Description Usage Arguments Details Value Author(s) References See Also Examples
Multiple Series Realized Variance (MSRV) is a generalization of the TSRV estimator of integrated volatility. It uses multiple time scales to account for the effects of additive market microstructure noise.
1 2 | variance_msrv(estimator,K=2,J=1)
variance_msrvRolling(estimator,K=2,J=1,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) |
J |
number of subsamples in the fast time series (default: 1) |
wLength |
Length of a rolling window for rolling estimators. Default window length is 23400 (number of seconds in a trading day) |
- Convergence speed: m^{1/4} (m - number of observation)
- Accounts for additive noise: yes
- Accounts for finite price jumps: no
- Accounts for time dependence in noise: yes
- Accounts for endogenous effects in noise: no
a numeric vector of the same length as input data.
Kostin Andrey <andrey.kostin@portfolioeffect.com>
Zhang, L. (2006). Efficient estimation of stochastic volatility using noisy observations: A multiscale approach.
variance_rv
variance_tsrv
variance_jrmrv
variance_mrv
variance_uzrv
variance_krv
1 2 3 4 5 6 7 8 9 10 11 |
## Not run:
data(spy.data)
estimator=estimator_create(priceData=spy.data)
estimator_settings(estimator,
inputSamplingInterval = '10s',
resultsSamplingInterval = '10s')
util_plot2d(variance_msrv(estimator),title='MSRV',legend='Simple')+
util_line2d(variance_msrvRolling(estimator,wLength=3600),legend='Rolling Window')
## End(Not run)
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