
First of all sorry for my bad english but its not my native language.
I am working on a paper on Portfolio Optimization with Markowitz and Lower Partial Moments.
I want to compare the returns of the minimum variance portfolios from booth methods. First of all i have an insample multivariate timeseries from 4 stocks reaching from 1.october.2011 to 1.october 2012 (logreturns, daily data, 257 observations for each stock).
I start to optimize my portfolio using the package tseries as follow:
portfolio.optim(x,pm=mean(x),riskless=F,rf=0,shorts=F,reslow=NULL,reshigh=NULL,covmat=cov(x))
with this i get the weights, the mean return of the whole period, the standard deviation and the returns on each day for my insample optimal portfolio Markowitz portfolio.
The out of sample data reaches from 2.october.2012 to 1.october.2013 (logreturns,daily data, 253 observations for each stock, again a multivariate time series). Now i want to optimize the Portfolio 253 times. Each time the logreturns for one day should be added to the original insample timeseries (first optimization 257 insample data plus the first from the out of sample data and so on). Now i should get new weights for every of the 253 periods and therefor new returns for the portfolio every period.
My advisor at the university told me i cant use backtest packages cause they cant handle the Lower Partial Moments part of my analysis. The problem is just for the markowitz portfolio optimization.
I hope you can help me with my problem
greetings wintwin111
