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Abstract:
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An aggregated method of nonparametric estimators based on time and state domain estimators is proposed. To attenuate the curse of dimensionality, we propose a factor modeling strategy. We first investigate the asymptotic behaviors of the volatility matrix estimators in the time and state domains. The asymptotic normality is separately established for them. They are asymptotically independent. Hence, they can be combined to improve the efficiency of the estimated volatility matrix. The optimal dynamic weights are derived and it is shown that the aggregated estimator uniformly dominates the estimators using time or state domain smoothing alone. A simulation study, based on an essentially affine model, is conducted and it demonstrates convincingly that the new procedure outperforms both time and state domain estimators. Empirical studies endorse further the advantages of our method.
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