This paper examines long memory volatility in the cross-section of stock returns. We show that long memory volatility is widespread in the U.S. and that the degree of memory can be related to firm characteristics such as market capitalization, book-to-market ratio, prior performance and price...

This paper examines long memory volatility in international stock markets. We show that long memory volatility is widespread in eighty-two countries and that the degree of memory can be related to macroeconomic variables such as inflation, unemployment rates, interest rates or stability of a...

Das Abflussverhalten des Rheins wird mittels flexibler saisonaler Modelle mit langem Gedächtnis modelliert. Zur Schätzung der Persistenz wird für jede Saisonfrequenz separat eine Log-Periodogramm Regression durchgeführt. Verglichen mit Standard-ARMA-Prozessen liefern diese Modelle eine gute...

Cointegration describes the pattern that pairs of time series keep together in long run, although they diverge in short run. A generalisation of this behaviour is the fractional cointegration. Two statistical tests, the M– and ML–test are formulated for fractional cointegration in different...

We show that small trends do not influence log-periodogram based estimators for the memory parameter in a stationary invertible long-memory process. In the case of slowly decaying trends which are easily confused with long-range dependence we show by Monte Carlo methods that the tapered...

We show that there is strong evidence of long-range dependence in the volatilities of several German stock returns. This will be done by estimating the memory parameter of the absolute returns with classical log-periodogram regression as well as by employing the tapered periodogram. Both...

The paper discusses structural change as possible mechanism that generates the appearance of long memory in economic time series. It shows that there are no long memory effects in German stock returns and that long memory in squares of German stock returns disappears once shifting means are...

We investigate the behaviour of S - estimators in the linear regression model, when the error terms are long-memory Gaussian processes. It turns out that under mild regularity conditions S - estimators are still normally distributed with a similar variance - covariance structure as in the i.i.d...

We investigate the behavior of nonparametric kernel M-estimators in the presence of long-memory errors. The optimal bandwidth and a central limit theorem are obtained. It turns out that in the Gaussian case all kernel M-estimators have the same limiting normal distribution. The motivation behind...

We derive the limiting null distribution of the robust CUSUM-M test and the recursive CUSUM-M test for structural change of the coefficients of a linear regression model with long-memory disturbances. It turns out that the asymptotic null distribution of the CUSUM-M statistic is a fractional...