Forecasting volatility models typically rely on either daily or high frequency (HF) data and the choice between these two categories is not obvious. In particular, the latter allows to treat volatility as observable but they suffer from many limitations. HF data feature microstructure problem,...

Forecasting-volatility models typically rely on either daily or high frequency (HF) data and the choice between these two categories is not obvious. In particular, the latter allows to treat volatility as observable but they suffer of many limitations. HF data feature microstructure problem,...

We propose a new nonlinear classification method based on a Bayesian "sum-of-trees" model, the Bayesian Additive Classification Tree (BACT), which extends the Bayesian Additive Regression Tree (BART) method into the classification context. Like BART, the BACT is a Bayesian nonparametric additive...

One of the most significant factors which influences the level of banking sector liquidity is Currency in Circulation. Although the central bank is in charge of distribution of the currency it can't assess the demand for the currency, as that demand is generated by the customers of commercial...

We propose autocorrelation-robust asymptotic variances of the Brier score and Brier skill score, which are generally applicable in circumstances with weak serial correlation. An empirical application in macroeconomics underscores the importance of taking care of serial correlation. We find that...

This paper studies the existence of risk premia in crude oil futures prices with simple regression and Bayesian VAR models. It also studies the importance of three main risk premia models in explaining and forecasting the risk premia in practice. Whilst the existence of the premia and the...