# Option valuation for arbitrary distribution using monte carlo simulation

19 messages
Open this post in threaded view
|

## Option valuation for arbitrary distribution using monte carlo simulation

 Dear list, (forgive me if this is a question already answered a hundred times here - I was unable to find the threads) I want to derive option prices (let's say european style for a start) for a given sample of historical return data (let's say the last 50 DAX returns) using monte carlo simulation. It is clear that the return data has to be transformed into "risk neutral" data first. I thought I could find a procedure for this task (data transformation and simulation) in R because it should be a standard approach for assessing market option prices. But I could not find anything. Can somebody help? Joachim _______________________________________________ [hidden email] mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-finance-- Subscriber-posting only. If you want to post, subscribe first. -- Also note that this is not the r-help list where general R questions should go.
Open this post in threaded view
|

## Re: Option valuation for arbitrary distribution using monte carlo simulation

 Hi Joachim , I'm not a prof but I recommend you the following book: Option Pricing and Estimation of Financial Models with R Stafano M. Iacus http://www.amazon.com/Option-Pricing-Estimation-Financial-Models/dp/0470745843/ref=sr_1_48?ie=UTF8&qid=1321974343&sr=8-48it'll explain a lot of thinks with code samples. Some very base simple code, I started with this one and while was reading the book I started to improve it #define functions require(package='fBasics',quietly=FALSE) #This function to create innovation #We keep innovation function autside of process because #once computed leave it stored for next usage innovation.norm<-function(pTrials=5000){   # remember to add the seed   #generate standard normal innovation   z <- rnorm(n=pTrials,mean=0,sd=1)   #genereta anthitetic innovation   ant.z <--z   #combine in the new one:   z <- c(z,ant.z)   #return innovation vector   return(z) } #this function to create the price distribution st.proc.norm<-function(pInnVect=z,mu=0,pSnow=15,pVol=0.15,pTradDaysToExp=20){   #set the number of trading days in one year   yearTradDays<-252   #compute the interval time   dt<-pTradDaysToExp/yearTradDays   s.final <- rep(0,length(pInnVect))   s.final<-pSnow*exp(pVol*sqrt(dt)*pInnVect)   out<-c(s.final,pSnow)   return(s.final) } #this function to build the option chain price.chain<-function(pSFinal,pIntRate=0.02,pStrikeMin=10,pStrikeMax=15,pDStrike=0.5,pCalDaysToExp=30){   #build strike chain   K<-seq(from=pStrikeMin,to=pStrikeMax,by=pDStrike)   #compute dicount factor   calDays<-365   dt<-pCalDaysToExp/calDays   #prepare storage for chain   prCall<-rep(x=0,length(K))   prPut<-rep(x=0,length(K))   se.call<-rep(x=0,length(K))   se.put<-rep(x=0,length(K))     for (i in (1:length(K))){     #print(i)     #print(K[i])     #formula for pricing     c<- ifelse(pSFinal>K[i],exp(-pIntRate*dt)*(pSFinal-K[i]), 0)     p<- ifelse(pSFinal
Open this post in threaded view
|

