This paper analyses the long-memory properties of US and European stock indices, as well as their linkages, using fractional integration and fractional cointegration techniques. These methods are more general and have higher power than the standard ones usually employed in the literature. The...

This paper uses fractional integration techniques to examine the stochastic behaviour of high and low stock prices in Europe and then to test for the possible existence of long-run linkages between them by looking at the range, i.e., the difference between the two logged series. Specifically,...

We examine how regularly scheduled macroeconomic announcements for the U.S., Germany and the euro area affect the German stock market, using high-frequency, minute-by-minute DAX data. Our study extends the literature on high-frequency announcement effects in several ways. First, we account for...

This paper investigates the impact of abnormal returns on stock prices by using daily and hourly data for some developed (US, UK, Japan) and emerging (China, India) markets over the period 01.01.2010-01.01.2020. Average analysis, t-tests, CAR and trading simulation methods are used to test the...

This paper examines the process of price discovery in the MTS system, which builds on the parallel quoting of euro-denominated government securities on a number of (relatively large) domestic markets and on a (relatively small) European marketplace (EuroMTS). Using twenty-seven months of daily...

This paper examines several US monthly financial time series data using fractional integration and cointegration techniques. The univariate analysis based on fractional integration aims to determine whether the series are I(1) (in which case markets might be efficient) or alternatively I(d) with...

Modern investors face a high-dimensional prediction problem: thousands of observable variables are potentially relevant for forecasting. We reassess the conventional wisdom on market efficiency in light of this fact. In our model economy, which resembles a typical machine learning setting, N...