In this study, we enhance the dynamic connectedness measures originally introduced by Diebold and Yılmaz (2012, 2014) with a time-varying parameter vector autoregressive model (TVP-VAR) which predicates upon a time-varying variance-covariance structure. This framework allows to capture possible...

Utilizing a machine learning technique known as random forests, we study whether regional output growth uncertainty helps to improve the accuracy of forecasts of regional output growth for 12 regions of the UK using monthly data for the period from 1970 to 2020. We use a stochastic volatility...

The study examines the vital connection between stock returns and oil price changes for oil exporting/importing countries separately. We present evidence employing granger causality, impulse response and error variance decomposition based on panel vector autoregression. The results of panel...