This paper introduces a methodology for incorporating risk preferences directly into forecasting model selection. The relative model information score, estimated from either a point-based information criterion or cross-validated errors, leverages the full distribution to map different risk propensities. We show that standard model selection in the literature is risk-agnostic. A risk-neutral stance is represented by the median of the relative model information score distribution, which characterises the plausibility of a model choice, while risk-averse and risk-tolerant choices correspond to its upper and lower quantiles. Our empirical evaluation demonstrates that risk-neutral and risk-averse selections consistently outperform the benchmark risk-agnostic choice in both point and quantile forecast accuracy. Moreover, we show that a risk-tolerant selection is beneficial during periods of extreme disruption. The proposed methodology provides a robust and flexible way to manage the forecast modelling risk, improving forecast accuracy and aligning forecasting modelling with stakeholders’ risk profiles.
Kourentzes et al. (Sat,) studied this question.