diag_mv_ch_model: Multivariate Conditional Heteroscedasticity Model Checking
In sebinum/baqgarchutil: Analyse a Fitted baqGARCH Model

Description Usage Arguments Details Value References See Also Examples

Apply four portmanteau test statistics to check the validity of a fitted multivariate volatility model.

1	diag_mv_ch_model(x, lags = c(8, 10, 12), baq_err_cor = TRUE)

`x`	A `list` of two `data.frame`s / `matrices` containing: Element 1 - eps: Residuals for a k-dimensional asset return series (return series minus conditional mean / residuals of the mean-equation). Element 2 - cnd_h: The fitted volatility matrices (estimated conditional covariance matrix). Has to have k^2 columns. Can handle `baq_nif` class objects.
`lags`	The number of lags used in the tests. Defaults to 10.
`baq_err_cor`	Logical switch if potential negative variances should be handled for `baq_nif` object. This is achieved by removing all observations up to the last negative variance in descending order from eps and cnd_h.

For the four test statistics employed check the details section of link{mv_ch_tests}. For the transformation of eps & cnd_h check the details section of diag_std_et_cnd. For an comprehensive explanation see the references, especially Tsay (2014).

Various test statistics and their p-values.

Ljung G. & Box G. E. P. (1978). On a measure of lack of fit in time series models. Biometrika 66: 67-72.

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.

Ling, S. & Li, W. K. (1997). Diagnostic checking of nonlinear multivariate time series with multivariate ARCH errors. Journal of Time Series Analysis, 18: 447–464.

Tse, Y. K. (2002). Residual-based diagnostics for conditional heteroscedasticity models. Econometric Journal, 5: 358–373.

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_std_et_cnd, diag_dufour_roy, diag_ljung_box

# create heteroscedastic data
dat <- mgarchBEKK::simulateBEKK(2, 150)
eps <- data.frame(eps1 = dat$eps[[1]], eps2 =dat$eps[[2]])

# fit a GARCH model
gjr <- mGJR(eps[, 1], eps[, 2])

# conditional covariance matrices
cnd_h <- matrix(unlist(gjr$H.estimated), ncol = 4, byrow = TRUE)

#diagnostics on estimated conditional covariance matrices
diag_mv_ch_model(x = list(as.matrix(eps), cnd_h))

#alternative for a fitted baq_nif object
baq <- baq_nifunction(gjr)
diag_mv_ch_model(baq)