Backtesting stock market investment strategies is fraught with danger – for example, overfitting. The signal to noise ratio in stock markets is so low that overfitting is inevitable. Simulation offers a means of assessing and compensating for the dangers. It is not obvious at first how...

This study looks at the effects of participating in active labour market policy programs for persons with work disabilities. More in detail, it draws attention to the importance of taking into account the timing of when a work disability has been registered for persons attending ALMP programs....

This paper provides practical insights into common statistical measures used to validate a model's discriminatory power for the probability of default (PD), loss liven default (LGD) and exposure at default (EAD). The study has more of an informative value without delivering empirical evidence....

We study parameter estimation from the sample X, when the objective is to maximize the expected value of a criterion function, Q, for a distinct sample, Y. This is the situation that arises when a model is estimated for the purpose of describing other data than those used for estimation. The...

We investigate a novel database of 10,217 extreme operational losses from the Italian bank UniCredit, covering a period of 10 years and 7 different event types. Our goal is to shed light on the dependence between the severity distribution of these losses and a set of macroeconomic, financial and...

We extend the results of De Luca et al. (2021) to inference for linear regression models based on weighted-average least squares (WALS), a frequentist model averaging approach with a Bayesian flavor. We concentrate on inference about a single focus parameter, interpreted as the causal effect of...

The paper lists salient characteristics of the certainty theory of consumer choice and discusses the import of prominent empirical analyses of the theory. All of them reject the theory's empirical relevance which suggests that the theory is unfit to analyze consumer choice in an uncertain world....

A key point to assess the application of statistical learning models in Artificial Intelligence (AI) is the evaluation of their predictive accuracy. This because the "automatic" choice of an action crucially depends on the made prediction. While the best model in terms of fit to the observed...

A trustworthy application of Artificial Intelligence requires to measure in advance its possible risks. When applied to regulated industries, such as banking, finance and insurance, Artificial Intelligence methods lack explainability and, therefore, authorities aimed at monitoring risks may not...

In a world that is increasingly connected on-line, cyber risks become critical. Cyber risk management is very difficult, as cyber loss data are typically not disclosed. To mitigate the reputational risks associated with their disclosure, loss data may be collected in terms of ordered severity...