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SIGNAL EXTRACTION IN LONG MEMORY STOCHASTIC VOLATILITY

Published online by Cambridge University Press:  14 October 2014

Josu Arteche*
Affiliation:
University of the Basque Country UPV/EHU
*
*Address correspondence to Josu Arteche, Dept. of Econometrics and Statistics, University of the Basque Country UPV/EHU, Bilbao 48015, Spain; e-mail: josu.arteche@ehu.es.

Abstract

Long memory in stochastic volatility (LMSV) models are flexible tools for the modeling of persistent dynamic volatility, which is a typical characteristic of financial time series. However, their empirical applicability is limited because of the complications inherent in the estimation of the model and in the extraction of the volatility component. This paper proposes a new technique for volatility extraction, based on a semiparametric version of the optimal Wiener–Kolmogorov filter in the frequency domain. Its main characteristics are its simplicity and generality, because no parametric specification is needed for the volatility component and it remains valid for both stationary and nonstationary signals. The applicability of the proposal is shown in a Monte Carlo and in a daily series of returns from the Dow Jones Industrial index.

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ARTICLES
Copyright
Copyright © Cambridge University Press 2014 

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Supplementary material: File

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Supplementary material: PDF

Arteche supplementary data

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