This paper focuses on nominal exchange rates, specifically the US dollar rate vis-à-vis the Euro and the Japanese Yen at a daily frequency. We model both absolute values of returns and squared returns using long-memory techniques, being particularly interested in volatility modelling and...

This paper analyses the long-memory properties of a high-frequency financial time series dataset. It focuses on temporal aggregation and other features of the data, and how they might affect the degree of dependence of the series. Fractional integration or I(d) models are estimated with a...

This study is motivated by the development of credit-related instruments and signals of stock price movements of large banks during the recent financial crisis. What is common to most of the empirical studies in this field is that they concentrate on modeling the conditional mean. However,...

This paper deals with the impact of the $/¿ exchange rate on German exports in the period from 1995Q1 to 2008Q4. Our main aim is to identify "pain thresholds" for German exporters. We rely on a non-linear model according to which suddenly strong spurts of exports occur when changes of the EXR...

This paper analyses the long-memory properties of high frequency financial time series. It focuses on temporal aggregation and the influence that this might have on the degree of dependence of the series. Fractional integration or I(d) models are estimated with a variety of specifications for...

This paper uncovers ongoing trends in idiosyncratic earnings volatility across generations by decomposing residual earnings auto-covariances into a permanent and a transitory component. We employ data on complete earnings life cycles forprime age men born 1935 through 1974 that covers earnings...

This paper analyses the long-memory properties of both the conditional mean and variance of UK real GDP over the period 1851-2013 by estimating a multivariate ARFIMA-FIGARCH model (with the unemployment rate and inflation as explanatory variables). The results suggest that this series is...