MCholV function

Multivariate Cholesky Volatility Model

Use Cholesky decomposition to obtain multivariate volatility models

MCholV(rtn, size = 36, lambda = 0.96, p = 0)

Arguments

• rtn: A T-by-k data matrix of a k-dimensional asset return series.
• size: The initial sample size used to start recursive least squares estimation
• lambda: The exponential smoothing parameter. Default is 0.96.
• p: VAR order for the mean equation. Default is 0.

Details

Use recursive least squares to perform the time-varying Cholesky decomposition. The least squares estimates are then smoothed via the exponentially weighted moving-average method with decaying rate 0.96. University GARCH(1,1) model is used for the innovations of each linear regression.

Returns

• betat: Recursive least squares estimates of the linear transformations in Cholesky decomposition

• bt: The transformation residual series

• Vol: The volatility series of individual innovations

• Sigma.t: Volatility matrices

References

Tsay (2014, Chapter 7)

Author(s)

Ruey S. Tsay

See Also

fGarch