What programs/packages can one use to minimize a portfolio's tracking error? What I am trying to do is see what ex post TE, portfolio returns and variance can be achieved when adding CSR constraints to the S&P500 by running 5 different specifications of expected(ex ante) TE. 4 linear specifications from Rudolf et al (1999) and the traditional TE variance from Roll (1992).

My thesis supervisor told me to use solver but I don't have access to a version that can handle more than 100 variables and I am completely new to R. I have taken a look at some packages like PortfolioAnalytics but it is not obvious to me how I should put in the different TE specifications. In matrix notation the TE specifications are as follows:

TE specifications

  • $\begingroup$ Perhaps an answer to my own question: To force the problem to fit in the version of solver that I have access to I'm currently subdividing the constituent lists of the index by industry and using that to make the optimization problem smaller. I guess it will also make the tracking error smaller as well by not throwing out entire low scoring industries. $\endgroup$ – Wouter Adolfsen Jun 27 '18 at 11:02
  • $\begingroup$ Perhaps you could use cvxopt $\endgroup$ – noob2 Jun 27 '18 at 13:17
  • $\begingroup$ There is a new R package on sparse index tracking. Maybe you find this interesting: cran.r-project.org/web/packages/sparseIndexTracking/vignettes/… $\endgroup$ – Ric Jun 27 '18 at 16:04

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