Stern School of Business >
IOMS: Statistics Working Papers >
Please use this identifier to cite or link to this item:
|Title: ||The Local Whittle Estimator of Long Memory Stochastic Volatility|
|Authors: ||Hurvich, Clifford M.|
Ray, Bonnie K.
|Keywords: ||long-range dependence|
|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.|
|Appears in Collections:||IOMS: Statistics Working Papers|
Items in Faculty Digital Archive are protected by copyright, with all rights reserved, unless otherwise indicated.