Bayesian Methods for Improving Credit Scoring Models

We propose a Bayesian methodology that enables banks to improve their credit scoring models by imposing prior information. As prior information, we use coefficients from credit scoring models estimated on other data sets. Through simulations, we explore the default prediction power of three Bayesian estimators in three different scenarios and find that they perform better than standard maximum likelihood estimates. We recommend that banks consider Bayesian estimation for internal and regulatory default prediction models.Credit Scoring, Bayesian Inference, Bankruptcy Prediction

Partial orderings of default predictions

We compare and generalize various partial orderings of probability forecasters according to the quality of their predictions. It appears that the calibration requirement is quite at odds with the possibility of some such ordering. However, if the requirements of calibration and identical sets of debtors are relaxed, comparability obtains more easily. Taking default predictions in the credit rating industry as an example, we show for a data base of 5333 (Moody’s) and 6505 ten-year default predictions (S&P), that Moody’s and S&P cannot be ordered neither according to their grade distributions given default or non-default or to their Ginicurves, but Moody’s dominate S&P with respect to the ROC-criterion

How do investors value sustainability?

We investigate how an investor’s preference for sustainable assets in the portfolio varies for differing levels of risk aversion. Using a sample of 411 publicly listed firms in the S&P 500, we calculate financial and sustainability returns, on which the investor’s utility depends. We approximate the investor’s preference by the exponential and s-shaped utility function and optimize with regard to the sustainability preference. We find that with increasing levels of risk aversion, both minimum-variance and maximum Sharpe ratio type investors seek to incorporate sustainable assets in the portfolio

How do Rating Agencies Score in Predicting Firm Performance

We use dynamic panel analysis to examine whether credit rating agencies achieve what they claim to achieve, namely, look into the future when assigning their ratings. We find that Moody's ratings help predict individual financial ratios over a horizon of up to five years. Ratings also predict a multivariate credit score, again over five years. The contribution of ratings appears to be economically significant and robust for different specifications.Credit Ratings, Predictive ability, Dynamic Panel Model.

Testing for structural changes in large portfolios

Model free tests for constant parameters often fail to detect structural changes in high dimensions. In practice, this corresponds to a portfolio with many assets and a reasonable long time series. We reduce the dimensionality of the problem by looking a compressed panel of time series obtained by cluster analysis and the principal components of the data. Using our methodology we are able to extend a test for a constant correlation matrix from a sub portfolio to whole indices and exemplify the procedure with the EuroStoxx index

Volatility forecasting accuracy for Bitcoin

We analyse the quality of Bitcoin volatility forecasting of GARCH-type models applying the commonly used volatility proxy based on squared daily returns as well as a jump-robust proxy based on intra-day returns and vary the degrees of asymmetry in robust loss functions. We construct model confidence sets (MCS) which contain superior models with a high probability and find them to be systematically smaller for asymmetric loss functions and the jump robust proxy. Our findings suggest a cautious use of GARCH models in forecasting Bitcoin's volatility

Do firms hedge in order to avoid financial distress costs? New empirical evidence using bank data

We present a new approach to test empirically the financial distress costs theory of corporate hedging. We estimate the ex-ante expected financial distress costs, which serve as a starting point to construct further explanatory variables in an equilibrium setting, as a fraction of the value of an asset-or-nothing put option on the firm's assets. Using single-contract data of the derivatives' use of 189 German middle-market companies that stems from a major bank as well as Basel II default probabilities and historical accounting information, we are able to explain a significant share of the observed cross-sectional differences in hedge ratios. Hence, our analysis adds further support for the financial distress costs theory of corporate hedging from the perspective of a financial intermediary

Managerial behavior in fund tournaments—the impact of TrueSkill

Measuring mutual fund managers’ skills by Microsoft’s TrueSkill algorithm, we find highly skilled managers to behave self-confident resulting in higher risk-taking in the second half of the year compared to less skilled managers. Introducing the TrueSkill algorithm, which is widely used in the e-sports community, to this branch of literature, we can replicate previous findings and theories suggesting overconfidence for mid-years winners

Dual disadvantage and dispersion dynamics for income distributions

Income distribution has been a longstanding focus of social and economic interest, but never more so than in recent times. New metrics for disadvantage and spread enable a more precise differentiation of directional asymmetry and dispersion, drawing on an internal contextual perspective. The dual metrics for asymmetry and spread can be plotted over time into a phase plane, enabling comparative social welfare perspectives over time and between countries. The methods are utilised to study the dramatic changes that took place in Europe prior to and after the GFC. Major differences are revealed. In terms of asymmetry and spread, some countries have been fallers (lower in both) while other countries are risers