## Re: Option valuation for arbitrary distribution using monte carlo simulation

 Hi:     I think this could work.       Now, I always find it useful to identify the hard parts up front.  Let's see if I can sketch this in a couple minutes. 1)  You need to estimate variance (vol^2) from the historical prices.  Now 50 days closing prices is a little low and when close to expiration you'd want some intraday data too.  But the model is a little crude anyhow, so perhaps it doesn't matter.  So assume the stock price St follows a log-normal process:  dlog(St) = (r - vol^2/2) dt + vol dBt and take first differences of log(St) for the returns process Rt ~ log(St/S(t-1)).  Rt is normally distributed so vol^2 ~ Sum{(Rt - R(t-1))^2}/48 - (1/49 Sum{Rt})^2}.   2)  You need the risk-free rate, so set r=treasusry-rate for the same maturity as the stock option time-to-expiration -- this is in calendar days. 3)  Let t = today and T=expiration.     The sample paths of the stock price can be assumed to follow: Ss -> exp{r s + vol N(0, 1) sqrt(s)}  // N(0,1) =normal distribution.                    Let c_(ns) = exp{-r (T-t)} max{ exp{r (T-t) + vol ns sqrt(T-t)} - X, 0} -- where ns is a random sample from N(0,1).                  european call(X,S,r,vol,T) = { c_(ns1) + c_(ns2) + ... + c_(ns(num_samples)) } / num_samples.     My math could be off a little (e.g. maybe a ito factor), but at least for me, it really helps to get some brief clean calculations up front, and then code.;-) ________________________________________ From: [hidden email] [[hidden email]] on behalf of msalese [[hidden email]] Sent: Tuesday, November 22, 2011 7:30 AM To: [hidden email] Subject: Re: [R-SIG-Finance] Option valuation for arbitrary distribution using monte carlo simulation Hi Joachim , I'm not a prof but I recommend you the following book: Option Pricing and Estimation of Financial Models with R Stafano M. Iacus http://www.amazon.com/Option-Pricing-Estimation-Financial-Models/dp/0470745843/ref=sr_1_48?ie=UTF8&qid=1321974343&sr=8-48it'll explain a lot of thinks with code samples. Some very base simple code, I started with this one and while was reading the book I started to improve it #define functions require(package='fBasics',quietly=FALSE) #This function to create innovation #We keep innovation function autside of process because #once computed leave it stored for next usage innovation.norm<-function(pTrials=5000){   # remember to add the seed   #generate standard normal innovation   z <- rnorm(n=pTrials,mean=0,sd=1)   #genereta anthitetic innovation   ant.z <--z   #combine in the new one:   z <- c(z,ant.z)   #return innovation vector   return(z) } #this function to create the price distribution st.proc.norm<-function(pInnVect=z,mu=0,pSnow=15,pVol=0.15,pTradDaysToExp=20){   #set the number of trading days in one year   yearTradDays<-252   #compute the interval time   dt<-pTradDaysToExp/yearTradDays   s.final <- rep(0,length(pInnVect))   s.final<-pSnow*exp(pVol*sqrt(dt)*pInnVect)   out<-c(s.final,pSnow)   return(s.final) } #this function to build the option chain price.chain<-function(pSFinal,pIntRate=0.02,pStrikeMin=10,pStrikeMax=15,pDStrike=0.5,pCalDaysToExp=30){   #build strike chain   K<-seq(from=pStrikeMin,to=pStrikeMax,by=pDStrike)   #compute dicount factor   calDays<-365   dt<-pCalDaysToExp/calDays   #prepare storage for chain   prCall<-rep(x=0,length(K))   prPut<-rep(x=0,length(K))   se.call<-rep(x=0,length(K))   se.put<-rep(x=0,length(K))   for (i in (1:length(K))){     #print(i)     #print(K[i])     #formula for pricing     c<- ifelse(pSFinal>K[i],exp(-pIntRate*dt)*(pSFinal-K[i]), 0)     p<- ifelse(pSFinal
Open this post in threaded view
|

