Monte Carlo模拟算法的Matlab实现.rar

npv=[] n=5000; tr=0.3; dr1=0.51; dr2=0.391; inv=82000000; for k=1:n x1=normrnd(4815.625,992.063,1,4); x2=normrnd(59942.2,22681.22,1,4); x3=normrnd(0.716,0.07829,1,4); x4=normrnd(6.242,0.988,1,4); x5=normrnd(33029.2,12015.6424,1,4); x6=normrnd(5842.2,2165.79,1,4); x7=normrnd(9291,3935.6463,1,4); y=0; z=0; for t=1:4 y=y+((x1(t)*x2(t)-x4(t)*x5(t)*1000)*x3(t)-x6(t)*1000-x7(t)*1000)/(1.11^t); end x11=normrnd(6524.378,849.243,1,6); x21=normrnd(89124.34,47839.14,1,6); x31=normrnd(0.8278,0.0085,1,6); x41=normrnd(7.96,0.24,1,6); x51=normrnd(63524.23,18293.243,1,6); x61=normrnd(6793.4,1789.23,1,6); x71=normrnd(5732,4513.92,1,6); for t=1:6 z=z+((x11(t)*x21(t)-x41(t)*x51(t)*1000)*x31(t)-x61(t)*1000-x71(t)*1000)/(1.11^(t+4)); end npv(k)=(y*dr1+z*dr2)*(1-tr)-inv; end [f,z]=ksdensity(npv); plot(z,f); resvar=var(npv); resexp=mean(npv); %xi=linspace(-400000000,400000000); %f=ksdensity(npv,xi,'function','cdf'); %plot(xi,f);