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I am working with a Heston model discretization through truncation, given by the following code:

(for (i in 1:Nsteps){
X<-log(S)
X<-X+(R-0.5*pmax(V,0))*dt+sqrt(pmax(V,0))*dW1
S<-exp(X)
V<-V+kappa*(theta-pmax(V,0))*dt+sigma*sqrt(pmax(V,0))*dW2

}))

I am trying to simulate several paths let's say 10 or 100 of the stock under Hestons model above. I have used the function replicate in the following fashion:

P_A_cap_B<-replicate(10,(for (i in 1:Nsteps){
X<-log(S)
X<-X+(R-0.5*pmax(V,0))*dt+sqrt(pmax(V,0))*dW1
S<-exp(X)
V<-V+kappa*(theta-pmax(V,0))*dt+sigma*sqrt(pmax(V,0))*dW2

}))

However I am getting null values in each collum. To summarize it is not working with the for loop

If I do:

replicate(10,S)

All the values across the columns are equal, which is clearly what I did not want.

Question:

How to simulate 10 different paths of the stock?

Thanks in Advance!

Appendix(Full code):

 BlackScholesFormula  <- function (spot,timetomat,strike,r, q=0, sigma, opttype=1, greektype=1)
{ 

d1<-(log(spot/strike)+ ((r-q)+0.5*sigma^2)*timetomat)/(sigma*sqrt(timetomat))
d2<-d1-sigma*sqrt(timetomat)

if (opttype==1 && greektype==1) result<-spot*exp(-q*timetomat)*pnorm(d1)-strike*exp(-r*timetomat)*pnorm(d2)

if (opttype==2 && greektype==1) result<-spot*exp(-q*timetomat)*pnorm(d1)-strike*exp(-r*timetomat)*pnorm(d2)-spot*exp(-q*timetomat)+strike*exp(-r*timetomat)

if (opttype==4 && greektype==1) result<-(spot^2)*exp((r+sigma^2)*timetomat)
if (opttype==4 && greektype==2) result<-2*spot*exp((r+sigma^2)*timetomat)

if (opttype==1 && greektype==2) result<-exp(-q*timetomat)*pnorm(d1)


if (opttype==2 && greektype==2) result<-exp(-q*timetomat)*(pnorm(d1)-1)

if (greektype==3) result<-exp(-q*timetomat)*dnorm(d1)/(spot*sigma*sqrt(timetomat))

if (greektype==4) result<-exp(-q*timetomat)*spot*dnorm(d1)*sqrt(timetomat)

BlackScholesFormula<-result

}

BlackScholesImpVol  <- function (obsprice,spot,timetomat,strike,r, q=0, opttype=1)

  { difference<- function(sigBS, obsprice,spot,timetomat,strike,r,q,opttype)
      {BlackScholesFormula (spot,timetomat,strike,r,q,sigBS, opttype,1)-obsprice
     }

    uniroot(difference, c(10^-6,10),obsprice=obsprice,spot=spot,timetomat=timetomat,strike=strike,r=r,q=q,opttype=opttype)$root

  }

BS.Fourier <- function(spot,timetoexp,strike,r,divyield,sigma,greek=1){

  X<-log(spot/strike)+(r-divyield)*timetoexp
  
  integrand<-function(k){
    integrand<-Re(exp( (-1i*k+0.5)*X- 0.5*(k^2+0.25)*sigma^2*timetoexp)/(k^2+0.25))
  } 
  dummy<-integrate(integrand,lower=-Inf,upper=Inf)$value
  
  BS.Fourier<-exp(-divyield*timetoexp)*spot-strike*exp(-r*timetoexp)*dummy/(2*pi)
}

Heston.Fourier <- function(spot,timetoexp,strike,r,divyield,V,theta,kappa,epsilon,rho,greek=1){

X<-log(spot/strike)+(r-divyield)*timetoexp
kappahat<-kappa-0.5*rho*epsilon
xiDummy<-kappahat^2+0.25*epsilon^2

integrand<-function(k){
    xi<-sqrt(k^2*epsilon^2*(1-rho^2)+2i*k*epsilon*rho*kappahat+xiDummy)
    Psi.P<--(1i*k*rho*epsilon+kappahat)+xi
    Psi.M<-(1i*k*rho*epsilon+kappahat)+xi
    alpha<--kappa*theta*(Psi.P*timetoexp + 2*log((Psi.M + Psi.P*exp(-xi*timetoexp))/(2*xi)))/epsilon^2
    beta<--(1-exp(-xi*timetoexp))/(Psi.M + Psi.P*exp(-xi*timetoexp))
    numerator<-exp((-1i*k+0.5)*X+alpha+(k^2+0.25)*beta*V)

