In the presence of asset returns’ non-normal behavior, optimal portfolio selection techniques should consider higher-moment risks. In this paper, we extend the Black-Litterman (BL) asset allocation model (Black & Litterman, 1990) by applying the hidden truncation skew-normal distribution...

The COVID-19 pandemic has highlighted the importance of reliable statistical models which, based on the available data, can provide accurate forecasts and impact analysis of alternative policy measures. Here we propose Bayesian time dependent Poisson autoregressive models, that include time...

Using a state-space system, I forecasted the US Treasury yields by employing frequentist and Bayesian methods after first decomposing the yields of varying maturities into its unobserved term structure factors. Then, I exploited the structure of the state-space model to forecast the Treasury...

In applications, the linear multiple regression model is often modified to allow for nonlinearity in an independent variable. It is argued here that in practice it may often be desirable to specify a Bayesian prior that the unknown functional form is "simple" or "uncomplicated" rather than to...

The rapid spread of COVID-19 across the globe primed a variety of non-pharmaceutical interventions (NPIs). Given these NPIs, whether the SIR parameters followed a Bayesian learning, a random walk pattern or other type of learning with evolving epidemiological data over time has implications for...

We estimate the effects of domestic and foreign quantitative easing (QE) programmes on a small open economy, Sweden, using a structural BVAR model. Domestic QE raised GDP, lowered unemployment and depreciated the currency, while effects on in ation are less clear. The ECB QE had large positive...

Knowledge management within organizations allows to support a global business strategy and represents a systemic and organized attempt to use knowledge within an organization to improve its performance. The objective of this research is to study and analyze knowledge management through Bayesian...

We consider both frequentist and empirical Bayes forecasts of a single time series using a linear model with T observations and K orthonormal predictors. The frequentist formulation considers estimators that are equivariant under permutations (reorderings) of the regressors. The empirical Bayes...

This paper analyzes the monetary policy of the Federal Reserve (Fed) and the Bank of England (BoE) both before and after the global financial crisis. In contrast to extant literature, we use a Bayesian model averaging (BMA) approach that does not rely on one underlying empirical model but...