We consider parameter estimation for time-dependent locally stationary long-memory processes. The asymptotic distribution of an estimator based on the local infinite autoregressive representation is derived, and asymptotic formulas for the mean squared error of the estimator, and the...

We consider temporal aggregation of stationary and nonstationary time series with short memory, long memory and antipersistence, within the framework of fractional autoregressive processes. Asymptotically, long memory and antipersistence are preserved whereas short memory components vanish. In...

We provide a methodology to disentangle the long-run relation between variables from their own dynamics. Macroeconomic and aggregate financial series have a high degree of inertia. If this persistence is not properly accounted for, spurious correlations will give rise to paradoxes. Our procedure...

The aim of this paper is to examine the measurement of persistence in a range of time series models nested in the framework of Cramer (1961). This framework is a generalization of the Wold (1938) decomposition for stationary time series which, in addition to accommodating the standard I(0) and...

We provide a methodology to disentangle the long-run relation between variables from their own dynamics. Macroeconomic and aggregate financial series have a high degree of inertia. If this persistence is not properly accounted for, spurious correlations will give rise to paradoxes. Our procedure...

Empirical studies have shown that a large number of financial asset returns exhibit fat tails and are often characterized by volatility clustering and asymmetry. Also revealed as a stylized fact is Long memory or long range dependence in market volatility, with significant impact on pricing and...

This paper studies the persistence of daily returns of 21 German stocks from 1960 to 2008. We apply a widely used test based upon the modified R/S-Method by Lo [1991]. As an extension to Lux [1996] and Carbone et al. [2004] and in analogy to moving average or moving volatility, the statistics is...