    if(greek==1) dummy<-Re(numerator/(k^2+0.25))
    if(greek==2) dummy<-Re((0.5-1i*k)*numerator/(spot*(k^2+0.25)))
    if(greek==3) dummy<--Re(numerator/spot^2)
    if(greek==4) dummy<-Re(numerator*beta)
    
    integrand<-dummy
  } 


  dummy<-integrate(integrand,lower=-100,upper=100)$value

  if (greek==1) dummy<-exp(-divyield*timetoexp)*spot-strike*exp(-r*timetoexp)*dummy/(2*pi)

  if(greek==2) dummy<-exp(-divyield*timetoexp)-strike*exp(-r*timetoexp)*dummy/(2*pi)

  if(greek==3) dummy<--strike*exp(-r*timetoexp)*dummy/(2*pi)
  
  if(greek==4) dummy<--strike*exp(-r*timetoexp)*dummy/(2*pi)

  Heston.Fourier<-dummy
}

Andreasen.Fourier <- function(spot,timetoexp,strike,Z,lambda,beta,epsilon){

X<-log(spot/strike)

integrand<-function(k){
    neweps<-lambda*epsilon
    xi<-sqrt(k^2*neweps^2+beta^2+0.25*neweps^2)
    Psi.P<--beta+xi
    Psi.M<-beta+xi
    A<--beta*(Psi.P*timetoexp + 2*log((Psi.M + Psi.P*exp(-xi*timetoexp))/(2*xi)))/(epsilon^2)
    B<-(1-exp(-xi*timetoexp))/(Psi.M + Psi.P*exp(-xi*timetoexp))
    integrand<-Re(exp( (-1i*k+0.5)*X+A-(k^2+0.25)*B*lambda^2*Z)/(k^2+0.25))
  } 

  dummy<-integrate(integrand,lower=-Inf,upper=Inf)$value

 Andreasen.Fourier<-spot-strike*dummy/(2*pi)
  
}


timetoexp<-1.0

S0<-100
R<-0.02
V0<-0.15^2
kappa<-2
theta<-0.2^2
sigma<-1.0
rho<--0.5

Params<-paste("S0=", S0, ", sqrt(V0)=",sqrt(V0),", r=",R, ", T=", timetoexp)
ModelParams<-paste("kappa =", kappa, ", theta =", theta, ", rho =", rho, ", sigma =", sigma)


Nsim<-10^4
NstepsPerYear<-1*252

Nsteps<-round(timetoexp*NstepsPerYear)
dt<-timetoexp/Nsteps

S<-rep(S0,Nsim); V<-rep(V0,Nsim)

AbsAtZero<-FALSE
if (AbsAtZero) title<-"Heston: Euler-S + |V| @0"
MaxAtZero<-FALSE
if (MaxAtZero) title<-"Heston: Euler-S + V^+ @0"
FullTruncation<-TRUE
# From page 6 in https://www.dropbox.com/s/nw7uzmf8k0imq0t/LeifHestonWP.pdf?dl=0
# "The scheme that appears to produce the smallest discretization bias
if (FullTruncation) title<-"Heston\n Euler-ln(S) + full truncation-V"
Andersen_1<-
if (Andersen_1) title<-"Heston: Euler-S + |V| @0"


ToFile<-FALSE; GraphFile="FullTruncation.png" 
IVspace<-FALSE

RunTime<-system.time(
  for (i in 1:Nsteps){
    dW1<-sqrt(dt)*rnorm(Nsim,0,1)
    dW2<-rho*dW1+sqrt(1-rho^2)*sqrt(dt)*rnorm(Nsim,0,1)
    if (AbsAtZero){ 
      S<-S+R*S*dt+sqrt(V)*S*dW1
      V<-abs(V + kappa*(theta-V)*dt + sigma*sqrt(V)*dW2)
    }
    if (MaxAtZero){ 
      S<-S+R*S*dt+sqrt(V)*S*dW1
      V<-pmax(V + kappa*(theta-V)*dt + sigma*sqrt(V)*dW2,0)
    }
    
    if (FullTruncation){
      X<-log(S)
      X<-X+(R-0.5*pmax(V,0))*dt+sqrt(pmax(V,0))*dW1
      S<-exp(X)
      V<-V+kappa*(theta-pmax(V,0))*dt+sigma*sqrt(pmax(V,0))*dW2
    }
    
    if(Andersen_1){
      S<-S+R*S*dt+sqrt(V)*S*dW1    
      alpha_h<-0.5
      gamma_1<-sqrt((1/dt)*log(1+((0.5*sigma*sigma)*(1/kappa)*V^(2*alpha_h)*(1-exp(-2*kappa*dt)))/((exp(-kappa*dt)*V+(1-exp(-kappa*dt))*theta)^2)))
      V<-(exp(-kappa*dt)*V+(1-exp(-kappa*dt))*theta)*exp(-0.5*(gamma_1*gamma_1)+gamma_1*dW2)
    }
    
