In the present paper we examine whether financial markets could have helped predict exchange rates in selected Central, Eastern and Southeastern European (CESEE) economies, namely the Czech Republic, Hungary and Poland, during the current financial crisis. To this end, we derive risk-neutral...

In the present paper we examine whether financial markets could have helped predict exchange rates in three selected Central and Eastern European (CEE) economies of the EU, namely the Czech Republic, Hungary and Poland, during the current financial crisis. To this end, we derive risk-neutral...

The empirical literature of stock market predictability mainly suffers from model uncertainty and parameter instability. To meet this challenge, we propose a novel approach that combines the documented merits of diffusion indices, regime-switching models, and forecast combination to predict the...

The empirical literature of stock market predictability mainly suffers from model uncertainty and parameter instability. To meet this challenge, we propose a novel approach that combines the documented merits of diffusion indices, regime-switching models, and forecast combination to predict the...

The empirical literature of stock market predictability mainly suffers from model uncertainty and parameter instability. To meet this challenge, we propose a novel approach that combines the documented merits of diffusion indices, regime-switching models, and forecast combination to predict the...

The empirical literature of stock market predictability mainly suffers from model uncertainty and parameter instability. To meet this challenge, we propose a novel approach that combines the documented merits of diffusion indices, regime-switching models, and forecast combination to predict the...

ANN (Artificial Neural Network) models and Spline techniques have been applied to economic analysis, to handle economic problems, evaluate portfolio risk and stock performance, and to forecast stock exchange rates and gold prices. These techniques are improving nowadays and continue to serve as...

We study the non-linear causal relation between uncertainty-due-to-infectious-diseases and stock-bond correlation. To this end, we use high-frequency 1-min data to compute daily realized measures of correlation and jumps, and then, we employ a nonlinear Granger causality test with the use of...

We study the non-linear causal relation between uncertainty-due-to-infectious-diseases and stock-bond correlation. To this end, we use high-frequency 1-min data to compute daily realized measures of correlation and jumps, and then, we employ a nonlinear Granger causality test with the use of...

This paper aims to introduce a nonlinear model to forecast macroeconomic time series using a large number of predictors. The technique used to summarize the predictors in a small number of variables is Principal Component Analysis (PC), while the method used to capture nonlinearity is artificial...