We address the calibration issues of the weighted-indexed semi-Markov chain (WISMC) model applied to high-frequency financial data. Specifically, we propose to automate the discretisation of the price returns and the volatility index by using four different approaches, two based on statistical...

Commercial AI solutions provide analysts and managers with data-driven business intelligence for a wide range of decisions, such as demand forecasting and pricing. However, human analysts may have their own insights and experiences about the decision-making that is at odds with the algorithmic...

The behavioural finance literature attributes the persistent market misvaluation observed in real data to the presence of deviations from rational thinking of the actors involved. Cognitive biases and the use of simple heuristics can be described using expected utility maximising agents that...

In this study, we empirically investigate whether or not the conventional belief that new information about fundamental value is revealed in the futures market ahead of the spot market is applicable to four important storable energy commodities, oil, gasoline, heating oil and natural gas. Taking...

We aim to assess the impact of a pandemic data point on the calibration of a stochastic multi-population mortality projection model and its resulting projections for future mortality rates. Throughout the paper, we put focus on the Li and Lee mortality model, which has become a standard for...

On the eve of its 60th anniversary, the gravity model of trade is a "celebrity", due to its intuitive appeal, solid theoretical foundations, and remarkable empirical success. Yet, many economists still view gravity simply as an intuitive but naive reduced-form estimating equation and apply it...

We establish 16 good practice principles for modelling defined contribution pension plans. These principles cover the following issues: model specification and calibration; modelling quantifiable uncertainty; modelling member choices; modelling member characteristics, such as occupation and...

Classical interval estimation ignores misspecification uncertainty that is almost inevitable in practice. This paper proposes an approach to construct an uncertainty interval that incorporates misspecification based on an $f$-divergence. We construct the uncertainty interval estimators using...

We propose a belief-generating model from which we build a statistical measure of investor disagreement. We simulate differences in beliefs across investors by endowing them with different machine learning models for forecasting returns from the same set of inputs. We measure disagreement as the...