求解蒙特卡罗模拟金融衍生品定价（张树德那本书）代码意义

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function [p,ci]=AsianMCCV(s0,k,r,t,sigma,nsamples,nrepl,npilot)
trypath=assetpaths1(s0,r,sigma,nsamples,npilot);
stocksum=sum(trypath,2);
pp=mean(trypath(:,2:(nsamples+1),2);
trypayoff=exp(-r*t)*max(0,pp-x);
matcov=cov(stocksum,trypayoff);
dt=t/nsamples;
expsum=s0*(1-exp((nsamples+1)*r*dt))/(1-exp(r*dt));
controlvars=zeros(nrepl,1);
for i=1:nrepl
    stockpath=assetpaths1(s0,r,sigma,t,nsamples,1);
    payoff(i)=exp(-r*t)*max(0,mean(stockpath(2:(nsamples+1)))-x);
    controlvars(i)=payoff(i)+c*(sum(stockpath)-expsum);
end
[p,aux,ci]=normfit(controlvars);
function spaths=assetpahts1(s0,mu,sigma,nsteps,nrepl)
dt=t/nsteps;
nudt=(mu-0.5*sigma^2)*dt;
sidt=sigma*sqrt(dt);
increments=nudt+sidt*randn(nrepl,nsteps);
logpaths=cumsum([log(s0)*ones(nrepl,1),increments],2)
spaths=exp(logpaths);

感觉trypath也不是向量啊，怎么可以对列求和或对行求和（红色标记出来）

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关键词：蒙特卡罗模拟 金融衍生品 衍生品定价 蒙特卡罗 金融衍生 蒙特卡罗 function 衍生品

1. function S = AssetPaths(S0,mu,sig,dt,steps,nsims)
   
2. % Function to generate sample paths for assets assuming geometric
   
3. % Brownian motion.
   
4. %
   
5. % S = AssetPaths(S0,mu,sig,dt,steps,nsims)
   
6. %
   
7. % Inputs: S0 - stock price
   
8. %       : mu - expected return
   
9. %       : sig - volatility
   
10. %       : dt - size of time steps
    
11. %       : steps - number of time steps to calculate
    
12. %       : nsims - number of simulation paths to generate
    
13. %
    
14. % Output: S - a matrix where each column represents a simulated
    
15. %             asset price path.
    
16. %
    
17. % Notes: This code focuses on details of the implementation of the
    
18. %        Monte-Carlo algorithm.
    
19. %        It does not contain any programatic essentials such as error
    
20. %        checking.
    
21. %        It does not allow for optional/default input arguments.
    
22. %        It is not optimized for memory efficiency or speed.
    
23. 
    
24. % Author: Phil Goddard (phil@goddardconsulting.ca)
    
25. % Date: Q2, 2006
    
26. 
    
27. % calculate the drift
    
28. nu = mu - sig*sig/2;
    
29. 
    
30. % Generate potential paths
    
31. S = S0*[ones(1,nsims); ...
    
32.             cumprod(exp(nu*dt+sig*sqrt(dt)*randn(steps,nsims)),1)];

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