## Re: Option valuation for arbitrary distribution using monte carlo simulation

 Dear Massimo, thanks for the hint to Iacus' book. I was aware of this title already but could not get hold of it by now. Thank you for the code snippet as well. Unfortunately, it does not seem to help with my main problem: How to correct the original data such that Monte Carlo sampling from it gives me samples from a risk-neutral but NONNORMAL process. Maybe my description is a bit awkward. Also I may lack the math and statistics skills. I try do say it another way: Let r : original log return series      ( with e.g. mean(r)  = -0.0001574849 and sd(r)  = 0.01479017 ) x:  risk-free rate of, say, 5%. Snow : stock price now time to maturity = 1 year = 252 trading days St : stock price in 1 year X: call option strike E[] : expected value C : the option price I am looking for a corrected return series rcorr = r + z such that E[ Snow * exp(  sum(  sample(rcorr, 252)     ) ) ] = 100 * exp(0.05) because then I can find the call option price with for (i in 1:bignumber) {    St[i] = Snow * exp(  sum(   sample(rcorr, 252)     ) )    op[i] = max(0, exp(-.05) * (St[i] - X)  ) } C = mean(op) I could of course use some search algorithm to find z. In fact thats what I did. But I thought this is so common a problem for finance folks that there must be some elegant R procedure for th task. Your code seems to assume that we are dealing with normal log returns but this is not the case for my sample. Do you see what I mean? Is this stuff dealt with in the Iacus book? Greetings Joachim > > Hi Joachim , I'm not a prof but I recommend you the following book: > Option Pricing and Estimation of Financial Models with R > Stafano M. Iacus > http://www.amazon.com/Option-Pricing-Estimation-Financial-Models/dp/0470745843/ref=sr_1_48?ie=UTF8&qid=1321974343&sr=8-48> > it'll explain a lot of thinks with code samples. > Some very base simple code, I started with this one and while was reading > the book I started to improve it > > #define functions > require(package='fBasics',quietly=FALSE) > > #This function to create innovation > #We keep innovation function autside of process because > #once computed leave it stored for next usage > innovation.norm<-function(pTrials=5000){ >    # remember to add the seed >    #generate standard normal innovation >    z<- rnorm(n=pTrials,mean=0,sd=1) >    #genereta anthitetic innovation >    ant.z<--z >    #combine in the new one: >    z<- c(z,ant.z) >    #return innovation vector >    return(z) > } > > #this function to create the price distribution > st.proc.norm<-function(pInnVect=z,mu=0,pSnow=15,pVol=0.15,pTradDaysToExp=20){ >    #set the number of trading days in one year >    yearTradDays<-252 >    #compute the interval time >    dt<-pTradDaysToExp/yearTradDays >    s.final<- rep(0,length(pInnVect)) >    s.final<-pSnow*exp(pVol*sqrt(dt)*pInnVect) >    out<-c(s.final,pSnow) >    return(s.final) > } > > #this function to build the option chain > price.chain<-function(pSFinal,pIntRate=0.02,pStrikeMin=10,pStrikeMax=15,pDStrike=0.5,pCalDaysToExp=30){ >    #build strike chain >    K<-seq(from=pStrikeMin,to=pStrikeMax,by=pDStrike) >    #compute dicount factor >    calDays<-365 >    dt<-pCalDaysToExp/calDays >    #prepare storage for chain >    prCall<-rep(x=0,length(K)) >    prPut<-rep(x=0,length(K)) >    se.call<-rep(x=0,length(K)) >    se.put<-rep(x=0,length(K)) > >    for (i in (1:length(K))){ >      #print(i) >      #print(K[i]) >      #formula for pricing >      c<- ifelse(pSFinal>K[i],exp(-pIntRate*dt)*(pSFinal-K[i]), 0) >      p<- ifelse(pSFinal      #price as average >      prCall[i]<-mean(c) >      prPut[i]<-mean(p) >      #standard error >      se.call[i]<-sd(c)/sqrt(length(pSFinal)) >      se.put[i]<-sd(p)/sqrt(length(pSFinal)) > >      ## make a confidence interval estimate >      alpha<-0.05 >      t.star<-qt(p=(1-alpha/2),df=length(pSFinal)-1,lower.tail=TRUE) >      prCall[i]+c(-1,1)*t.star*se.call[i] >      prPut[i]+c(-1,1)*t.star*se.put[i] >    } > >    #build the data frame for option's chain > > pr.chain<-data.frame(CALL=round(prCall,digits=3),Strike=K,PUT=round(prPut,digits=3)) > >    return(pr.chain) > } > > > ################################################################################## > ##  PRICING WITH NORMAL > inn.n<-innovation.norm(pTrials=500) > hist(inn.n) > #you must estimate volatility from past data > s.final.n<-st.proc.norm(pInnVect=inn.n,pSnow=13.40,pVol=0.25,pTradDaysToExp=20) > hist(s.final.n) > #you must estimate interest rate > price.chain(pSFinal=s.final.n,pIntRate=0.02,pStrikeMin=10,pStrikeMax=15,pDStrike=0.5,pCalDaysToExp=20) _______________________________________________ [hidden email] mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-finance-- Subscriber-posting only. If you want to post, subscribe first. -- Also note that this is not the r-help list where general R questions should go.
Open this post in threaded view
|

