A Tale of Two Averagings: Estimating the Integrated Volatility Using 'Pooled' High-Frequency Data

2015 ◽  
Author(s):  
Zhi Liu ◽  
Xinbing Kong ◽  
Bingyi Jing
Keyword(s):  
High Frequency ◽  
High Frequency Data ◽  
Frequency Data ◽  
Integrated Volatility

FAST CONVERGENCE RATES IN ESTIMATING LARGE VOLATILITY MATRICES USING HIGH-FREQUENCY FINANCIAL DATA

2013 ◽  
Vol 29 (4) ◽  
pp. 838-856 ◽  
Author(s):  
Minjing Tao ◽  
Yazhen Wang ◽  
Xiaohong Chen
Keyword(s):  
Sample Size ◽  
High Frequency ◽  
Convergence Rates ◽  
Fast Convergence ◽  
High Frequency Data ◽  
Frequency Data ◽  
Microstructure Noise ◽  
Finite Sample ◽  
Integrated Volatility ◽  
The Matrix

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.

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