R help - May 2013 - How to fit a normal inverse gaussian distribution to my data using optim

Dear R Help
Please forgive my lack of knowledge,.I would be very thankful for some help. 
Here is my problem:
I was using optim to estimate parameters of a model and I get this error message
"Error in optim(x0, fn = riskll, method = "L-BFGS-B", lower =
lbs, upper = ubs,  :
L-BFGS-B needs finite values of 'fn'"



Below is the R code I have written.


library('GeneralizedHyperbolic')
data=read.table(file="MSCI_USA.csv",sep=',',header=T)
data=data[1:8173,]
#starting value
x0 <- c(-0.011,0.146, 0.013,   0.639, 0.059,0.939,  -0.144  ,  1.187,   
1.601,      -0.001)
#lower bound and upper bound
lbs <- c(-5,    -5,    -5,  -0.99999, 0.00001,   0,   -1,    0.1,   
1.2000001,    -2)
ubs<-  c( 5,     5,    10,   0.99999,    5,      2,     0,    3,      1000,  
10)
#the likelihood function
riskll <- 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)
    vx[i] =
(exret[i]-phi*exret[i-1]-m0-(m1+beta*((gamma^0.5)/(gamma^2+beta^2)^0.5))*h[i])/h[i]
  }
  
  mu = -1*beta*((gamma^0.5)/(gamma^2+beta^2)^0.5)
  delta=((gamma^1.5)/(gamma^2+beta^2)^0.5)
  alpha=gamma
  beta=beta
  param = c(mu, delta, alpha, beta)
  riskll <- -1*sum(log(dnig(vx,param=param)))
  
  return(riskll)
}




#optimization
optim(x0,fn=riskll,method ="L-BFGS-B",lower=lbs,upper=ubs, data =
data)
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Dear R Help
Please forgive my lack of knowledge,.I would be very thankful for some help.
Here is my problem:
I was using optim to estimate parameters of a model and I get this error message
"Error in optim(x0, fn = riskll, method = "L-BFGS-B", lower =
lbs, upper = ubs,  :
L-BFGS-B needs finite values of 'fn'"



Below is the R code I have written.


library('GeneralizedHyperbolic')
data=read.table(file="MSCI_USA.csv",sep=',',header=T)
data=data[1:8173,]
#starting value
x0 <- c(-0.011,0.146, 0.013,   0.639, 0.059,0.939,  -0.144  ,  1.187,   
1.601,      -0.001)
#lower bound and upper bound
lbs <- c(-5,    -5,    -5,  -0.99999, 0.00001,   0,   -1,    0.1,   
1.2000001,    -2)
ubs<-  c( 5,     5,    10,   0.99999,    5,      2,     0,    3,      1000,  
10)
#the likelihood function
riskll <- 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)
    vx[i] =
(exret[i]-phi*exret[i-1]-m0-(m1+beta*((gamma^0.5)/(gamma^2+beta^2)^0.5))*h[i])/h[i]
  }
 
  mu = -1*beta*((gamma^0.5)/(gamma^2+beta^2)^0.5)
  delta=((gamma^1.5)/(gamma^2+beta^2)^0.5)
  alpha=gamma
  beta=beta
  param = c(mu, delta, alpha, beta)
  riskll <- -1*sum(log(dnig(vx,param=param)))
 
  return(riskll)
}




#optimization
optim(x0,fn=riskll,method ="L-BFGS-B",lower=lbs,upper=ubs, data =
data)
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