期刊名称：CORE Discussion Papers / Center for Operations Research and Econometrics (UCL), Louvain
出版年度：2011
卷号：2011
期号：1
出版社：Center for Operations Research and Econometrics (UCL), Louvain
摘要：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
关键词：spectral density, long range dependence, nonparametric estimation,
periodogram, kernel smoothing, Beta kernel, cross-validation
