Aggregation of Nonparametric Estimators for Volatility Matrix

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dc.creatorFan, Jianqing
dc.creatorFan, Yingying
dc.creatorLv, Jinchi
dc.date2007-01-03
dc.date.accessioned2026-07-07T08:08:34Z
dc.date.available2026-07-07T08:08:34Z
dc.descriptionAn 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.
dc.description46 pages, 11 PostScript figures
dc.identifierhttps://arxiv.org/abs/math/0701107
dc.identifierhttp://arxiv.org/abs/math/0701107
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/131307
dc.subjectStatistics Theory
dc.subject62G05; 62G20; 62M05
dc.titleAggregation of Nonparametric Estimators for Volatility Matrix
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