copula_wireo3t_估计copula参数_混合copula函数_matlabcopula_matlabcopula函数

%基于copula函数计算的投资组合价值 spec=garchset('C',NaN,'Distribution','t','P',1,'Q',1,'display','off'); [Coeff,Errors,LLF,Innovations,Sigmas,Summmary]=garchfit(spec,data(:1)); stand_1=Innovations./Sigmas; [sig2exp_1 uexp_1]=garchpred(Coeff,data(:1)); [Coeff,Errors,LLF,Innovations,Sigmas,Summmary]=garchfit(spec,data(:2)); stand_2=Innovations./Sigmas; [sig2exp_2 uexp_2]=garchpred(Coeff,data(:2)); [Coeff,Errors,LLF,Innovations,Sigmas,Summmary]=garchfit(spec,data(:3)); stand_3=Innovations./Sigmas; [sig2exp_3 uexp_3]=garchpred(Coeff,data(:3)); [Coeff,Errors,LLF,Innovations,Sigmas,Summmary]=garchfit(spec,data(:4)); stand_4=Innovations./Sigmas; [sig2exp_4 uexp_4]=garchpred(Coeff,data(:4)); ut_1=tcdf(stand_1,3.9171): ut_2=tcdf(stand_2,5.0595); ut_3=tcdf(stand_3,4.5625); ut_4=tcdf(stand_4,6.9737); UT=[ut_1,ut_2,ut_3,ut_4]; [RHOHAT,nuhat]=copulafit('t',UT,'Method','ApproximateML'); %t-copula函数的蒙特卡洛模拟 U=copularnd('t',RHOHAT,nuhat,10000); U=U'; simulationu(1,:)=tinv(U(1,:),3.9171); simulationu(2,:)=tinv(U(2,:),5.0595); simulationu(3,:)=tinv(U(3,:),4.5625); simulationu(4,:)=tinv(U(4,:),6.9737); weight=[0.25 0.25 0.25 0.25]; total_return=weight*[sig2exp_1.*simulationu(1,:);sig2exp_2.*simulationu(2,:);sig2exp_3.*simulationu(3,:);+sig2exp_4.*simulationu(4,:);]; tr=sort(total_return); %投资组合的VaR和CVaR值 var=[tr(100) tr(250) tr(500)] cvar=[mean(tr(1:100)) mean(tr(1:250)) mean(tr(1:500))] %CVaR优化程序 [J,nAssets]=size(ScenRets);%将收益率的模拟值保存在变量ScenRets中 w0=[(1/nAssets)*ones(1,nAssets)]; LB=0; UB=1; beta=.99; VaR0=quantile(ScenRets*w0',.99); i=1:nAssets; Riskfun=@(w) w(nAssets+1)+(1/J)*(1/(1-beta))*sum(max(-w(i)*ScenRets(:,i)'-w(nAssets+1),0)); wo=[wo VaR0]; A=[-mean(ScenRets) 0]; A=[A; -eys(nAssets) zeros(nAssets,1)]; A=[A; eys(nAssets) zeros(nAssets,1)]; b=[-R0 -LB*ones(1,nAssets) UB*ones(1,nAssets)];%R0为设定的期望收益率； b=b'; Aeq=[ones(1,nAssets) 0]; beq=[1]; options=optimset('LargeScale','off'); options=optimset(options,'MaxFunEvals',5000); %其中VaR的值为优化结果中w(nassets+1)的值 [w,fval,exitflag,output]=fmincon(Riskfun,wo,A,Aeq,beq,LB,UB,[],options)