isdccfit: Fit an Independent Regime Switching Model

Description Usage Arguments Details Author(s) References Examples

Description

Fit an Independent Regime Switching Model

Usage

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isdccfit(R, numRegimes = NA, transMatbounds = c(2, 10), dccBounds = c(0,
  1), w = NA, ...)

Arguments

R

xts object of asset returns

numRegimes

number of regimes to fit to the data

transMatbounds

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

Author(s)

Rohit Arora

References

Lee, H.-T. (2010). Regime switching correlation hedging. Journal of Banking &

Examples

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## Not run: 
 data("largereturn")
 model <- isdccfit(largesymdata, numRegimes=2, maxiter=50, paralletType=0)

## End(Not run)


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