Nonparametric Beta Kernel Estimator for Long Memory Time Series

Bouezmarni, Taoufik and Van Bellegem, Sébastien (2009) Nonparametric Beta Kernel Estimator for Long Memory Time Series. TSE Working Paper, n. 09-082

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Official URL: http://tse-fr.eu/pub/23200

Abstract

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.

Item Type: Monograph (Working Paper)
Language: English
Date: 11 September 2009
Uncontrolled Keywords: spectral density, long rage dependence, nonparametric estimation
Subjects: B- ECONOMIE ET FINANCE
Divisions: TSE-R (Toulouse)
Site: UT1
Date Deposited: 18 Jan 2012 06:02
Last Modified: 07 Mar 2018 13:22
OAI ID: oai:tse-fr.eu:23200
URI: http://publications.ut-capitole.fr/id/eprint/3421

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