FAST CONVERGENCE RATES IN ESTIMATING LARGE VOLATILITY MATRICES USING HIGH-FREQUENCY FINANCIAL DATA
Financial 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...
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| Veröffentlicht in: | Econometric theory 2013-08, Vol.29 (4), p.838-856 |
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| container_title | Econometric theory |
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| creator | Tao, Minjing Wang, Yazhen Chen, Xiaohong |
| description | Financial 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. |
| doi_str_mv | 10.1017/S0266466612000746 |
| format | Article |
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| source | Cambridge Journals Online; JSTOR |
| subjects | Assets Consistent estimators Convergence Covariance Data processing Econometrics Economic models Economic theory Eigenvalues Estimating techniques Estimators Finance Frequency Matrices Noise Perceptron convergence procedure Prices Random variables Sample size Samples Simulation Simulations Statistical estimation Studies Volatility |
| title | FAST CONVERGENCE RATES IN ESTIMATING LARGE VOLATILITY MATRICES USING HIGH-FREQUENCY FINANCIAL DATA |
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