diag_std_et: Standardized Univariate Series e_t
In sebinum/baqgarchutil: Analyse a Fitted baqGARCH Model

Description Usage Arguments Value Details References See Also Examples

Transform a k-dimensional series a_t to the standardized univariate series e_t.

1	diag_std_et(x)

`x`	A `matrix` / `data.frame` / `numeric vector` of (multivariate) financial time series. Each column contains a series, each row an observation of the series.

The standardized univariate series e_t of x as a matrix.

A k-dimensional series a_t can be transformed to a standardized univariate series e_t:

e_t = a'_t * ∑^-1 * a_t - k

where ∑ denotes the unconditional covariance matrix of the k-dimensional series a_t.

Dufour J. M. & Roy, R. (1985). The t copula and related copulas. Working Paper. Department of Mathematics, Federal Institute of Technology.

Dufour J. M. & Roy, R. (1986). Generalized portmanteau statistics and tests of randomness. Communications in Statistics-Theory and Methods, 15: 2953-2972.

Tsay, R. S. (2014). Multivariate Time Series Analysis with R and Financial Applications. John Wiley. Hoboken, NJ.

Tsay, R. S. (2015). MTS: All-Purpose Toolkit for Analyzing Multivariate Time Series (MTS) and Estimating Multivariate Volatility Models. R package version 0.33.

diag_dufour_roy & diag_ljung_box for Test Statistics for the transformed standardized series diag_std_et

# create heteroscedastic data
dat <- mgarchBEKK::simulateBEKK(3, 500)
eps <- data.frame(eps1 = dat$eps[[1]], eps2 = dat$eps[[2]],
                  eps3 = dat$eps[[3]])

# transform to standardized univariate series e_t
et <- diag_std_et(eps)