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Catégorie :Category: nCreator TI-Nspire
Auteur Author: SPITZER2001
Type : Classeur 3.0.1
Page(s) : 1
Taille Size: 2.00 Ko KB
Mis en ligne Uploaded: 21/11/2024 - 20:53:10
Uploadeur Uploader: SPITZER2001 (Profil)
Téléchargements Downloads: 3
Visibilité Visibility: Archive publique
Shortlink : http://ti-pla.net/a4333583
Type : Classeur 3.0.1
Page(s) : 1
Taille Size: 2.00 Ko KB
Mis en ligne Uploaded: 21/11/2024 - 20:53:10
Uploadeur Uploader: SPITZER2001 (Profil)
Téléchargements Downloads: 3
Visibilité Visibility: Archive publique
Shortlink : http://ti-pla.net/a4333583
Description
Fichier Nspire généré sur TI-Planet.org.
Compatible OS 3.0 et ultérieurs.
<<
Number of shares : = initial weight*initial portfolio value/constituent price Post rebalance =SI($N3(rebalancing);A$3(initial weight)*$M3(portfolio value);H3(sinon prebalance value) Portfolio expected return =SOMMEPROD(weights;expectedreturn) Portfolio variance = SOMMEPROD((TRANSPOSE weights);PRODUITMAT(cov mat;TRANSPOSE(weights))) Covariance 2 portfolios =SOMMEPROD(TRANSPOSE(weightsp2);PRODUITMAT(covmat ;TRANSPOSE(weightsp1))) Risk tolerance parameter (t) = RACINE((a*targetvolat^2-1)/(a*c-b^2)) Uncorellated veut dire que la matrix de convariance va etre diagonal avec sur les diagonales les valeurs des volatilités au carée (variance) GMV portfolio weights : =£^(-1) * 1/SOMME((£^(-1) * 1)de tout les poids)) Reciprocals of variances : =1/Volatilité A^2 Inverse variance portfolio : =Reciprocals of variance A /SOMME(Reciprocals of variances) We note that the inverse variance portfolio coincides with the GMV portfolio. Inverse covariance matrix (£^(-1)) =INVERSEMAT(cov matrix) Efficient portfolio with target volatility by matrix formula Weights1 = b*(t)*Gmv weight 1 +(1-b*(t))*Tan weight 1 Covariance matrix formula : =COVARIANCE.PEARSON(DECALER(Constituents!$N$5:$N$185;0;MAX($A32;B$31)-1);DECALER(Constituents!$N$5:$N$185;0;MIN($A32;B$31)-1)) 1 / a is the variance of the GMV portfolio, so 1 / sqrt(a) is the minimum volatility b / a is the expected return on the GMV portfolio c is the maximum squared expected return-to-volatility ratio, so sqrt(c) is the maximum ratio Efficient frontier moment : Expected return (mu_p) : = b / a +RACINE(c - b^2 / a)*RACINE(Volatility (sigma_p) ^2-1 / a) Made with nCreator - tiplanet.org
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Compatible OS 3.0 et ultérieurs.
<<
Number of shares : = initial weight*initial portfolio value/constituent price Post rebalance =SI($N3(rebalancing);A$3(initial weight)*$M3(portfolio value);H3(sinon prebalance value) Portfolio expected return =SOMMEPROD(weights;expectedreturn) Portfolio variance = SOMMEPROD((TRANSPOSE weights);PRODUITMAT(cov mat;TRANSPOSE(weights))) Covariance 2 portfolios =SOMMEPROD(TRANSPOSE(weightsp2);PRODUITMAT(covmat ;TRANSPOSE(weightsp1))) Risk tolerance parameter (t) = RACINE((a*targetvolat^2-1)/(a*c-b^2)) Uncorellated veut dire que la matrix de convariance va etre diagonal avec sur les diagonales les valeurs des volatilités au carée (variance) GMV portfolio weights : =£^(-1) * 1/SOMME((£^(-1) * 1)de tout les poids)) Reciprocals of variances : =1/Volatilité A^2 Inverse variance portfolio : =Reciprocals of variance A /SOMME(Reciprocals of variances) We note that the inverse variance portfolio coincides with the GMV portfolio. Inverse covariance matrix (£^(-1)) =INVERSEMAT(cov matrix) Efficient portfolio with target volatility by matrix formula Weights1 = b*(t)*Gmv weight 1 +(1-b*(t))*Tan weight 1 Covariance matrix formula : =COVARIANCE.PEARSON(DECALER(Constituents!$N$5:$N$185;0;MAX($A32;B$31)-1);DECALER(Constituents!$N$5:$N$185;0;MIN($A32;B$31)-1)) 1 / a is the variance of the GMV portfolio, so 1 / sqrt(a) is the minimum volatility b / a is the expected return on the GMV portfolio c is the maximum squared expected return-to-volatility ratio, so sqrt(c) is the maximum ratio Efficient frontier moment : Expected return (mu_p) : = b / a +RACINE(c - b^2 / a)*RACINE(Volatility (sigma_p) ^2-1 / a) Made with nCreator - tiplanet.org
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