fit.mst {QRMlib}R Documentation

Fit Multivariate Student t Distribution

Description

fits multivariate Student's t distribution using variant of EM algorithm

Usage

fit.mst(data, nit=2000, tol=1e-10)

Arguments

data matrix of data where rows are vector observations. A good choice might be data.t.5d = rmghyp(n,lambda=(-nu/2),chi=nu,psi=0,Sigma=P,mu=mu,gamma=gamma)
nit number of iterations of EM-type algorithm
tol tolerance of improvement for stopping iteration

Details

see page 75 of QRM

Value

list containing parameter estimates, standard errors and details of fit

Author(s)

documentation by Scott Ulman for R-language distribution

See Also

fit.mNH, fit.NH, fit.st

Examples

data(DJ);
Ret.DJ <- mk.returns(DJ); 
window1.start <- timeDate("01/01/1993",format="%m/%d/%Y"); 
window1.end <-  timeDate("12/31/2000",format="%m/%d/%Y"); 
sample1 <- (seriesPositions(Ret.DJ) > window1.start 
            & seriesPositions(Ret.DJ) < window1.end); 
selection1 <- c("AXP","EK","BA","C","KO","MSFT",
              "HWP","INTC","JPM","DIS"); 
DJ30daily <- Ret.DJ[sample1,selection1]; 
DJ30weekly <-  aggregateWeeklySeries(DJ30daily, FUNC= colSums); 
mod.t <- fit.mst(DJ30weekly);

[Package QRMlib version 1.4.4 Index]