simulvar.m

统计工具软件

function [svar] = simulvar(DR,RC,X,delta,valuta,legame,vm,t,p,tipo,rendimento)

%SIMULVAR	hystorical simulation VaR.
%
%	[svar] = simulvar(DR,RC,X,delta,valuta,legame,vm,t,p,tipo,rendimento) 
%   calculates historical simulation VaR given the vector vm of portfolio
%   value, holding period t, probability p, the X matrix of asset prices,
%   the RC matrix of exchange rates, the DR matrix of risk factors values,
%   the vectors legame and valuta.
%   If tipo = 0 uses the historical data of risk factors only and the delta
%   coefficients, if tipo = 1 uses the asset prices directly. Put
%   rendimento = 0 if the yields are calculated with aritmetic methods, 1
%   if calculates with logaritmic methods. Market value are based on the
%   last asset prices.
%
%	All data must be in columns.
%
%	See DELTAVAR, MONTEVAR
%

%	Copyright (c) 2004 by Flavio Bazzana
%   Department of Computer and Management Sciences
%   University of Trento
%   38100 - Trento ITALY
%   [e-mail]

m = length(legame);
n = size(X,1);
if tipo == 0
	Z(1,:)=X(1,:);
	prezzi=X(n,:)';

% calculation of asset prices on the basis of historical returns
% of risk factors and mapping factors

	for j=2:n
		for k=1:m
			h=legame(k);
			if rendimento==0
				Z(j,k)=Z(j-1,k)*(1+delta(k)*DR(j-1,h));
				else
				Z(j,k)=Z(j-1,k)*exp(delta(k)*DR(j-1,h));
			end
		end
	end

% historical matrix of currencies

	cp = vm./prezzi;
	for j=1:m
		if valuta(j)==0
			X1(:,j)=ones(n,1);
			else X1(:,j)=RC(:,valuta(j));
		end
	end

% calculation of portfolio VaR

	X2 = Z.*X1;

% calculation of portfolio VaR directly from the assets

	else
	cp = vm./X(n,:)';
	for j=1:m
		if valuta(j)==0
			X1(:,j)=ones(n,1);
			else X1(:,j)=RC(:,valuta(j));
		end
	end
	X2 = X.*X1;

end
vp = X2*cp;

% ordering the daily losses

dvp = diff(vp);
sdvp = sort(dvp);

% calculation of portfolio VaR

n = length(sdvp);
svar = -sdvp(fix(n-n*p))*sqrt(t); 

% graph

[freq,classi]=hist(dvp*sqrt(t),n/25);
bar(classi,freq,'b')
line([-svar -svar],[0 max(freq)],'color','red')
title('Historical VaR simulation')
xlabel('classes of losses')
ylabel('frequencies')
text(-svar+(svar/10),max(freq)/2,num2str(p))