We investigate the evolution of co-movement and lead-lag relationships between the nominal effective European exchange rate and the largest European stock markets in the time and frequency dimension. We decompose the financial return series into different time scales and apply the cross-wavelet...

The paper contributes to the literature on integration of stock markets by addressing the issue of non-synchronous trading. We argue that controlling for time differences in trading hours of stock markets is important and show that time-adjustment improves estimates of market integration. We...

We model the complex global dependencies in international financial markets using spatial techniques. Our methodology allows us to go beyond conventional correlation analyses and volatility-spillover models confined to studying pairwise relationships, and improves the accuracy of return...

Nonlinear, non-Gaussian state space models have found wide applications in many areas. Since such models usually do not allow for an analytical representation of their likelihood function, sequential Monte Carlo or particle filter methods are mostly applied to estimate their parameters. Since...

We develop importance sampling methods for computing two popular Bayesian model comparison criteria, namely, the marginal likelihood and deviance information criterion (DIC) for TVP-VARs with stochastic volatility. The proposed estimators are based on the integrated likelihood, which are...

This study is the first attempt to investigate both the linear and non-linear Granger causality between wavelet transformed spot and futures oil prices. Our findings consistently indicate bidirectional causality between the spot and futures oil markets at different time scales, under linear and...