RT Monograph
SR 00
A1 Bouezmarni, Taoufik
A1 Van Bellegem, Sébastien
T1 Nonparametric Beta Kernel Estimator for Long Memory Time Series
YR 2009
FD 2009-09-11
VO 09-082
K1 spectral density
K1 long rage dependence
K1 nonparametric estimation
AB The paper introduces a new nonparametric estimator of the spectral density that is given in smoothing the periodogram by the probability density of Beta random variable (Beta kernel). The estimator is proved to be bounded for short memory data, and diverges at the origin for long memory data. The convergence in probability of the relative error and Monte Carlo simulations suggest that the estimator automaticaly adapts to the long- or the short-range dependency of the process. A cross-validation procedure is also studied in order to select the nuisance parameter of the estimator. Illustrations on historical as well as most recent returns and absolute returns of the S&P500 index show the reasonable performance of the estimation, and show that the data-driven estimator is a valuable tool for the detection of long-memory as well as hidden periodicities in stock returns.
T2 TSE Working Paper
PB TSE Working Paper
AV Published
LK https://publications.ut-capitole.fr/id/eprint/3421/
UL http://tse-fr.eu/pub/23200