Semiparametric estimation in perturbed long memory series

Josu Arteche

No 22, Computing in Economics and Finance 2006 from Society for Computational Economics

Abstract: The estimation of the memory parameter in perturbed long memory series has recently attracted attention motivated especially by the strong persistence of the volatility in many financial and economic time series and the use of Long Memory in Stochastic Volatility (LMSV) processes to model such a behaviour. This paper discusses frequency domain semiparametric estimation of the memory parameter and proposes an extension of the log periodogram regression which explicitly accounts for the added noise, comparing it, asymptotically and in finite samples, with similar extant techniques. Contrary to the non linear log periodogram regression of Sun and Phillips, we do not use a linear approximation of the logarithmic term which accounts for the added noise. A reduction of the asymptotic bias is achieved in this way and makes possible a faster convergence by permitting a larger bandwidth. Monte Carlo results confirm this bias reduction in finite samples. An application to a series of returns of the Spanish Ibex35 stock index is finally included.

Keywords: long memory; stochastic volatility; semiparametric estimation (search for similar items in EconPapers)
JEL-codes: C22 C13 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ets
Date: 2006-07-04
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Related works:
Working Paper: Semiparametric estimation in perturbed long memory series (2005)
Journal Article: Semiparametric estimation in perturbed long memory series (2006)
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Persistent link: http://EconPapers.repec.org/RePEc:sce:scecfa:22

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