Vincent Zoonekynd
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 Sep 24 awarded Autobiographer Jan 19 awarded Yearling Aug 16 comment DCF of Arbitrary Dates Cash Flows The conversion between the dates and the year fraction is determined by the day counting convention you use. The following document explains this in more details: marchioro.webs.com/White-papers/… May 7 revised How to implement Maximum Diversification in R? Correct formula (the documentation is typographically ambiguous, but b' D b is in the numerator) May 7 suggested approved edit on How to implement Maximum Diversification in R? Apr 16 comment PCA Variances and Principal Portfolio Variances If I run your code, I have identical values. Try in a fresh session (rm(list=ls()) is usually not sufficient). Jan 19 awarded Yearling Jul 8 comment Methods for distributing cash into allocation This is called dollar cost averaging, but it just says that if you do not have all the money at the begining and if you do not have any view on the evolution of the market, you should not worry and invest progressively, as the money arrives: the market fluctuations will more or less even out. But if you have all the money at the begining and/or know how the market will change, this is suboptimal. Jun 24 revised Any thoughts on how Warren Buffet's B of A warrants might be “marked-to-market” by either counterparty? Escape the $signs Jun 24 suggested approved edit on Any thoughts on how Warren Buffet's B of A warrants might be “marked-to-market” by either counterparty? Jun 21 awarded Commentator Jun 21 comment Quadratic Programming Problem I assume that$V$and$G$are variance matrices (of returns, etc.): in particular, they are positive semi-definite. If there are no constraints, then$X=0$is a solution. If you have equality constraints, you can use Lagrange multipliers. If you also have inequality constraints, you can use a quadratic solver. I am not sure what you mean by "both V and G should be uncorrelated" (diagonal variance matrices?). Jun 7 comment How to quickly estimate a lower bound on correlation for a large number of stocks? An upper bound, in the general case, can be obtained in the same way -- compute the determinant of the$3\times3$matrix and solve for$c$: one of the roots is a lower bound, the other an upper bound. The formula is the same, except for the sign in front of the square root. May 28 comment Historical volatility from close prices (Haug pg 166) You seem to have logarithms of squared ratio returns (log_squared_returns) instead of squares of log-returns: sum_squares_1 can be negative. May 10 comment GJR-GARCH Model In R library(sos); ???GJR suggests that the rugarch package can fit this model. May 5 comment MPT: Adding constraint on minimum asset weight @BobJansen: I have made the change you suggested, to match the constraints in the question. May 5 revised MPT: Adding constraint on minimum asset weight Use the same bounds as in the question May 5 comment MPT: Adding constraint on minimum asset weight The constraints $w_i = 0$or$0.01 \leq w_i \leq 0.05$'' can be rewritten as$0.01 n_i \leq w_i \leq 0.05 n_i$by adding binary variables$n_i \in \{0,1\}$. The constraints are still linear, but the objective function is still quadratic: we would need a mixed integer quadratic solver -- Rglpk is only a (mixed integer) linear solver... May 4 answered MPT: Adding constraint on minimum asset weight May 4 comment MPT: Adding constraint on minimum asset weight That only considers the constraint$0 \leq w_i \leq 0.05$, not the non-convex constraint $w_i=0$or$0.01 \leq w_i \leq 0.05\$''.