In this paper we propose a statistically derived measure as an alternative to the simple average PD to provide more accurate risk assessment at portfolio level. The theoretical analysis is followed by a numerical example in sections 3 and 4. We then assess the accuracy and representativeness...

In delegated portfolio management, we formulate a central-planned portfolio selection problem by multi-objective programming (utilities of the investor and the manager) to study the Pareto optimal portfolio and find Pareto improvement. First, we solve out two cases of the closed-form Pareto...

While common machine learning algorithms focus on minimizing the mean-square errors of model fit, we show that genetic programming, GP, is well-suited to maximize an economic objective, the Sharpe ratio of the usual spread portfolio in the cross-section of expected stock returns. In contrast to...

The COVID-19 pandemic placed many challenges to everyone in terms of wellbeing and economic activities. As for banking and finance, while many rely on re-calibration of probability of default models to adapt own portfolio to the latest reality, it is worthwhile to bring to reader's attention...