Supplementary Material for "Dependent Microstructure Noise and Integrated Volatility Estimation from High-Frequency Data''

2019 ◽  
Author(s):  
Z. Merrick Li ◽  
Michel Vellekoop ◽  
Roger Jean Auguste Laeven
Keyword(s):  
High Frequency ◽  
High Frequency Data ◽  
Frequency Data ◽  
Microstructure Noise ◽  
Volatility Estimation ◽  
Integrated Volatility ◽  
Supplementary Material

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.

scholarly journals Bivariate Volatility Modeling with High-Frequency Data

Econometrics ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 41 ◽  
Author(s):  
Matei ◽  
Rovira ◽  
Agell
Keyword(s):  
High Frequency ◽  
Mathematical Structure ◽  
Conditional Variance ◽  
High Frequency Data ◽  
Conditional Heteroskedasticity ◽  
Frequency Data ◽  
Volatility Estimation ◽  
Structure Information ◽  
Volatility Modeling ◽  
Bivariate Models

We propose a methodology to include night volatility estimates in the day volatility modeling problem with high-frequency data in a realized generalized autoregressive conditional heteroskedasticity (GARCH) framework, which takes advantage of the natural relationship between the realized measure and the conditional variance. This improves volatility modeling by adding, in a two-factor structure, information on latent processes that occur while markets are closed but captures the leverage effect and maintains a mathematical structure that facilitates volatility estimation. A class of bivariate models that includes intraday, day, and night volatility estimates is proposed and was empirically tested to confirm whether using night volatility information improves the day volatility estimation. The results indicate a forecasting improvement using bivariate models over those that do not include night volatility estimates.

Volatility Estimation Based on High-Frequency Data

2011 ◽  
pp. 335-369 ◽  
Author(s):  
Christian Pigorsch ◽  
Uta Pigorsch ◽  
Ivaylo Popov
Keyword(s):  
High Frequency ◽  
High Frequency Data ◽  
Frequency Data ◽  
Volatility Estimation
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