R/ModelHeston.R

Defines functions ModelHeston

Documented in ModelHeston

#' Compute the maximum likelihood estimate of the Heston model
#'
#' Heston model:
#' \eqn{dln(S_t) = (\mu - \sigma^2/2) dt + \sqrt{V_t}dW_t^{1}}
#' \eqn{dV_t = \kappa(\theta - V_t)dt  + \sigma \sqrt{V_t}dW_t^{2}}
#' 4 parameters to be estimated: (rho, kappa, theta, sigma)
#'
#' @param x Observation of the state variable at time t
#' @param x0 Observation of the state variable at time t-1
#' @param del The time step between the current and previous observation
#' @param param The parameter 4-vector (rho,kappa>0,theta>0,sigma>0)
#' @param args Optional additional arguments
#' @export output a list with a llk variable storing the result of the log likelihood calculation
#' @examples
#' \dontrun{
#' ModelHeston(0.4,0.3,0.1,c(0.1,0.3,0.2,0.1))
#' }


# The Heston price, vega and the implied volatility approximation is computed in c++.
# filename<-paste(getwd(),'/src/HestonFourierCosine.cpp',sep='')
# sourceCpp(filename)

ModelHeston<- function(x,x0,del,param,args=NULL){

  output <- list()
  rho   <- param[1]
  kappa <- param[2]
  theta <- param[3]
  sigma <- param[4]

  if (!is.null(args)){

   if (args$mode=='implied')
   {
    S<-exp(x[1])
    K<-S
    objfun<-function(v){
      objfun<- HestonCOS(S,K,args$T_0,args$rate,args$q,param[4],param[2],param[3],v,param[1],args$callput, args$N)
    }

    args$v_0 <- getImpliedVolatility(S,x[3],K,args$T_0,args$rate,args$q,param[4],param[2],param[3],args$v_0,param[1],args$callput, 0.01,100, args$N) # the implied volatility
    #print(paste('implied vol estimate: ',v_0))
    # calculate the vega to obtain the Jacobian
    output$v <- args$v_0
    dVdv0 <- HestonVega(S,K,args$T_0,args$rate,args$q,sigma,kappa,theta,args$v_0,rho,args$callput,args$N)

    #print(paste('Jacobian: ',dVdv0))
    #print(paste('Numerical Jacobian', grad(objfun,x[2])))
    J <- dVdv0
    if (is.nan(log(J))){
      #print(paste('Error: NAN occured in ModelHeston.R: ',J,sigma,kappa,theta,rho,args$v_0,args$T_0))
      output$llk <-0
    }
    else
      output$llk <- -log(J)
   }
  }

  # m,a,b,s,r
  param_prime <- c(args$rate-args$q, kappa*theta, kappa, sigma, rho)
  model <- ModelB6(x,x0,del,param_prime)$llk
  if(is.nan(model)){
    print("Warning: Model likelihood is nan")
    print(param_prime)
    model <- 0
  }
  if (is.null(output$llk))
  {
    output$llk <- 0
  }

  output$llk <- output$llk + model

  return(output)
}
mfrdixon/MLEMVD documentation built on May 1, 2018, 11:38 p.m.