  An aggregated method of nonparametric estimators based on time-domain and
state-domain estimators is proposed and studied. To attenuate the curse of
dimensionality, we propose a factor modeling strategy. We first investigate the
asymptotic behavior of nonparametric estimators of the volatility matrix in the
time domain and in the state domain. Asymptotic normality is separately
established for nonparametric estimators in the time domain and state domain.
These two estimators are asymptotically independent. Hence, they can be
combined, through a dynamic weighting scheme, to improve the efficiency of
volatility matrix estimation. The optimal dynamic weights are derived, and it
is shown that the aggregated estimator uniformly dominates volatility matrix
estimators using time-domain or state-domain smoothing alone. A simulation
study, based on an essentially affine model for the term structure, is
conducted, and it demonstrates convincingly that the newly proposed procedure
outperforms both time- and state-domain estimators. Empirical studies further
endorse the advantages of our aggregated method.
