bounds on the parameter tau as described in (Lee, 2010).
Each paramter is defaulted to lie in the range (2,10)
dccBounds
bounds on the paramter theta as described in (Lee, 2010).
Each paramter is defaulted to lie in the range (0,1)
w
proportion of entries to consider in initializing correlation for
for each regime. It is defualted to split data equally across
all regimes
...
addition control paramters that can be passed to the control object
in DEoptim
Details
This method takes in returns data and the number of regimes and fits
sepearate covariances to each regime using the Expectation Maximization
algorithm decribed in (Lee, 2010). IS-DCC model avoids the path dependency
problem observed in other regime switching models and makes the solution more
tractable by running a separate DCC process for each regime.
Fitting the IS-DCC model to data corresponds works in two steps. In the first
step a time varying univariate volatility process, GARCH(1,1) is fitted to
each time series. In the second step DCC parameters for each state
are estimated along with the transition probabilities corresponding to the
Hidden Markov model. This is done by maximising the log-likelihood of
observing the residuals