  }  
)[3]

ExperimentParams<-paste("#steps/year =",NstepsPerYear, ", #paths =", Nsim, ", runtime (seconds) = ", round(RunTime,2))


StdErrSimCall<-TrueCall<-SimCall<-Strikes<-S0+(-10:10)*2
ConfBandIVSimCall<-IVSimCall<-IVTrueCall<-SimCall

if(ToFile) png(GraphFile,width=14,height=14,units='cm',res=300)

for (i in 1:length(SimCall)) {
  SimCall[i]<-exp(-R*timetoexp)*mean(pmax(S-Strikes[i],0))
  StdErrSimCall[i]<-sd(exp(-R*timetoexp)*(pmax(S-Strikes[i],0)))/sqrt(Nsim)
  if (IVspace) IVSimCall[i]<-BlackScholesImpVol(SimCall[i],S0,timetoexp,Strikes[i],R, q=0, opttype=1)
  if (IVspace) ConfBandIVSimCall[i]<-BlackScholesImpVol(SimCall[i]+1.96*StdErrSimCall[i],S0,timetoexp,Strikes[i],R, q=0, opttype=1)
  TrueCall[i]<-Heston.Fourier(S0,timetoexp,Strikes[i],R,0,V0,theta,kappa,sigma,rho,greek=1)
  if (IVspace) IVTrueCall[i]<-BlackScholesImpVol(TrueCall[i],S0,timetoexp,Strikes[i],R, q=0, opttype=1)
}

if(!IVspace){
  dummy<-c(min(SimCall,TrueCall),max(SimCall,TrueCall))
  plot(Strikes,SimCall,type='b',ylim=dummy,col='blue',ylab="Call price",main=title,xlab="Strike")
  points(Strikes,TrueCall,type='l')
  points(Strikes,SimCall+1.96*StdErrSimCall,type='l',lty=2,col='blue')
  points(Strikes,SimCall-1.96*StdErrSimCall,type='l',lty=2,col='blue')
  
  text(min(Strikes),min(dummy)+0.10*(max(dummy)-min(dummy)),"Black: Closef-form Heston ala Lipton",adj=0,cex=0.5)
  text(min(Strikes),min(dummy)+0.05*(max(dummy)-min(dummy)),"Blue: Sim' est' (o) + 95% conf' band' (---)", col="blue",adj=0,cex=0.5)
  
  text(max(Strikes),max(dummy),Params,adj=1, cex=0.5)
  text(max(Strikes),0.95*max(dummy),ModelParams,adj=1, cex=0.5)
  text(max(Strikes),0.90*max(dummy),ExperimentParams,adj=1, cex=0.5)
  text(max(Strikes),0.85*max(dummy),".../Dropbox/FinKont2/LiveAtLectures_HestonSimulation.R",adj=1, cex=0.5)
}

if(IVspace){
  dummy<-c(min(2*IVSimCall-ConfBandIVSimCall,IVTrueCall),max(IVTrueCall,ConfBandIVSimCall) )
  plot(Strikes,IVSimCall,type='b',col='blue',ylim=dummy, ylab="Implied volatility",main=title,xlab="Strike")
  points(Strikes,IVTrueCall,type='l')
  points(Strikes,ConfBandIVSimCall,type='l',lty=2,col='blue')
  points(Strikes,2*IVSimCall-ConfBandIVSimCall,type='l',lty=2,col='blue')
  
  text(min(Strikes),min(dummy)+0.10*(max(dummy)-min(dummy)),"Black: Closef-form Heston ala Lipton",adj=0,cex=0.5)
  text(min(Strikes),min(dummy)+0.05*(max(dummy)-min(dummy)),"Blue: Sim' est' (o) + 95% conf' band' (---)", col="blue",adj=0,cex=0.5)
  
  text(max(Strikes),max(dummy),Params,adj=1, cex=0.5)
  text(max(Strikes),0.95*max(dummy),ModelParams,adj=1, cex=0.5)
  text(max(Strikes),0.90*max(dummy),ExperimentParams,adj=1, cex=0.5)
  text(max(Strikes),0.85*max(dummy),".../Dropbox/FinKont2/LiveAtLectures_HestonSimulation.R",adj=1, cex=0.5)
}

if (ToFile)
  
