We consider the problem of maximizing the out-of-sample Sharpe ratio when portfolio weights have to be estimated. We apply an improved bootstrap-based estimator, and an approximative estimator derived from a Taylor series. In a simulation study and empirical analysis with 15 datasets the proposed estimators outperform the minimum variance and equally weighted portfolio strategies. Out-of-sample Sharpe ratios improve by 15 and 32 percent on average, respectively, in the empirical analysis. While effectively dealing with estimation risks, the estimators produce considerable amounts of turnover. Realized net Sharpe ratios improve by 40 percent on average when the effects of accruing transaction costs are incorporated ex-ante into estimation of portfolio weights. When adding a risk-free asset, net Sharpe ratios remain of the same magnitude and portfolio volatility does not exceed a predefined target level.

Literature on Losses Given Default (LGD) usually focuses on mean predictions, even though losses are extremely skewed and bimodal. This paper proposes a Quantile Regression (QR) approach to get a comprehensive view on the entire probability distribution of losses. The method allows new insights on covariate effects over the whole LGD spectrum. In particular, middle quantiles are explainable by observable covariates while tail events, e.g., extremely high LGDs, seem to be rather driven by unobservable random events. A comparison of the QR approach with several alternatives from recent literature reveals advantages when evaluating downturn and unexpected credit losses. In addition, we identify limitations of classical mean prediction comparisons and propose alternative goodness of fit measures for the validation of forecasts for the entire LGD distribution.

This paper analyzes the capital incentives and adequacy of financial institutions for asset portfolio securitizations. The empirical analysis is based on US securitization rating and impairment data. The paper finds that regulatory capital rules for securitizations may be insufficient to cover implied losses during economic downturns such as the Global Financial Crisis. In addition, the rating process of securitizations provides capital arbitrage incentives for financial institutions and may further reduce regulatory capital requirements. These policy-relevant findings assume that the ratings assigned by rating agencies are correct and can be used to build a test for the ability of Basel capital regulations to cover downturn losses.

This study analyses the level of systematic risk for US mortgage portfolio securitisations based on the variation of default rates which cannot be explained by observed deterministic factors. Systematic risk is decomposed into general systemic risk, rating-class-specific systematic risk and their covariance structure. General systematic risk sensitivities increase from lower rating classes to medium rating classes and decreases to higher rating classes. Rating-class-specific systematic risk shows an opposite pattern. The methodology provides for more accurate probability of default and Value-at-Risk forecasts.

The accurate measurement and effective control of financial risk are of crucial importance to risk managers and regulators. However, risk measures are potentially affected by errors in the estimation of model parameters from limited samples, leading to parameter risk. The key contribution of this paper is the formulation of a general framework to hedge this parameter risk. Applying the new framework to credit portfolio modeling, we highlight the importance of parameter risk, estimation methods, and diversification effects.

We extend the linear quantile regression with a neural network structure to enable more flexibility in every quantile of the bank loan loss given default distribution. This allows us to model interactions and non-linear impacts of any kind without the need of specifying the exact form beforehand. The precision of the quantile forecasts increases up to 30% compared to the benchmark, especially for higher quantiles which are most important in credit risk. By using a novel feature importance measure, we calculate the strength, direction, interactions and other non-linear impacts for every conditional quantile and every variable. This enables us to explain why our extension exhibits superior performance over the benchmark. Moreover, we find that the macroeconomy is up to two times more important in USA than in Europe and has large joint impacts in both regions. The macroeconomy is most important in the US, whereas in Europe collateralization is essential.

Resolution of non-performing loans is a key determinant of bank credit default losses. This paper analyzes macroeconomic and systematic frailty effects of the default resolution time for a sample of 17,395 defaulted bank loans in USA, Great Britain, and Canada. We find that frailties have a huge impact on the resolution times. In a representative sample portfolio, median resolution times more than double in a recession when compared to an expansion. This leads to highly skewed distributions of losses and considerable systematic risk of the bank portfolio.