TY - JOUR
T1 - Bias-Reduced estimation of Long-memory Stochastic Volatility
AU - Frederiksen, Per
AU - Nielsen, Morten Ørregaard
PY - 2008
Y1 - 2008
N2 - 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.
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
UR - http://www.scopus.com/inward/record.url?scp=53849139441&partnerID=8YFLogxK
U2 - 10.1093/jjfinec/nbn009
DO - 10.1093/jjfinec/nbn009
M3 - Journal article
AN - SCOPUS:53849139441
SN - 1479-8409
VL - 6
SP - 496
EP - 512
JO - Journal of Financial Econometrics
JF - Journal of Financial Econometrics
IS - 4
ER -