## Re: Option valuation for arbitrary distribution using monte carlo simulation

 You can tray to use non normal innovations (sampled from stable distribution), but you have to estimate alpha, beta, gamma and delta parameters innovation.stable<-function(pTrials=5000,pAlpha=1.7,pBeta=0.5,pGamma=1,pDelta=1,pPm=0){   #you must estimate parameters from past data using stableFit   #alpha -> index tail (0,2]   #beta -> skewness [-1,1]   #gamma -> scale   #delta -> shift   #pm -< parametrizzation (0,1,2)   require(package='fBasics',quietly=FALSE)   z <- rstable(n=pTrials,alpha=pAlpha,beta=pBeta,gamma=pGamma,delta=pDelta,pm=pPm)   #genereta anthitetic innovation   ant.z <--z   #combine in the new one:   z <- c(z,ant.z)   #return innovation vector   return(z) } ##############################Ã  Iacus' Book starts teaching you to price with normal innovations and then improves the code and teachs you how to parallelize all.
Open this post in threaded view
|

## Re: Option valuation for arbitrary distribution using monte carlo simulation

 Probably my question is quite trivial, however could somebody clarify me why I need to have some stable distribution instead having any arbitrary distribution? I know what the stable distribution is however could not get the reason in the asset price generation context. Thanks andregards,
Open this post in threaded view
|

## Re: Option valuation for arbitrary distribution using monte carlo simulation

 I appreciate msalese's help, but: good question! Am 24.11.2011 12:59, schrieb Bogaso: > Probably my question is quite trivial, however could somebody clarify me why > I need to have some stable distribution instead having any arbitrary > distribution? I know what the stable distribution is however could not get > the reason in the asset price generation context. > > Thanks andregards, > _______________________________________________ [hidden email] mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-finance-- Subscriber-posting only. If you want to post, subscribe first. -- Also note that this is not the r-help list where general R questions should go.
Open this post in threaded view
|

## Re: Option valuation for arbitrary distribution using monte carlo simulation

 Hi guys, I think that you can use what distribution you want. Stable is one of that better fits the log returns (it's my opinion!) But you can have more info giving a look at http://www.mathestate.com/tools/Financial/map/Overview.html). If you prefer you can price with GARCH to better reproduce the smile effect. I'm a buyer side trader (risk taker), I price options only to create future scenarios and on that scenarios I plan the reaction.
Open this post in threaded view
|

## Re: Option valuation for arbitrary distribution using monte carlo simulation

 Thank you for the interesting link. I agree that you can use a (fit of a) stable distribution for sampling purposes. But please: Why not simply use the raw data and sample from that? What could be a better starting point? Again, it is clear that you cannot use the raw return series as it comes off your data feed; there is a need for adjusting. But there cannot be a better fit to the raw data than the raw data itself... Am 24.11.2011 14:04, schrieb msalese: > Hi guys, I think that you can use what distribution you want. > Stable is one of that better fits the log returns (it's my opinion!) > But you can have more info giving a look at > http://www.mathestate.com/tools/Financial/map/Overview.html). > If you prefer you can price with GARCH to better reproduce the smile effect. > I'm a buyer side trader (risk taker), I price options only to create future > scenarios and on that scenarios I plan the reaction. > _______________________________________________ [hidden email] mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-finance-- Subscriber-posting only. If you want to post, subscribe first. -- Also note that this is not the r-help list where general R questions should go.
Open this post in threaded view
|

## Re: Option valuation for arbitrary distribution using monte carlo simulation

 In reply to this post by msalese The stable distribution is among the earliest distributions that have been proposed for describing the returns of financial assets.  It was first suggested by Benoit Mandelbrot [1], the same person after whom the Mandelbrot set is named.  It's probably fair to say that Mandelbrot's work launched the study of asset price distributions.  Since then, there have been dozens of distributions proposed that have been argued to have better properties.  From a modeling perspective, one of the disadvantages of the stable distribution is that the variance is undefined (infinite) if alpha < 2, so it is not clear what it would mean to talk about the volatility of a stable-distributed process. [1] Mandelbrot, Benoit, "The Variation of Certain Speculative Prices," The Journal of Business, Vol. 36, No. 4 (Oct. 1963), pp. 394-419. On Thu, Nov 24, 2011 at 8:04 AM, msalese <[hidden email]> wrote: > Hi guys, I think that you can use what distribution you want. > Stable is one of that better fits the log returns (it's my opinion!) > But you can have more info giving a look at > http://www.mathestate.com/tools/Financial/map/Overview.html). > If you prefer you can price with GARCH to better reproduce the smile > effect. > I'm a buyer side trader (risk taker), I price options only to create future > scenarios and on that scenarios I plan the reaction. > > > -- > View this message in context: > http://r.789695.n4.nabble.com/Option-valuation-for-arbitrary-distribution-using-monte-carlo-simulation-tp4095718p4103714.html> Sent from the Rmetrics mailing list archive at Nabble.com. > > _______________________________________________ > [hidden email] mailing list > https://stat.ethz.ch/mailman/listinfo/r-sig-finance> -- Subscriber-posting only. If you want to post, subscribe first. > -- Also note that this is not the r-help list where general R questions > should go. > -- Matthew Clegg [hidden email]         [[alternative HTML version deleted]] _______________________________________________ [hidden email] mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-finance-- Subscriber-posting only. If you want to post, subscribe first. -- Also note that this is not the r-help list where general R questions should go.
Open this post in threaded view
|

