Wavelet transform for log periodogram regression in long memory stochastic volatility model

Jin Lee

No 682, Econometric Society 2004 Far Eastern Meetings from Econometric Society

Abstract: We consider semiparametric log periodogram regression estimation of memory parameter for the latent process in long memory stochastic volatility models. It is known that though widely used among researchers, the Geweke and Porter-Hudak (1983; GPH) LP estimator violates the Gaussian or Martingale assumption, which results in significant negative bias due to the existence of the spectrum of non-Gaussian noise. Through wavelet transform of the squared process, we effectively remove the noise spectrum around zero frequency, and obtain Gaussian-approximate spectral representation at zero frequency. We propose wavelet-based regression estimator, and derive the asymptotic mean squared error and the consistency in line with the asymptotic theory in the long memory literature. Simulation studies show that wavelet-based regression estimation is an effective way in reducing the bias, compared with the GPH estimator

Keywords: Long memory stochastic volatility; Wavelet transform; Log periodogram regression (search for similar items in EconPapers)
JEL-codes: C13 C14 (search for similar items in EconPapers)
Date: 2004-08-11
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-fin
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Persistent link: https://EconPapers.repec.org/RePEc:ecm:feam04:682

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