Bias-reduced estimation of long memory stochastic volatility

Per Frederiksen and Morten Nielsen

CREATES Research Papers from Department of Economics and Business Economics, Aarhus University

Abstract: We propose to use a variant of the local polynomial Whittle estimator to estimate the memory parameter in volatility for long memory stochastic volatility models with potential nonstation- arity in the volatility process. We show that the estimator is asymptotically normal and capable of obtaining bias reduction as well as a rate of convergence arbitrarily close to the parametric rate, n1=2. A Monte Carlo study is conducted to support the theoretical results, and an analysis of daily exchange rates demonstrates the empirical usefulness of the estimators

Keywords: Bias reduction; local Whittle estimation; long memory stochastic volatility model (search for similar items in EconPapers)
JEL-codes: C14 C22 (search for similar items in EconPapers)
Pages: 15
Date: 2008-06-24
New Economics Papers: this item is included in nep-ecm and nep-ets
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

Downloads: (external link)
https://repec.econ.au.dk/repec/creates/rp/08/rp08_35.pdf (application/pdf)

Related works:
Journal Article: Bias-Reduced Estimation of Long-Memory Stochastic Volatility (2008)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:aah:create:2008-35

Access Statistics for this paper

More papers in CREATES Research Papers from Department of Economics and Business Economics, Aarhus University
Bibliographic data for series maintained by ().