rc.kernel {realized}R Documentation

Realized Covariance: Kernel

Description

Realized covariance calculation using a kernel estimator.

Usage

rc.kernel(x, y, q, align.period = 1, adj = TRUE, type = 0, cts = TRUE, makeReturns = FALSE,...)

Arguments

x RealizedObject or TimeSeries for S+
y RealizedObject or TimeSeries for S+
q Number of lags
align.period Align the returns to this period first
cts Create calendar time sampling if a non realizedObject is passed
makeReturns Prices are passed make them into log returns
adj T to use dof adjustment
type 0-11 or a character string
... ...

Details

The different types of kernels can be found using rKernel.available().

Value

Kernel estimate of realized covariance.

Author(s)

Scott Payseur <spayseur@u.washington.edu>

References

Ole E. Barndorff-Nielsen, Peter Reinhard Hansen, Asger Lunde, and Neil Shephard. Regular and modified kernel-based estimators of integrated variance: The case with independent noise. Working Paper, 2004.

Michiel de Pooter, Martin Martens, and Dick van Dijk. Predicting the daily covariance matrix for sp100 stocks using intraday data - but which frequency to use? Working Paper, October 2005.

J. E. Griffen and R. C. A. Oomen. Covariance measurement in the presence of non-synchronous trading and market microstructure noise. Working Paper, June 27th, 2006.

See Also

rRealizedVariance,rv.kernel, rKernel.available,rKernel

Examples


data(msft.real.cts)
data(ge.real.cts)

# kernel realized covariance for CTS aligned at one minute returns
#
rc.kernel(x = msft.real.cts[[1]], y = ge.real.cts[[1]], q=1, type="bartlett", align.period=60)

[Package realized version 0.81 Index]