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Please use this identifier to cite or link to this item: http://hdl.handle.net/2451/26331

Title: The Local Whittle Estimator of Long Memory Stochastic Volatility
Authors: Hurvich, Clifford M.
Ray, Bonnie K.
Keywords: long-range dependence
nonlinearity
semiparametric estimation
Issue Date: May-2003
Publisher: Stern School of Business, New York University
Series/Report no.: SOR-2003-5
Abstract: We propose a new semiparametric estimator of the degree of persistence in volatility for long memory stochastic volatility (LMSV) models. The estimator uses the periodogram of the log squared returns in a local Whittle criterion which explicitly accounts for the noise term in the LMSV model. Finite-sample and asymptotic standard errors for the estimator are provided. An extensive simulation study reveals that the local Whittle estimator is much less biased and that the finite-sample standard errors yield more accurate confidence intervals than the widely-used GPH estimator. The estimator is also found to be robust against possible leverage effects. In an empirical analysis of the daily Deutsche Mark/US Dollar exchange rate, the new estimator indicates stronger persistence in volatility than the GPH estimator, provided that a large number of frequencies is used.
URI: http://hdl.handle.net/2451/26331
Appears in Collections:IOMS: Statistics Working Papers

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