noise_acnv: Autocovariance Noise Variance
In PortfolioEffectEstim: High Frequency Price Estimators by PortfolioEffect

Description Usage Arguments Details Value Author(s) References See Also Examples

Autocovariance Noise Variance (ACNV) estimates the noise variance based on the autocovariance of returns, rather than the Rescaled Noise Variance (RNV). It is generally preferred to RNV as it leads to a reduction in MSE and is robust to the presence of rare jumps. Also, this approach can be extended straightforwardly to estimate the parameters of higher order noise dependence.

1	noise_acnv(estimator)

estimator

Vector of (time, price) observations for market asset when external market data is used.

- Convergence speed: m^{1/2} (m - number of observation)

- Accounts for additive noise: yes

- Accounts for finite price jumps: yes

- Accounts for time dependence in noise: yes

- Accounts for endogenous effects in noise: no

a numeric vector of the same length as data.

Kostin Andrey <andrey.kostin@portfolioeffect.com>

R. C. Oomen, "Comment on realized variance and market microstructure noise by peter r. hansen and asger lunde," pp. 1-15, 23 September, 2005.

noise_rnv noise_urnv noise_uznv

 
## Not run: 
data(spy.data) 
estimator=estimator_create(priceData=spy.data)
estimator_settings(estimator,
				   inputSamplingInterval = '10s',
				   resultsSamplingInterval = '10s')
util_plot2d(noise_acnv(estimator),title="ACNV")

## End(Not run)