Bias-Reduced estimation of Long-memory Stochastic Volatility

Per Frederiksen; Morten Ørregaard Nielsen

doi:10.1093/jjfinec/nbn009

Bias-Reduced estimation of Long-memory Stochastic Volatility

Per Frederiksen^*, Morten Ørregaard Nielsen

^*Corresponding author for this work

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaper › Journal article › Research › peer-review

10 Citations (Scopus)

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 nonstationarity 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, n^1/2. 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.

Original language	English
Journal	Journal of Financial Econometrics
Volume	6
Issue	4
Pages (from-to)	496-512
Number of pages	17
ISSN	1479-8409
DOIs	https://doi.org/10.1093/jjfinec/nbn009
Publication status	Published - 2008
Externally published	Yes

Keywords

Bias reduction
Local Whittle estimation
Long memory stochastic volatility model

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10.1093/jjfinec/nbn009

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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 nonstationarity 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.",

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AB - 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 nonstationarity 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.

KW - Bias reduction

KW - Local Whittle estimation

KW - Long memory stochastic volatility model

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ER -

Bias-Reduced estimation of Long-memory Stochastic Volatility

Abstract

Keywords

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