# Control Variates
  
  
   S_vc<-rep(S0,Nsim)
  
for (i in 1:Nsteps){
    dW1<-sqrt(dt)*rnorm(Nsim,0,1)
    dW2<-rho*dW1+sqrt(1-rho^2)*sqrt(dt)*rnorm(Nsim,0,1)
    S_vc<-S_vc+R*S_vc*dt+sqrt(V0)*S_vc*dW1}


print(S_vc)

#Consider strike


strike_v0<-75

#equation 6)P_A_CV=P_A_cap+(P_G-P_G_cap)

#Parameters
#Estimating P_A_cap
P_A_cap_vec<-rep(0,length(S))
for (i in 1:length(S)){P_A_cap_vec[i]<-(max((S[i]-strike_v0),0))}
print(P_A_cap_vec) 
P_A_cap<-mean(P_A_cap_vec)
print(P_A_cap)

#Estimating P_G
P_G<-BlackScholesFormula(S0,timetoexp,strike_v0,R,0,V0,1,1)
print(P_G)
#Estimating P_G_cap

P_G_cap_vec<-rep(0,length(S_vc))
for(i in 1:length(S_vc)){
P_G_cap_vec<-(max(S_vc[i]-strike_v0,0))}
P_G_cap<-mean(P_G_cap_vec)
print(P_G_cap)

#Equation 6) Control Variate
P_A_CV<-P_A_cap+(P_G-P_G_cap)

#Estiamtor of the Heston model Option Pricing
print(P_A_CV)

#equation 7)P_A_CV=P_A_cap+Beta*(P_G-P_G_cap), using the parameter beta to minimize variance

#Generate random S and S_vc paths and compute P_A_cap and P_G_cap for each path.

set.seed(190)

P_A_cap_B<-replicate(10,{for (i in 1:Nsteps){
X<-log(S)
X<-X+(R-0.5*pmax(V,0))*dt+sqrt(pmax(V,0))*dW1
S<-exp(X)
V<-V+kappa*(theta-pmax(V,0))*dt+sigma*sqrt(pmax(V,0))*dW2}

})
print(P_A_cap_B)


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  • $\begingroup$ Where are dW1 and dW2 coming from? $\endgroup$ – Bob Jansen Mar 21 at 19:18
  • $\begingroup$ @BobJansen Jansen dW1<-sqrt(dt)*rnorm(Nsim,0,1) dW2<-rho*dW1+sqrt(1-rho^2)*sqrt(dt)*rnorm(Nsim,0,1) $\endgroup$ – Pedro Gomes Mar 21 at 19:32
  • $\begingroup$ Please make your code reproducible. It’s hard to help if important details are missing. $\endgroup$ – Bob Jansen Mar 21 at 19:43
  • $\begingroup$ @BobJansen I have just uploaded the full code in my answer. $\endgroup$ – Pedro Gomes Mar 21 at 19:48
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The replicate function works best when you fully define your discretization scheme within a function. Then you can simply replicate the function-call x amount of times. Also, try and keep code duplication to a minimum and improve your general syntax. This will help you and your peers that might need to review and/or change your code in the future.

Nevertheless, one way to solve your problem is to define the discretization scheme for the Heston model within a function:

HestonSimTruncation <- function(S0, V0, kappa, theta, rho, epsilon, r, Nsteps, T){

  V <- numeric() 

  S <- numeric()

  dt <- T/Nsteps 

  dw1 <- sqrt(dt) * rnorm(Nsteps) #Generation of Brownian increments for dw1

  dw2 <- rho*dw1+sqrt((1-rho^2)*dt)*rnorm(Nsteps) #Generation of Brownian increments for dw2

  S[1] <- log(S0) 

  V[1] <- V0 

  for(i in 1:Nsteps){

    V[i+1] <- V[i] + kappa*(theta-V[i])*dt + epsilon*sqrt(V[i]) * dw1[i]

    if( V[i+1] < 0 ){ V[i+1] <- 0 } #Absorption barrier, similar to full truncation. 

    S[i+1] = S[i] + (r-0.5 * V[i]) * dt + sqrt(V[i]) * dw2[i]

  } 

  return(exp(S))

}

Then you can input your parameters and simply do:

test2 <- replicate(10, HestonSimTruncation(S0, V0, kappa, theta, rho, epsilon, r, Nsteps, T))

Which will give you a matrix with a dimension of $\mathbb{R}^{Nsteps \times Npaths}$, where $Npaths = 10$. Below, I have provided a graphical illustration of the first 5 simulations using the above function, hestonsims

where I have used the parameters $S_0 = 100$, $V_0 = 0.2^2$, $\kappa = 3.8$, $\theta = 0.3095$, $\rho = -0.78$, $\epsilon = 0.9288$, $Nsteps = 1000$, $T=1$ (I found these parameters on the internet). I hope this help.

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