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...

The nuclear industry uses fuel performance codes to demonstrate integrity preservation of fuel rods. These codes include a complex system of models with empirical constants that one needs to calibrate for best estimates and uncertainties. However, deriving the appropriate level of uncertainty is...

Essential features of the multigrid Ensemble Kalman Filter (G. Moldovan , G. Lehnasch , L. Cordier , M. Meldi , A multigrid /ensemble Kalman filter strategy for assimilation of unsteady flows , Journal of Computational Physics 443-110481 ) recently proposed for Data Assimilation of fluid flows...

In this paper, we show how the factor modeling approach widely used to model yield curve evolution in real-world applications can be adapted to pricing applications using the Musiela HJM modeling framework. The resulting risk-neutral modeling framework combines the intuitiveness and...

This paper provides estimates of COVID-19 effective reproduction numbers worldwide and explains their evolution for selected European countries since the start of the pandemic, taking account of changes in voluntary and government-mandated social distancing, incentives to comply, vaccination and...

This paper provides an analysis of scenarios on the evolution of the EU GDP and aggregate consumption over the period 2020-2030, focusing on the role played by the COVID-19 shock. The gap between estimated precrisis trends and projected paths for three important variables are obtained using...

The primary objective of this paper is to revisit DSGE models with a view to bringing out their key weaknesses, including statistical misspecification, non-identification of deep parameters, substantive inadequacy, weak forecasting performance, and potentially misleading policy analysis. It is...

We propose a framework for estimation and inference when the model may be misspecified. We rely on a local asymptotic approach where the degree of misspecification is indexed by the sample size. We construct estimators whose mean squared error is minimax in a neighborhood of the reference model,...