We build on Fackler and King (1990) and propose a general calibration model for implied risk neutral densities. Our model allows for the joint calibration of a set of densities at different maturities and dates. The model is a Bayesian dynamic beta Markov random field which allows for possible...

Non-homogeneous post-processing is often used to improve the predictive performance of probabilistic ensemble forecasts. A common quantity to develop, test, and demonstrate new methods is the near-surface air temperature frequently assumed to follow a Gaussian response distribution. However,...

Using the standard linear model as a base, a unified theory of Bayesian Analysis of Cointegration Models is constructed. This is achieved by defining (natural conjugate priors in the linear model and using the implied priors for the cointegration model

We propose a nonparametric Bayesian approach for conducting inference on probabilistic surveys. We use this approach to study whether U.S. Survey of Professional Forecasters density projections for output growth and inflation are consistent with the noisy rational expectations hypothesis. We...

This paper presents a unified framework for the popular Skewed Generalized t (SGT) distribution and its special cases the Skewed Generalized Error Distribution (SGED), the skewed student's t, the skewed Laplace and skewed normal distributions. The analytical moment equations presented can be...

This paper examined a set of over two thousand crypto-coins observed between 2015 and 2020 to estimate their credit risk by computing their probability of death. We employed different definitions of dead coins, ranging from academic literature to professional practice, alternative forecasting...