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008			240319b |||||||| |||| 00| 0 eng d
022			_a02664666
040			_aMSU _bEnglish _cMSU _erda
050	0	0	_aHB139.T52 ECO
100	1		_aTao, Minjing _eauthor
245	1	0	_aFast convergence rates in estimating large volatility matrices using high frequency financial data _ccreated by Minjing Tao, Yazhen Wang and Xiaohong Chen
264	1		_aCambridge: _bCambridge University Press, _c2013.
336			_2rdacontent _atext _btxt
337			_2rdamedia _aunmediated _bn
338			_2rdacarrier _avolume _bnc
440			_aEconometric Theory _vVolume 29, number 4
520	3		_aFinancial practices often need to estimate an integrated volatility matrix of a large number of assets using noisy high-frequency data. Many existing estimators of a volatility matrix of small dimensions become inconsistent when the size of the matrix is close to or larger than the sample size. This paper introduces a new type of large volatility matrix estimator based on nonsynchronized high-frequency data, allowing for the presence of microstructure noise. When both the number of assets and the sample size go to infinity, we show that our new estimator is consistent and achieves a fast convergence rate, where the rate is optimal with respect to the sample size. A simulation study is conducted to check the finite sample performance of the proposed estimator.
650			_aEstimation _vVolatility _xEstimation theory
650			_aTime series analysis _vFinancial market _xForecasting model
700	1		_aWang, Yazhen _eco author
700	1		_aChen, Xiaohong _eco author
856			_uhttps://doi.org/10.1017/S0266466612000746
942			_2lcc _cJA
999			_c164435 _d164435