rcov: Realized Covariance

In starvars: Vector Logistic Smooth Transition Models Estimation and Prediction

Description

Function returns the vectorization of the lowest triangular of the Realized Covariance matrices for different frequencies.

Usage

rcov(
  data,
  freq = c("daily", "monthly", "quarterly", "yearly"),
  make.ret = TRUE,
  cholesky = FALSE
)

Arguments

data	a (T x N) xts object containing the N price/return series over period T
freq	a string defining the desired frequency for the Realized Covariance matrices between "daily", "monthly", "quarterly" or "yearly"
make.ret	boolean, in case it is TRUE the data are converted in returns, FALSE otherwise
cholesky	boolean, in case it is TRUE the Cholesky factors of the Realized Covariance matrices are calculated, FALSE by default

Value

Realized Covariances	a M \times N(N+1)/2 matrix of realized covariances, where M is the number of lower frequency data
Cholesky Factors (optional)	a M \times N(N+1)/2 matrix of Cholesky factors of the realized covariance matrices, where M is the number of lower frequency data
returns (optional)	a T \times N matrix of returns, when make.ret = TRUE

Author(s)

Andrea Bucci

References

Andersen T.G., Bollerslev T., Diebold F.X. and Labys P. (2003), Modeling and Forecasting Realized Volatility. Econometrica. 71: 579-625

Barndorff-Nielsen O.E. and Shephard N. (2002), Econometric analysis of realised volatility and its use in estimating stochastic volatility models Journal of the Royal Statistical Society. 64(2): 253-280

Examples

data(Sample5minutes)
rc <- rcov(Sample5minutes, freq = 'daily', cholesky = TRUE, make.ret = TRUE)
print(rc)

starvars documentation built on Jan. 18, 2022, 1:08 a.m.