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2013 May 20
1
How to fit a normal inverse gaussian distribution to my data using optim
...ll <- function(data,para) { m0 <- para[1] m1 <- para[2] omega <- para[3] tau <- para[4] a <- para[5] b <- para[6] beta <- para[7] theta <- para[8] gamma <- para[9] phi <- para[10] T <- nrow(data) ret <- data[,2]; rate <- data[,3] exret=100*(ret+1-((rate/100)+1)^(1/365)) h = rep(0,T); vx = rep(0,T); h[1] = 10000*exret[1]^2 vx[1] = (exret[1]-m0-(m1+beta*((gamma^0.5)/(gamma^2+beta^2)^0.5))*h[1])/h[1] for ( i in (2:T) ) { h[i] = (omega+a*(abs(h[i-1]*vx[i-1])-tau*h[i-1]*vx[i-1])^theta+b*(h[i-1]^theta))^(1/theta)...