## Re: Option valuation for arbitrary distribution using monte carlo simulation

 I think it *is* clear what the volatility of a stable distribution (other than Gaussian) is: infinite. That implies, I believe, that the price of most options would also be infinite. I'm not so convinced that doing a Monte Carlo for options pricing with a stable distribution is a good idea. Caveat: I'm basically ignorant about options pricing. Pat On 24/11/2011 15:36, Matthew Clegg wrote: > The stable distribution is among the earliest distributions that have been > proposed for describing the returns of financial assets.  It was first > suggested by Benoit Mandelbrot [1], the same person after whom the > Mandelbrot set is named.  It's probably fair to say that Mandelbrot's work > launched the study of asset price distributions.  Since then, there have > been dozens of distributions proposed that have been argued to have better > properties.  From a modeling perspective, one of the disadvantages of the > stable distribution is that the variance is undefined (infinite) if alpha< > 2, so it is not clear what it would mean to talk about the volatility of a > stable-distributed process. > > [1] Mandelbrot, Benoit, "The Variation of Certain Speculative Prices," The > Journal of Business, Vol. 36, No. 4 (Oct. 1963), pp. 394-419. > > > On Thu, Nov 24, 2011 at 8:04 AM, msalese<[hidden email]>  wrote: > >> Hi guys, I think that you can use what distribution you want. >> Stable is one of that better fits the log returns (it's my opinion!) >> But you can have more info giving a look at >> http://www.mathestate.com/tools/Financial/map/Overview.html). >> If you prefer you can price with GARCH to better reproduce the smile >> effect. >> I'm a buyer side trader (risk taker), I price options only to create future >> scenarios and on that scenarios I plan the reaction. >> >> >> -- >> View this message in context: >> http://r.789695.n4.nabble.com/Option-valuation-for-arbitrary-distribution-using-monte-carlo-simulation-tp4095718p4103714.html>> Sent from the Rmetrics mailing list archive at Nabble.com. >> >> _______________________________________________ >> [hidden email] mailing list >> https://stat.ethz.ch/mailman/listinfo/r-sig-finance>> -- Subscriber-posting only. If you want to post, subscribe first. >> -- Also note that this is not the r-help list where general R questions >> should go. >> > > > -- Patrick Burns [hidden email] http://www.burns-stat.comhttp://www.portfolioprobe.com/blogtwitter: @portfolioprobe _______________________________________________ [hidden email] mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-finance-- Subscriber-posting only. If you want to post, subscribe first. -- Also note that this is not the r-help list where general R questions should go.
Open this post in threaded view
|

## Re: Option valuation for arbitrary distribution using monte carlo simulation

 In reply to this post by Joachim Breit On Thu, 2011-11-24 at 16:19 +0100, Joachim Breit wrote: > Thank you for the interesting link. > > I agree that you can use a (fit of a) stable distribution for sampling > purposes. But please: Why not simply use the raw data and sample from > that? What could be a better starting point? Again, it is clear that > you cannot use the raw return series as it comes off your data feed; > there is a need for adjusting. But there cannot be a better fit to the > raw data than the raw data itself... Of course you start with sampling from your data, but if all you do is sample from the data, with no 'noise', then all you are doing is smoothing out the prior observations.  This may lead you to a false sense of security, and give you an incorrect idea of how likely a fat tailed event is.  So by adding noise from a stable or other fat tailed distribution (skewed Student's T is also popular), you are believing that your prior data is both stationary and fully representative. Regards,   - Brian -- Brian G. Peterson http://braverock.com/brian/Ph: 773-459-4973 IM: bgpbraverock _______________________________________________ [hidden email] mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-finance-- Subscriber-posting only. If you want to post, subscribe first. -- Also note that this is not the r-help list where general R questions should go.
Open this post in threaded view
|

