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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