How to compute the global minimum-variance portfolio

Author

Keywords

portfolio optimisation, R

Review Status

draft

Introduction

The global minimum-variance (MV) portfolio is the leftmost point of the mean-variance efficient frontier. It is found by solving the problem

(1)

$\begin{split} \min_{w} w' \Sigma w\ w'\iota = 1 \end{split}$

where $w$ is the vector of portfolio weights, $\Sigma$ is the variance-covariance matrix of the assets, and $\iota$ is an appropriately-sized vector of ones. We do not put any further constraints on the problem, in particular, short sales are allowed here.

Using a QP solver

R

We use the quadprog package for R (see the references below).

require(quadprog)

# create artificial data with mean 0 and sd 5%
nO <- 100 # number of observations
nA <- 10  # number of assets
mData <- array(rnorm(nO * nA, mean = 0, sd = 0.05), dim = c(nO, nA))

## 
# qp
aMat  <- array(1, dim = c(1,nA))
bVec  <- 1
zeros <- array(0, dim = c(nA,1))
solQP <- solve.QP(cov(mData), zeros, t(aMat), bVec, meq = 1)

The quadprod package will minimise an obvective function $\frac{1}{2} w' \Sigma w$ , hence the returned minimum-value will be one-half of the portfolio's variance.

all.equal(as.numeric(var(mData %*% solQP$solution)), as.numeric(2 * solQP$value))

A regression representation

The problem can also be implemented as a regression, see Kempf and Memmel [1]. The authors show that regressing the negative excess returns of $\mathrm{n_A} - 1$ assets on the returns of the remaining asset results in coefficients that equal the $\mathrm{n_A} - 1$ respective portfolio weights; the remaining asset's weight is determined by the budget constraint. (Excess return here means with respect to the remaining asset.) Formally, we run the regression

(2)

$r_{\mathrm{n_A}} = \alpha + w_1 (r_{\mathrm{n_A}} - r_1) + w_2 (r_{\mathrm{n_A}} - r_2) + \ldots + w_{\mathrm{n_A}-1}(r_{\mathrm{n_A}}-r_{\mathrm{n_A}-1}) + \epsilon$

where $r_i$ is the vector of returns of the $i$ th asset, and $\epsilon$ is the vector of errors.

From this equation, we directly obtain the weights $w_1$ to $w_{\mathrm{n_A}-1}$ . Since the weights need to sum to unity, we have

(3)

$w_{\mathrm{n_A}} = 1 - \sum_{i=1}^{\mathrm{n_A}-1} w_i\,.$

Furthermore, the resulting portfolio's mean return will equal $\alpha$ , and the portfolio's variance will equal the variance of the residuals.

R

# (...continued)

##
# regression
# choose 1st asset as regressand
y <- mData[,1]
X <- mData[,1] - mData[,2:nA]
solR <- lm(y~X)

The results should be the same as the results from quadprog.

# weights from qp
as.vector(solQP$solution)

# weights from regression
as.vector(c(1-sum(coef(solR)[-1]), coef(solR)[-1]))

# variance of portfolio
all.equal(as.numeric(var(mData %*% solQP$solution)), var(solR$residuals))

Solving a system of linear equations

The weights can also be found by solving a system of linear equations.

R

# solve
x <- solve(cov(mData), numeric(nA) + 1)

# rescale
x <- x / sum(x)

The resulting vector x should give the same weights.

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References

1. Kempf, A. and C. Memmel (2006). Estimating the Global Minimum Variance Portfolio. Schmalenbach Business Review 58, 332-348.

2. R Development Core Team (2008). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. http://www.R-project.org.

3. Turlach, B.A. [S original] and A. Weingessel [R port] (2007). quadprog: Functions to solve Quadratic Programming Problems. R package version 1.4-11. available from CRAN.

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Page tags: new portfolio_optimisation r

page_revision: 17, last_edited: 1248788533|%e %b %Y, %H:%M %Z (%O ago)

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