Bayesian learning provides a core concept of information processing in financial markets. Typically it is assumed that market participants perfectly know the quality of released news. However, in practice, news' precision is rarely disclosed. Therefore, we extend standard Bayesian learning...

In this paper, the influence of information costs on the integration of Northern European financial markets between ca. 1350 and 1560 is explored. The approach is based on splitting information costs into their constitutive components and on measuring one of these, i.e. the costs of transmitting...

In this paper, the influence of information costs on the integration of Northern European financial markets between ca. 1350 and 1560 is explored. The approach is based on splitting information costs into their constitutive components and on measuring one of these, i.e. the costs of transmitting...

The present paper embarks on an analysis of interactions between the US and Euroland in the capital, foreign exchange, money and stock markets from 1994 until 2006. Considering influences on financial market volatility, the estimations are carried out in multivariate EGARCH models using...

A great proportion of stock dynamics can be explained using publicly available information. The relationship between dynamics and public information may be of nonlinear character. In this paper we offer an approach to stock picking by employing so-called decision trees and applying them to XETRA...

This paper investigates the information content of the Norges Bank's key rate projections. Wavelet spectrum estimates provide the basis for estimating jump probabilities of short- and long-term interest rates on monetary policy announcement days before and after the introduction of key rate...

The Reserve Bank of New Zealand (RBNZ) has been the first central bank that began to publish interest rate projections in order to improve its guidance of monetary policy. This paper provides new evidence on the role of interest rate projections for market expectations about future short-term...

We introduce a copula-based dynamic model for multivariate processes of (non-negative) high-frequency trading variables revealing time-varying conditional variances and correlations. Modeling the variables' conditional mean processes using a multiplicative error model we map the resulting...

Multiplicative error models (MEM) became a standard tool for modeling conditional durations of intraday transactions, realized volatilities and trading volumes. The parametric estimation of the corresponding multivariate model, the so-called vector MEM (VMEM), requires a specification of the...