[R-SIG-Finance] Monte Carlo Option Pricing formula R code vs Matlab
Enrico Schumann
enricoschumann at yahoo.de
Fri Feb 3 09:08:46 CET 2012
Hi Darko,
R's 'max' function behaves differently from MATLAB's. You probably don't
want
payoff = max(delS - exercisePrice, 0);
but
payoff = pmax(delS - exercisePrice, 0)
(and you should settle on one number of time steps per year; either 255
or 252).
If you only work with European options and use the
Black--Scholes--Merton SDE for the underlier, then there is no need to
simulate the paths of the SDEs; you can do one time step and "jump from
0 to T".
And as rex has pointed out, there would indeed be a substantial speedup
if you rewrote your code in a vectorised fashion. (This is also true for
MATLAB, though the speedup is typically much smaller than in R.)
Regards,
Enrico
Am 03.02.2012 00:36, schrieb Roupell, Darko:
> Thanks Enrico,
>
> To test if the code structure is correct I googled for alternative samples of Monte Carlo Option pricing coded in Matlab.
>
> What I find the most puzzling is that even if I re-code using sample from matlab the results obtained in R are very different to those obtained by matlab, despite using the same parameters apart of Rnorm(). As I am at loss I am hoping that you or someone else in R-SIG may spot the difference that explains it.
>
> ########################################## R code ###########################################################
>
> exercisePrice = 100;
> timeToExpiry = 5; #% in years
> underlyingPrice = 100; #% underlying in cents
> expectedVol = 0.2; #% expected volatility
> expectedDiv = 0; # expected dividend
> riskFreeRate = 0.06; #% interest rate
> itr = 10000 #% number of iterations
> delS = 0*array(0,itr)
>
> dt = 1/252
> nudt = (riskFreeRate - expectedDiv - 0.5 * expectedVol^ 2)* dt
> sigsdt = expectedVol * sqrt(dt)
> itr = 10000
> delS = array(0,itr)
> drifts = 255 * timeToExpiry
>
> for( i in (1:itr))
> {
> dS = underlyingPrice
>
> for (j in (1:drifts))
> {
> eps = rnorm(1, mean = 0, sd = 1)
> dS = dS * exp(nudt +sigsdt*eps)
> }
> delS[i] =dS
> }
>
> payoff = max(delS - exercisePrice, 0);
> cal=mean(payoff) * exp(-riskFreeRate*timeToExpiry)
>
> results:
>> cal
> [1] 396.2675
>
> ################################################## MATLAB ###########################################
>
> S0=100; K=100; r=0.06; sig=0.2; T=5; div=0;
>
> dt = 1/252;
> nudt = (r - div - 0.5 * sig ^ 2) * dt;
> sigsdt = sig * sqrt(dt)
> sim=10000;
> Si=zeros(1,sim);
>
> drifts=255*T
> for i=1:sim
> S=S0;
> for j=1:drifts;
> z=randn(1,1);
>
> S = S* exp(nudt + sigsdt * z);
> end
> Si(i)=S;
> end
>
> payoff = max(Si - K, 0);
> cal=mean(payoff) * exp(-r* T)
>
> calbs=blsprice(S0,K,r,T,sig,div)<---B&S
>
> results:
>
> cal = 32.0173
>
>
> calbs = 31.6150
>
> http://www.quantnet.com/forum/threads/accuracy-of-monte-carlo-simulation-for-option-pricing.2224/
>
> __________________________________________________
> Commonwealth Bank
> Darko Roupell
> Associate Quantitative Analyst
> Institutional Banking& Markets
> Equities Research
> Darling Park Tower 1
> Level 23, 201 Sussex Street
> Sydney, NSW 2000
> P: +61 2 9117 1254
> F: +61 2 9118 1000
> M: +61 400 170 515
> E: Darko.Roupell at cba.com.au
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>
> -----Original Message-----
> From: Enrico Schumann [mailto:enricoschumann at yahoo.de]
> Sent: Thursday, 2 February 2012 8:45 PM
> To: Roupell, Darko
> Cc: r-sig-finance at r-project.org
> Subject: Re: [R-SIG-Finance] Monte Carlo Option Pricing formula
>
>
> Hi, Darko,
>
> Am 02.02.2012 07:44, schrieb Roupell, Darko:
>> Hi All,
>>
>> I am trying to cross check option implied employee option price that was derived using Monte Carlo simulation. Below is code and parameter used and after accounting for dividend yield ( 1.46%) the derived option price is 206.8843 using the code snippet provided. Approx 1 cent below 207.95 that is listed in company prospect using their Monte Carlo simulation and below parameters.
>>
>> As we all know number of iteration can also slightly impact the average price, but am I rightly concerned that my methodology may not be correct?
>
> Hm, I have not really looked at your programme so I cannot comment
> whether it is correct. But we are talking about a difference of about
> half a percentage point here. Which is not much. I just ran you script
> 20 times.
>
> > summary(results)
> Min. 1st Qu. Median Mean 3rd Qu. Max.
> 205.6 206.0 206.3 206.3 206.8 206.9
>
> Admittedly, all results are all below the company's price, but
> nevertheless: they vary.
>
> There are details that they might have done differently. For instance,
> you do not compound (if I see correctly). What if you replaced
>
> riskFreeRate*timeToExpiry
>
> with
>
> (1+riskFreeRate)*timeToExpiry-1
>
> But even if that gives you the price: from a practical point of view,
> the difference is small, really.
>
> (Much better would be to check what would happen if the div did not turn
> out as expected, if the vol were different, etc)
>
> Regards,
> Enrico
>
>
>>
>> Any feedback will be appreciated.
>>
>>
>> exercisePrice = 0;
>> timeToExpiry = 3; #% in years
>> underlyingPrice = 490; #% underlying in cents
>> expectedVol = 0.5; #% expected volatility
>> expectedDiv = 0.0146; #% expected dividend in cents
>> riskFreeRate = 0.0425; #% interest rate
>> itr = 500000 #% number of iterations
>> delS = 0*array(0,itr)
>>
>> for( i in (1:itr))
>> {
>> eps = rnorm(1) #% random number generator
>> dS = expectedDiv*underlyingPrice+underlyingPrice*(riskFreeRate*timeToExpiry) + (underlyingPrice*expectedVol*eps*sqrt(timeToExpiry))
>> mv = dS - exercisePrice;
>>
>> delS[i] = max(mv,0);
>> }
>>
>> mean(delS)
>>
>> __________________________________________________
>> Commonwealth Bank
>> Darko Roupell
>> Associate Quantitative Analyst
>> Institutional Banking& Markets
>> Equities Research
>> Darling Park Tower 1
>> Level 23, 201 Sussex Street
>> Sydney, NSW 2000
>> P: +61 2 9117 1254
>> F: +61 2 9118 1000
>> M: +61 400 170 515
>> E: Darko.Roupell at cba.com.au
>
> [...]
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>
--
Enrico Schumann
Lucerne, Switzerland
http://nmof.net/
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