Semiparametric estimation of long range dependence – experimental and empirical evidence on the German stock market
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Beschreibung
This thesis is concerned with experimental and empirical aspects of long memory in time series.
The aim of this dissertation is to further investigate estimating methods for the order of
fractional integration by employing Monte Carlo simulations, and to provide empirical evidence of
long range dependence in German asset returns.
In particular, the performance of a popular semiparametric estimator of long memory, the Local
Whittle (LW) estimator, is studied in detail under empirically relevant conditions and compared
to modified versions of it. The experimental results shed light on the finite sample performance
of the estimators and also address the choice of the number of frequencies to be used in the
estimation. It turns out that extended versions of the LW estimator dominate the classical
variant given an appropiate choice of the bandwidth, and are clearly needed to ensure consistency
in the nonstationarity region or in the presence of an additive noise.
The empirical work in this thesis investigates the persistence in volatility measures of the
German stock price index DAX. For this purpose, not only semiparametric estimators of long
memory are applied to the data, but also possible breaks in mean and persistence are taken into
account. The results are highly indicative of the presence of long memory in absolute and
logsquared returns, and suggest a significant increase in persistence in the year 2001.
Autor*in
Maya Olivares-Horn
Themen in »Semiparametric estimation of long range dependence – experimental and empirical evidence on the German stock market«
Finanzen Semiparametric estimation Statistik long memory stochastic volatility Ökonometrie