As with Litterman, the prior mean of
is assumed to be all zero, other than the own first lag terms. However the Ghysels prior differs slightly by allowing a different hyper-parameter for the own lag term depending on whether a variable is high or low frequency. Another crucial difference is that the variables are assumed to follow an AR(1) process in the high-frequency, so that when estimating in low-frequency space, the parameter is exponential.