a total of nine different minimum-distance estimators. In particular, I consider CvM-, KS- and L1 variants of the CvM-statistic based on the empirical copula process, Kendalls dependence function and Rosenblatts probability integral transform. The results presented in this paper show that in most settings canonical maximum-likelihood yields smaller estimation biases at less computational effort than any of the MD-estimators. There exist, however, some cases (especially when the sample size increases) where minimum-distance estimators based on the empirical copula process are superior to the ML-estimator. MD-estimators based on Kendalls transform on the other hand yield only suboptimal results in all configurations of the simulation study. The results of the simulation study are confirmed by empirical examples where the Value-at-Risk as well as the Expected Shortfall of 100 bivariate portfolios are computed. Interestingly, the estimates for these risk measures differed considerably depending on the choice of parameter estimator. This result stresses the need for carefully choosing the parameter estimator in contrast to focusing all attention on choosing the parametric copula model.

Smooth Nonparametric Bernstein Vine Copulas

We consider the problem of accurately modelling the distribution of the market risk of a multivariate financial portfolio. We employ a multivariate GARCH model in which the dependence structure between the assets is modelled via a vine copula. We address the problem of how the parametric pair-copulas in a vine copula should be chosen by proposing to use nonparametric Bernstein copulas as bivariate pair-copulas. An extensive simulation study illustrates that our smooth nonparametric vine copula model is able to match the results of a competing parametric vine model calibrated via Akaike’s Information Criterion while at the same time significantly reducing model risk. Our empirical analysis of financial market data demonstrates that our proposed model yields Value-at-Risk forecasts that are significantly more accurate than those of a benchmark parametric model.

Catastrophe Bonds and Systemic Risk

Do catastrophe bonds increase or decrease the exposure and contribution to systemic risk of the issuing insurance companies? And if such issues influence systemic stability, what design features of the bond and characteristics of the issuing insurer cause catastrophe bond issues to destabilize the financial sector? Contrary to current conjectures of insurance regulators, we find that the contribution of ceding insurers to systemic risk actually decreases significantly after the issue of a catastrophe bond. We empirically confirm that a higher pre-issue leverage, a higher firm valuation and previous cat bond issues all exert a decreasing effect on the issuers systemic risk contribution.

Household Sentiment, Market Liquidity, and Credit Default Swap Spreads

We show that household sentiment affects the cross-section and time-series of credit default swap (CDS) spreads. We employ internet search volume data to proxy for an exogenous shock to the pessimistic sentiment of households about credit risk outside the CDS market. Consistent with the conjecture of significant noise trading existing in the CDS market, we find our proxy of household sentiment to be a significant driver of CDS spreads. Our results are in line with theories that emphasize the role noise trading plays in providing market liquidity with higher levels of sentiment being associated with lower average CDS spreads.

Extreme Liquidity Risk and Credit Default Swap Spreads

We show that extreme liquidity risk is a significant driver of credit default swap (CDS) spreads. We capture the extreme liquidity risk of a CDS contract written on a firm by estimating the tail dependence between the bid-ask spread of the firms CDS and the liquidity of a CDS market index. Our results show that sellers of credit protection earn a statistically and economically significant premium for bearing the risk of joint extreme downwards movements in the liquidity of individual CDS contracts and the CDS market. This effect holds in various robustness checks and is particularly pronounced during the financial crisis. Finally, we discuss the implications of our findings for insurers and pension funds that enter the CDS market to take on credit risk.

Systemic Risk, Bank Capital, and Deposit Insurance Around the World

We analyze the effect of bank capital, regulation and deposit insurance on the global systemic risk of international banks during the period of 1999-2012. Using a comprehensive panel of large global banks, we identify factors that influence the build-up of systemic risk worldwide across a large set of heterogeneous regulatory and supervisory schemes. We find that higher Tier 1 capital decreases both the exposure and contribution of individual banks to global systemic risk. We also show that deposit insurance schemes that require banks and depositors to bear more financial risk are associated with a more pronounced vulnerability and contribution of individual banks to a crisis of the financial sector. Further results show that bank size and interconnectedness are positively related and competition is negatively related to global financial fragility. In contrast, we find no convincing evidence that a bank’s supervisory environment or non-interest income significantly influence a bank’s exposure or contribution to systemic risk.

On the Wealth Effects of Merging Banks’ Rivals Empirical Evidence for US Bank Mergers

Abstract: Using a unique sample of 425 bank mergers in the US announced between 2000 and 2008 this paper provides clear evidence supporting the collusion and productive efficiency hypotheses. By analyzing 425 bank mergers and a total of 1112 possible rivals, our analysis shows that the majority of transactions can be explained by at least one abnormal return pattern attributed to either the collusion, productive efficiency, acquisition probability or pre-emptive merger hypothesis. In contrast to previous empirical literature starting with Eckbo (1983), however, we find evidence that wealth effects of the rivals of merging banks can be explained best by the collusion and productive efficiency hypotheses. Methodically, we extend previous approaches considerably by proposing a rivalry index incorporating balance sheet data as well as firm characteristics in order to identify the merging banks’ rivals. Empirical results of our main analysis also hold when performing several robustness checks. Using random scaling factors, we find that our main results are not sensitive to a change in the scaling factors that determine our rivalry index. Moreover, our main results are robust to a change of the method for computing abnormal returns.