Estimation of large volatility matrix for high-frequency financial data
Seminar Room 1, Newton Institute
Statistical theory for estimating large covariance matrix shows that even for noiseless synchronized high-frequency financial data, the existing realized volatility based estimators of integrated volatility matrix of p assets are inconsistent, for large p (the number of assets and large n (the sample size for high-frequency data). This paper proposes new types of estimators of integrated volatility matrix for noisy non-synchronized high-frequency data. We show that when both n and p go to infinity with p/n approaching to a constant, the proposed estimators are consistent with good convergence rates. Our simulations demonstrate the excellent performance of the proposed estimators under complex stochastic volatility matrices. We have applied the methods to high-frequency data with over 600 stocks.
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