Aggregation of nonparametric estimators for volatility matrix

Jianqing Fan Yingying Fan Jinchi Lv

Statistics Theory and Methods mathscidoc:1912.43374

Journal of Financial Econometrics, 5, (3), 321-357, 2007.4
An aggregated method of nonparametric estimators based on time-domain and state-domain estimators is proposed and studied. To attenuate the <i>curse of dimensionality</i>, 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
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@inproceedings{jianqing2007aggregation,
  title={Aggregation of nonparametric estimators for volatility matrix},
  author={Jianqing Fan, Yingying Fan, and Jinchi Lv},
  url={http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191221114025054287934},
  booktitle={Journal of Financial Econometrics},
  volume={5},
  number={3},
  pages={321-357},
  year={2007},
}
Jianqing Fan, Yingying Fan, and Jinchi Lv. Aggregation of nonparametric estimators for volatility matrix. 2007. Vol. 5. In Journal of Financial Econometrics. pp.321-357. http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191221114025054287934.
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