## Re: Option valuation for arbitrary distribution using monte carlo simulation

 Luenberger discusses the task of creating risk-neutral probabilities from any set of observed returns. I cannot find the specific reference in his book (Investment Science) but there is a discussion paper by Winston (google) that applies his method using Excel and @Risk to a sample of Microsoft returns. I think it would be relatively simple to program it in R. Charles Ward On 24 November 2011 19:43, Brian G. Peterson <[hidden email]> wrote: > On Thu, 2011-11-24 at 16:19 +0100, Joachim Breit wrote: > > Thank you for the interesting link. > > > > I agree that you can use a (fit of a) stable distribution for sampling > > purposes. But please: Why not simply use the raw data and sample from > > that? What could be a better starting point? Again, it is clear that > > you cannot use the raw return series as it comes off your data feed; > > there is a need for adjusting. But there cannot be a better fit to the > > raw data than the raw data itself... > > Of course you start with sampling from your data, but if all you do is > sample from the data, with no 'noise', then all you are doing is > smoothing out the prior observations.  This may lead you to a false > sense of security, and give you an incorrect idea of how likely a fat > tailed event is.  So by adding noise from a stable or other fat tailed > distribution (skewed Student's T is also popular), you are believing > that your prior data is both stationary and fully representative. > > Regards, > >  - Brian > > -- > Brian G. Peterson > http://braverock.com/brian/> Ph: 773-459-4973 > IM: bgpbraverock > > _______________________________________________ > [hidden email] mailing list > https://stat.ethz.ch/mailman/listinfo/r-sig-finance> -- Subscriber-posting only. If you want to post, subscribe first. > -- Also note that this is not the r-help list where general R questions > should go. >         [[alternative HTML version deleted]] _______________________________________________ [hidden email] mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-finance-- Subscriber-posting only. If you want to post, subscribe first. -- Also note that this is not the r-help list where general R questions should go.
Open this post in threaded view
|

## Re: Option valuation for arbitrary distribution using monte carlo simulation

 In reply to this post by Patrick Burns-2 Hi again guys, Let me say that (it's what I see looking at data) if you sample at 1 day or less, log return of financial instruments are not normal so I think you should use the distribution that best fit this situation. May be stable it's not the best so you can decide to use some normal mixture to fit fat tail behavior. The point is: how do you look at option ? Are you an hedger or a risk taker (retail trader) ? It's my experience that as risk taker it's better to have some pdf that let you build very extreme scenarios and then plan the option strategy to protect you from this unlikely situation but not impossible. The interesting think is that R give you the possibility to use different pdf so write the code to price with MC in a such way to separate the innovation function from pricing option-chain function, than change the innovations pdf and look what happen, it's the best school : experiment yourself.
Open this post in threaded view
|

## Re: Option valuation for arbitrary distribution using monte carlo simulation

 This post has NOT been accepted by the mailing list yet. In reply to this post by klswacha Hi Klswacha, could you be little bit more specific on Winston's work? I tried with Google but could not get anything specific. Thanks,
Open this post in threaded view
|

