monteback.m

统计工具软件

function [mn,mvar,vp] = monteback(X,RF,RC,legame,valuta,vm,p,rendimento,campione,dati,tipo,l,lambda,datidelta)

%DELTABACK	backtesting for Monte Carlo VaR.
%
%	[mn,mvar,vp] = monteback(X,RF,RC,legame,valuta,vm,p,rendimento,campione,dati,tipo,l,lambda,datidelta) 
%   calculates the number of outlier in Monte Carlo VaR portfolio
%   backtesting. Campione is the number of simulation (for Basel 250), dati
%   is the number of data used for VaR calculation, and l is the number of
%   monate Carlo simulations.
%	Lambda is the weight in EWMA volatility forecast.
%   If Datidelta = 0 the mapping procedure is calculated over all X,
%   = 1 only with defined data.
%
%	All data must be in columns.
%
%	See MONTEVAR
%

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

n=size(X,1);
m=length(valuta);
[DX,DR,cambi,fattori,prezzi] = rendimenti(X,RF,RC,rendimento);
[deltax,dev,cor,C] = semplicecorr(DX,DR,legame);
cp = vm./prezzi;
cp = vm./prezzi;

for i=1:campione+1
home; disp(i)

% VaR interval

	X1=X(n-dati-i:n-i,:);
	RF1=RF(n-dati-i:n-i,:);
	RC1=RC(n-dati-i:n-i,:);
	
% calculation of montevar over such interval
	
	[DX,DR,cambi,fattori,prezzi] = rendimenti(X1,RF1,RC1,rendimento);
	if lambda==1
		[delta,dev,cor,C] = semplicecorr(DX,DR,legame);
		else [delta,dev,cor,C] = ewmacorr(DX,DR,legame,lambda);
	end
	if datidelta==1
		[mvar(i)] = montevar(delta,C,legame,valuta,cambi,fattori,prezzi,vm,1,p,tipo,l,rendimento);
		else [mvar(i)] = montevar(deltax,C,legame,valuta,cambi,fattori,prezzi,vm,1,p,tipo,l,rendimento);
	end

% calculation of portfolio value
	
	for j=1:m
		if valuta(j)==0
			k1(j)=1;
			else k1(j)=cambi(valuta(j));
		end
	end
	k2 = prezzi.*k1';
	vp(i) = k2'*cp;

end

% calculation of the exceptions

mn=0;
for i=1:campione
	if vp(i+1)-vp(i)<-mvar(i)
		mn=mn+1;
	end
	if vp(i+1)-vp(i)>mvar(i)
		mn=mn+1;
	end
end

f=1:campione;
plot(f,-mvar(1:campione),'b',f,mvar(1:campione),'b',f,diff(vp),'r')
title('Monte Carlo VaR backtesting')
xlabel('Sample')
ylabel('VaR')