Hello,
I am trying to estimate parameters of mean reverting process with jumps given
by: dp=k(mu-p)dt+sigma*dz+Jdq where dp represents change in log of price, k is
reversion factor, mu is long run level of price, sigma is standard deviation,
and dq equals one with probability lambda if jump occurs and 1-lambda
otherwise. J is jump size with mean muj and standard deviation delta.
Therefore, when there is no jump, mean of the distribution is just expected
value of mean reverting process which in this case is E(p(t))=b0+b1*p(t-1)
because it can be written as AR(1) with standard deviation equal to sigma. When
jump occurs, mean is equal to b0+b1*p(t-1)+muj and standard deviation is equal
to sqrt(sigma^2+delta^2)
The code I use is:
p<-matrix(data$p) # price at time t
lp<-cbind(1,data$lp) # price at time t-1
mle <- function(theta) {
sigma<-theta[1]
delta<-theta[2]
lambda<-theta[3]
muj<-theta[4]
b<- theta[5:6]
e<-lp%*%b
#I have omitted 2pi from likelihood function
logl<- sum(log(
(((1-lambda)/sigma)*exp(-t(p-e)%*%(p-e)))/(2*sigma^2)+((lambda)/sqrt(sigma^2+delta^2))*exp(-t(p-e-muj)%*%(p-e-muj))/(2*((delta^2+sigma^2))))
)
return(-logl)
}
out <- optim(c(1,1,1,1,1,1),fn=mle, method = "BFGS")
Unfortunately I get the error that Error in optim(c(1, 1, 1, 1, 1, 1), fn =
mle, method = "BFGS") : initial value in 'vmmin' is not finite
Can someone help.
Thanks,
Jurica Brajkovic
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