## Re: Option valuation for arbitrary distribution using monte carlo simulation

 In reply to this post by klswacha This is the paper: http://www.realoptions.org/papers1999/WINSTONOptions.PDFAm 25.11.2011 09:48, schrieb Charles Ward: > Luenberger discusses the task of creating risk-neutral probabilities > from any set of observed returns. I cannot find the specific reference > in his book (Investment Science) but there is a discussion paper by > Winston (google) that applies his method using Excel and @Risk to a > sample of Microsoft returns. I think it would be relatively simple to > program it in R. > > Charles Ward > > On 24 November 2011 19:43, Brian G. Peterson <[hidden email] > > wrote: > >     On Thu, 2011-11-24 at 16:19 +0100, Joachim Breit wrote: >      > Thank you for the interesting link. >      > >      > I agree that you can use a (fit of a) stable distribution for >     sampling >      > purposes. But please: Why not simply use the raw data and sample from >      > that? What could be a better starting point? Again, it is clear that >      > you cannot use the raw return series as it comes off your data feed; >      > there is a need for adjusting. But there cannot be a better fit >     to the >      > raw data than the raw data itself... > >     Of course you start with sampling from your data, but if all you do is >     sample from the data, with no 'noise', then all you are doing is >     smoothing out the prior observations.  This may lead you to a false >     sense of security, and give you an incorrect idea of how likely a fat >     tailed event is.  So by adding noise from a stable or other fat tailed >     distribution (skewed Student's T is also popular), you are believing >     that your prior data is both stationary and fully representative. > >     Regards, > >       - Brian > >     -- >     Brian G. Peterson >     http://braverock.com/brian/>     Ph: 773-459-4973 >     IM: bgpbraverock > >     _______________________________________________ >     [hidden email] >     mailing list >     https://stat.ethz.ch/mailman/listinfo/r-sig-finance>     -- Subscriber-posting only. If you want to post, subscribe first. >     -- Also note that this is not the r-help list where general R >     questions should go. > > _______________________________________________ [hidden email] mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-finance-- Subscriber-posting only. If you want to post, subscribe first. -- Also note that this is not the r-help list where general R questions should go.
Open this post in threaded view
|

## Re: Option valuation for arbitrary distribution using monte carlo simulation

 In reply to this post by klswacha I gave it a try. See the (ugly) code below. Comments are welcome. There are still a few issues to solve: - How can one do the Monte Carlo simulation faster/more efficient? - Can somebody explain Winston's daily risk free rate? - Is there a better optimization algorithm for the utility function than genoud? ---------------------------------------------- library("fImport") library('fOptions') library("rgenoud") msft = yahooImport(query="MSFT&a=00&b=4&c=1999&d=03&e=9&f=1999&ignore=.csv",file = "msft", source = "http://ichart.finance.yahoo.com/table.csv?", save = TRUE) cl = msft@data[,"Close"]   ### unadjusted close prices cl[10:67] = cl[10:67]/2    ### a 2:1 split on 1999-03-29 ret = cl[1:66] /cl[2:67] - 1   ### the returns logret = log(1+ret)            ### log returns vola = sd(logret) * sqrt(252) ###  0.4091751 bsm = GBSOption(TypeFlag = "p", S = 90, X = 80, Time = 63/252, r = log(1+0.08), b = log(1+0.08), sigma = vola)@price  #### 2.577398 (just to compare) rf = .000317   ## don't know where this comes from in Winston's paper;                 ## should be 1.08^(1/252)-1 = 0.0003054476, right? meanutil <- function (alpha) { x = log(alpha * (1 + ret) + (1 - alpha) * (1 + rf))  ### portf. utility x[which(is.nan(x))] = -100000    ### correct for ln(x<0) undefined x = mean(x) return(x) } optalpha = genoud(meanutil, nvars = 1, max = TRUE, pop.size = 1000)\$par ### the optimal portfolio under utility function ln(x) portret = optalpha * (1 + ret) + (1 - optalpha) * (1 + rf) ## the optimal portfolio returns (in the sense of: daily end wealth) num = (1 / length(ret)) * (1 / portret) rnprob = num/sum(num)  ### the risk neutral probabilities ov=0           #### monte carlo simulation for (i in 1:100000) { ov[i] = max(80 - 90 * prod(1+ sample(ret, size=63, replace = TRUE, prob = rnprob)),   0  ) } mov = 1/1.08^(63/252) * mean(ov)     ### 2.512722 = option value Am 25.11.2011 09:48, schrieb Charles Ward: > Luenberger discusses the task of creating risk-neutral probabilities > from any set of observed returns. I cannot find the specific reference > in his book (Investment Science) but there is a discussion paper by > Winston (google) that applies his method using Excel and @Risk to a > sample of Microsoft returns. I think it would be relatively simple to > program it in R. > > Charles Ward _______________________________________________ [hidden email] mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-finance-- Subscriber-posting only. If you want to post, subscribe first. -- Also note that this is not the r-help list where general R questions should go.