R/logdensity2loglik.R

In mfrdixon/MLEMVD: Maximum Likelihood Estimation for Diffusion Equations

#' Compute the log likelihood function of the diffusion model
#'
#' @param logdensity the model log likelihood function
#' @param x Time series of the observed state variables
#' @param del The uniform time step between observations
#' @param param The parameter vector
#' @param args Specifiy whether to use implied vol
#' @export output: the score of the MLE
#' @examples
#'  logdensity2loglik(ModelU1,c(0.1,0.2,0.13,0.14),0.1,c(0.01,0.2))
#'

logdensity2loglik<-function(logdensity,x,del,param,args=NULL){
# Inputs:
#
# logdensity: is the function handle of the transition density with the
# structure such as logdensity(x,x0,del,param)
#
# x: the price series nxk, n is the number of observations and k is the
# dimension of the multivariate series
#
# del: sampling interval
# param: parameter vector

# example input of x
# x<-cbind(c(1,2,3), c(3,4,5))
  y <- as.matrix(x)
  n <- dim(y)[1] - 1
  output <- list()
  output$llk <- 0
  if (is.null(args)==FALSE)
  {
    if (args$mode=='implied')
    {
      output$v <- rep(0,n+1)
      output$v[1] <-args$v_0
    }
  }
  for (i in 1:n){
      m <- logdensity(y[i+1,],y[i,],del,param,args)
      output$llk <- output$llk + m$llk  # update the log likelihood function
      if (is.null(args)==FALSE)
      {
        if (args$mode=='implied')
        {
           output$v[i+1] <- m$v              # update the implied volatility
        }
      }
  }

  return(output)
}