rRealizedVariance {realized} | R Documentation |
Calculate realized variance, covariance, correlation, covariance matrix, or correlation matrix.
rRealizedVariance(x, y = NULL, type = "naive", period = 1, lags = 1, cor = FALSE, args = list(), cts = TRUE, makeReturns = FALSE)
x |
RealizedObject or TimeSeries for S+ |
y |
RealizedObject or TimeSeries for S+ |
period |
Sampling period |
type |
Type of realized estimator to use, a rv. or rc. is appended to this value and that function is called |
lags |
Number of lags or subgrids for kernel and subsample based estimators |
cor |
T for correlation |
args |
List of extra parameters to pass into rv.* or rc.* |
cts |
Create calendar time sampling if a non realizedObject is passed |
makeReturns |
Prices are passed make them into log returns |
Calculate realized variance, covariance, correlation, covariance matrix, or correlation matrix.
A single numeric value or a matrix if x is multicolumn matrix.
Scott Payseur <spayseur@u.washington.edu>
rc.avg
, rc.kernel
, rc.naive
, rc.timescale
, rv.avg
, rv.kernel
, rv.naive
, rv.timescale
data(msft.real.cts) data(ge.real.cts) # Traditional Estimate at highest frequency rRealizedVariance(x=msft.real.cts[[1]], y=ge.real.cts[[1]], type="naive", period=1) # Traditional Estimate at one minute frequency rRealizedVariance(x=msft.real.cts[[1]], y=ge.real.cts[[1]], type="naive", period=1, args=list(align.period=60)) # Traditional Estimate at 10 minute frequency rRealizedVariance(x=msft.real.cts[[1]], y=ge.real.cts[[1]], type="naive", period=10, args=list(align.period=60)) # Bartlett Kernel Estimate with minute aligned data at 20 lags rRealizedVariance(x=msft.real.cts[[1]], y=ge.real.cts[[1]], type="kernel", lags=20, args=list(align.period=60, type="Bartlett")) # Cubic Kernel Estimate with second aligned data at 400 lags rRealizedVariance(x=msft.real.cts[[1]], y=ge.real.cts[[1]], type="kernel", lags=400, args=list(type="Cubic")) # Lead-Lag with one lag at one minute frequency rRealizedVariance(x=msft.real.cts[[1]], y=ge.real.cts[[1]], type="kernel", lags=1, args=list(align.period=60)) # Subsample Average Estimate with second aligned data at 600 subgrids rRealizedVariance(x=msft.real.cts[[1]], y=ge.real.cts[[1]], type="avg", period=600) # Traditional Estimate at highest frequency rRealizedVariance(x=merge(msft.real.cts[[1]], ge.real.cts[[1]]), type="naive", period=1) # Traditional Estimate at 10 minute frequency rRealizedVariance(x=merge(msft.real.cts[[1]], ge.real.cts[[1]]), type="naive", period=10, args=list(align.period=60)) # Lead-Lag with one lag at one minute frequency> rRealizedVariance(x=merge(msft.real.cts[[1]], ge.real.cts[[1]]), type="kernel", lags=1, args=list(align.period=60)) # Subsample Average Estimate with second aligned data at 600 subgrids rRealizedVariance(x=merge(msft.real.cts[[1]], ge.real.cts[[1]]), type="avg", period=600) # Traditional Estimate at highest frequency rRealizedVariance(x=msft.real.cts[[1]], y=ge.real.cts[[1]], type="naive", period=1, cor=TRUE) # Traditional Estimate at one minute frequency rRealizedVariance(x=msft.real.cts[[1]], y=ge.real.cts[[1]], type="naive", period=1, args=list(align.period=60), cor=TRUE) # Traditional Estimate at 10 minute frequency rRealizedVariance(x=msft.real.cts[[1]], y=ge.real.cts[[1]], type="naive", period=10, args=list(align.period=60), cor=TRUE) # Bartlett Kernel Estimate with minute aligned data at 20 lags rRealizedVariance(x=msft.real.cts[[1]], y=ge.real.cts[[1]], type="kernel", lags=20, args=list(align.period=60, type="Bartlett"), cor=TRUE) # Cubic Kernel Estimate with second aligned data at 400 lags rRealizedVariance(x=msft.real.cts[[1]], y=ge.real.cts[[1]], type="kernel", lags=400, args=list(type="Cubic"), cor=TRUE) # Lead-Lag with one lag at one minute frequency rRealizedVariance(x=msft.real.cts[[1]], y=ge.real.cts[[1]], type="kernel", lags=1, args=list(align.period=60), cor=TRUE) # Subsample Average Estimate with second aligned data at 600 subgrids rRealizedVariance(x=msft.real.cts[[1]], y=ge.real.cts[[1]], type="avg", period=600, cor=TRUE) # Correlation Matrices # Traditional Estimate at highest frequency rRealizedVariance(x=merge(msft.real.cts[[1]], ge.real.cts[[1]]), type="naive", period=1, cor=TRUE) # Traditional Estimate at 10 minute frequency rRealizedVariance(x=merge(msft.real.cts[[1]], ge.real.cts[[1]]), type="naive", period=10, args=list(align.period=60), cor=TRUE) # Lead-Lag with one lag at one minute frequency rRealizedVariance(x=merge(msft.real.cts[[1]], ge.real.cts[[1]]), type="kernel", lags=1, args=list(align.period=60), cor=TRUE) # Subsample Average Estimate with second aligned data at 600 subgrids > rRealizedVariance(x=merge(msft.real.cts[[1]], ge.real.cts[[1]]), type="avg", period=600, cor=TRUE)