rolling regression

Dear all,

I have spent the last few days on a seemingly simple and previously documented rolling regression.

I have a 60 year data set organized in a ts matrix.
The matrix has 5 columns; cash_ret, epy1, ism1, spread1, unemp1

I have been able to come up with the following based on previous help threads. It seems to work fine.
The trouble is I get regression coefficients but need the immediate next period forecast.

cash_fit= rollapply(cash_data, width=60,

function(x) coef(lm(cash_ret~epy1+ism1+spread1+unemp1, data = as.data.frame(x))),

by.column=FALSE, align="right"); cash_fit


I tried to replace "coef" above to "predict" but I get a whole bunch of results too big to be displayed. I would be grateful
if someone could guide me on how to get the next period forecast after each regression.

If there is a possibility of getting the significance of each regressor and the standard error in addition to R-sq
without having to spend the next week, that would be helpful as well.

Many thanks,
Darius





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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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