Hello everyone,
I would like assistance with a snippet I have written to do a recursive
portfolio optimization given time-varying return forecasts.
In my case, I have forecast the monthly returns for nearly 55 years out on 8
asset classes.
I need to calculate the weights for the optimal (tangency) portfolio based on
my monthly forecasts and an arbitrary covariance matrix
Getting these weights have proven difficult.
# these are forecast (out of sample) returns; each is a 648x1 matrix
cash_forecast2=as.ts(cash_forecast)
larg_forecast2=as.ts(larg_forecast)
valu_forecast2=as.ts(valu_forecast)
grow_forecast2=as.ts(grow_forecast)
smal_forecast2=as.ts(smal_forecast)
tres_forecast2=as.ts(tres_forecast)
cred_forecast2=as.ts(cred_forecast)
comm_forecast2=as.ts(comm_forecast)
# make a matrix of all expected returns
# each line corresponds to forecast monthly returns for each asset class; this
is a 648x8 matrix
asset_forecast=ts.intersect(cash_forecast2, larg_forecast2,valu_forecast2,
grow_forecast2, smal_forecast2, tres_forecast2, cred_forecast2, comm_forecast2)
# make a covariance matrix based on the entire data
actual_ret=cbind(cash_ret,
larg_ret,valu_ret,grow_ret,smal_ret,tres_ret,cred_ret,comm_ret)
cov_matrix=cov(actual_ret)
opt_port = ts(matrix(,nrow=648,ncol=8))
for (i in 1:648) opt_port[i,]= portfolio.optim(asset_forecast[i,],
riskless=FALSE, shorts=TRUE, covmat = "cov_matrix",
by.column = FALSE, by=1, align="right")
I get the following error message; "Error in
portfolio.optim.default(asset_forecast[i, ], shorts = TRUE, covmat =
"cov_matrix", : x is not a matrix"
So clearly, asset_forecast[i,] is not a matrix. So I need another method to do
this. Can anyone suggest a solution that would allow my to set sail in the
right direction?
Many thanks,
Bond, Jamesss....sorry that's my screen name... Darius :)
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