Title: Nonparametric beta kernel estimator for long memory time series

Speaker: Taoufik Bouezmarni – Université de Sherbrooke, Canada

This paper introduces a new nonparametric estimator of the spectral density that is given by smoothing the periodogram by beta kernel density. 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 show that the estimator automatically 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.

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