The{BLCOP} package is an implementation of theBlack-Litterman and copula opinion pooling frameworks. TheBlack-Litterman model was devised in 1992 by Fisher Black and RobertLitterman. Their goal was to create a systematic method of specifyingand then incorporating analyst/portfolio manager views into theestimation of market parameters.
BLViews() andCOPViews() construct viewsobjectsaddBLViews() andaddCOPViews() allow moreviews to be added to existing objectsdistribution() andmvdistribution() createdistribution andmvdistribution objectsBLPosterior() calculates the posterior distributionusing the Black-Litterman modelCOPPosterior() calculates the posterior distributionusing copula opinion poolingYou can install the released version of BLCOP fromCRAN with:
install.packages("BLCOP")And the development version fromGitHub with:
# install.packages("devtools")devtools::install_github("MangoTheCat/BLCOP")library(BLCOP)# For a matrix of monthly returns for 6 assetshead(monthlyReturns)#> IBM MS DELL C JPM BAC#> 1998-02-02 0.057620253 0.19578623 0.40667739 0.1224778047 0.157384084 0.143954576#> 1998-03-02 -0.005457679 0.04383326 -0.51565628 0.0785547367 0.087215863 0.064817518#> 1998-04-01 0.115529027 0.08233841 0.19188192 0.0198333333 0.027283511 0.041952290#> 1998-05-01 0.014067489 -0.01027006 0.02055728 0.0009805524 -0.018908776 -0.006578947#> 1998-06-01 -0.022893617 0.17050986 0.12619828 -0.0101224490 -0.444607915 0.015761589#> 1998-07-01 0.154080655 -0.04717084 0.17002478 0.1091868712 0.001589404 0.039900900# Define a pick matrix (a vector of confidences)pickMatrix<-matrix(c(1/2,-1,1/2,0,0,0),nrow =1,ncol =6)# Create a views objectviews<-BLViews(P = pickMatrix,q =0.06,confidences =100,assetNames =colnames(monthlyReturns))# Determine the posterior distribution of these assetsBLPosterior(monthlyReturns, views,tau =1/2,marketIndex = sp500Returns)#> Prior means:#> IBM MS DELL C JPM BAC#> 0.002269870 0.005799591 -0.001161339 0.001718354 -0.009042287 0.005472691#> Posterior means:#> IBM MS DELL C JPM BAC#> 0.009795730 -0.016744179 0.014453759 -0.004741680 -0.015465517 0.001505639#> Posterior covariance:#> IBM MS DELL C JPM BAC#> IBM 0.022113337 0.011762652 0.013388809 0.009418743 0.01189892 0.006017050#> MS 0.011762652 0.033040555 0.018441735 0.014076656 0.01650328 0.009143918#> DELL 0.013388809 0.018441735 0.048344919 0.008453909 0.01088555 0.005957519#> C 0.009418743 0.014076656 0.008453909 0.017307957 0.01246270 0.007215142#> JPM 0.011898924 0.016503281 0.010885549 0.012462701 0.03032755 0.012937189#> BAC 0.006017050 0.009143918 0.005957519 0.007215142 0.01293719 0.011893184