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