We propose a new approach based on a generalization of the classic logit model to improve prediction accuracy in US bank failures. We introduce mixed-data sampling (Midas) aggregation to construct financial predictors in a logistic regression. This allows us to relax the limitation of...

This paper introduces a method based on the use of various linear and nonlinear state space models that uses non-synchronous data to extract global stochastic financial trends (GST). These models are specifically constructed to take advantage of the intraday arrival of closing information coming...

We introduce a new strategy for the prediction of linear temporal aggregates, we call it "hybrid", and study its performance using asymptotic theory. This scheme consists of carrying out model parameter estimation with data sampled at the highest available frequency and the subsequent prediction...

This paper provides implementation details and application examples of the asymptotic error evaluation formulas introduced in the reference [GO14a] concerning three different approaches to the forecasting of linear temporal aggregates using estimated linear processes. The first two techniques...