variance_tsrv: Two Scales Realized Variance
In PortfolioEffectEstim: High Frequency Price Estimators by PortfolioEffect

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

Two Scale Realized Variance (TSRV) estimates integrated volatility consistently. The idea is to use realized variance type estimators over two time scales to correct the effect of additive market microstructure noise.

1 2	variance_tsrv(estimator,K=2) variance_tsrvRolling(estimator,K=2,wLength=23400)

`estimator`	Vector of (time, price) observations for market asset when external market data is used.
`K`	number of subsamples in the slow time series (default: 2)
`wLength`	Length of a rolling window for rolling estimators. Default window length is 23400 (number of seconds in a trading day)

- Convergence speed: m^{1/6} (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.

variance_rv variance_jrmrv variance_msrv variance_mrv variance_uzrv variance_krv

 
## Not run: 
data(spy.data) 
estimator=estimator_create(priceData=spy.data)
estimator_settings(estimator,
				   inputSamplingInterval = '10s',
				   resultsSamplingInterval = '10s')
util_plot2d(variance_tsrv(estimator),title='TSRV',legend='Simple')+
util_line2d(variance_tsrvRolling(estimator,wLength=3600),legend='Rolling Window')

## End(Not run)