noise_urnv: Unbiased Rescaled Noise Variance
In PortfolioEffectEstim: High Frequency Price Estimators by PortfolioEffect

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

Unbiased Rescaled Noise Variance (URNV) corrects for a bias of Rescaled Noise Variance.

1	noise_urnv(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: no

- Accounts for time dependence in noise: no

- Accounts for endogenous effects in noise: no

a numeric vector of the same length as input data.

Kostin Andrey <andrey.kostin@portfolioeffect.com>

L. Zhang, P. A. Mykland, and Y. Ait-Sahalia, "A tale of two time scales: Determining integrated volatility with noisy high-frequency data," Journal of the American Statistical Association, vol. 100, No. 472, pp. 1394-1411, December 2005.

noise_rnv noise_acnv noise_uznv

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

## End(Not run)