This function designs risk parity portfolios to equalize/distribute the risk contributions of the different assets, which is missing if we simply consider the overall volatility of the portfolio as in the mean-variance Markowitz portfolio. In addition to the vanilla formulation, where the risk contributions are perfectly equalized subject to no shortselling and budget constraints, many other formulations are considered that allow for box constraints, as well as the inclusion of additional objectives like the expected return and overall variance. In short, this function solves the following problem:
minimize R(w) - lmd_mu * t(w) %*% mu + lmd_var * t(w) %*% Sigma %*% w
subject to sum(w) = 1, w_lb <= w <= w_ub,
Cmat %*% w = cvec, Dmat %*% w <= dvec,
where R(w) denotes the risk concentration,
t(w) %*% mu is the expected return, t(w) %*% Sigma %*% w is the
overall variance, lmd_mu and lmd_var are the trade-off weights
for the expected return and the variance terms, respectively, w_lb and
w_ub are the lower and upper bound vector values for the portfolio vector w,
Cmat %*% w = cvec denotes arbitrary linear equality constrains, and
Dmat %*% w = dvec denotes arbitrary linear inequality constrains.
riskParityPortfolio(
Sigma,
b = NULL,
mu = NULL,
lmd_mu = 0,
lmd_var = 0,
w_lb = 0,
w_ub = 1,
Cmat = NULL,
cvec = NULL,
Dmat = NULL,
dvec = NULL,
method_init = c("cyclical-spinu", "cyclical-roncalli", "newton"),
method = c("sca", "alabama", "slsqp"),
formulation = NULL,
w0 = NULL,
theta0 = NULL,
gamma = 0.9,
zeta = 1e-07,
tau = NULL,
maxiter = 1000,
ftol = 1e-08,
wtol = 5e-07,
use_gradient = TRUE,
use_qp_solver = TRUE
)Covariance or correlation matrix (this is the only mandatory argument).
Budget vector, i.e., the risk budgeting targets. The default is the uniform 1/N vector.
Vector of expected returns (only needed if the expected return term is desired in the objective).
Scalar weight to control the importance of the expected return term.
Scalar weight to control the importance of the variance term (only currently available for the SCA method).
Lower bound (either a vector or a scalar) on the value of each portfolio weight.
Upper bound (either a vector or a scalar) on the value of each portfolio weight.
Equality constraints matrix.
Equality constraints vector.
Inequality constraints matrix.
Inequality constraints vector.
Method to compute the vanilla solution. In case of
additional constraints or objective terms, this solution is used as
the initial point for the subsequent method. The default is
"cyclical-spinu". See details below.
Method to solve the non-vanilla formulation. The default is "sca".
See details below. (DEPRECATED)
String indicating the risk concentration formulation to be used.
It must be one of: "diag", "rc-double-index",
"rc-over-b-double-index", "rc-over-var vs b",
"rc-over-var", "rc-over-sd vs b-times-sd",
"rc vs b-times-var", "rc vs theta", or
"rc-over-b vs theta". The default is "rc-over-b-double-index".
If formulation is not provided and no additional terms or
constraints are set, such as expected return or shortselling, then the
vanilla risk parity portfolio will be returned. If formulation is
"diag" then the analytical solution of the risk parity optimization for
for a diagonal covariance matrix will be returned. See details below.
Initial value for the portfolio weights. Default is a convex combination of the risk parity portfolio, the (uncorrelated) minimum variance portfolio, and the maximum return portfolio.
Initial value for theta (in case formulation uses theta). If not provided, the optimum solution for a fixed vector of portfolio weights will be used.
Learning rate for the SCA method.
Factor used to decrease the learning rate at each iteration for the SCA method.
Regularization factor.
Maximum number of iterations for the SCA loop.
Convergence tolerance on the objective function.
Convergence tolerance on the values of the portfolio weights.
This parameter is meaningful only if method is either
"alabama" or "slsqp". If TRUE (default value), analytical gradients of the
objective function will be used (strongly recommended to achieve faster results).
Whether or not to use the general QP solver from quadprog to solve each iteration of the SCA algorithm. Default is TRUE.
A list containing possibly the following elements:
wOptimal portfolio vector.
relative_risk_contributionThe relative risk contribution of every asset.
thetaOptimal value for theta (in case that it is part of the chosen formulation.)
obj_funSequence of values of the objective function at each iteration.
risk_concentrationRisk concentration term of the portfolio R(w).
mean_returnExpected return term of the portoflio t(w)%*%mu,
if the term is included in the optimization.
varianceVariance term of the portfolio t(w)%*%Sigma%*%w,
if the term is included in the optimization.
elapsed_timeElapsed time recorded at every iteration.
convergenceBoolean flag to indicate whether or not the optimization converged.
is_feasibleBoolean flag to indicate whether or not the computed portfolio respects the linear constraints.
By default, the problem considered is the vanilla risk parity portfolio:
w >= 0, sum(w) = 1, with no expected return term, and no variance term. In this case,
the problem formulation is convex and the optimal solution is guaranteed to be achieved with
a perfect risk concentration, i.e., R(w) = 0. By default, we use the formulation by
Spinu (2013) (method_init = "cyclical-spinu"), but the user can also select the formulation
by Roncalli et al. (2013) (method_init = "cyclical-roncalli").
In case of additional box constraints, expected return term, or variance term,
then the problem is nonconvex and the global optimal solution cannot be
guaranteed, just a local optimal. We use the efficient sucessive
convex approximation (SCA) method proposed in Feng & Palomar (2015),
where the user can choose among many different risk concentration
terms (through the argument formulation), namely:
formulation = "rc-double-index": sum_{i,j} (r_i - r_j)^2
formulation = "rc-vs-theta": sum_{i} (r_i - theta)^2
formulation = "rc-over-var-vs-b": sum_{i} (r_i/r - b_i)^2
formulation = "rc-over-b double-index": sum_{i,j} (r_i/b_i - r_j/b_j)^2
formulation = "rc-vs-b-times-var": sum_{i} (r_i - b_i*r)^2
formulation = "rc-over-sd vs b-times-sd": sum_{i} (r_i/sqrt(r) - b_i*sqrt(r))^2
formulation = "rc-over-b vs theta": sum_{i} (r_i/b_i - theta)^2
formulation = "rc-over-var": sum_{i} (r_i/r)^2
where r_i = w_i*(Sigma%*%w)_i is the risk contribution and
r = t(w)%*%Sigma%*%w is the overall risk (i.e., variance).
For more details, please check the vignette.
Y. Feng, and D. P. Palomar (2015). SCRIP: Successive Convex Optimization Methods for Risk Parity Portfolio Design. IEEE Trans. on Signal Processing, vol. 63, no. 19, pp. 5285-5300. <https://doi.org/10.1109/TSP.2015.2452219>
F. Spinu (2013). An Algorithm for Computing Risk Parity Weights. <https://dx.doi.org/10.2139/ssrn.2297383>
T. Griveau-Billion, J. Richard, and T. Roncalli (2013). A fast algorithm for computing High-dimensional risk parity portfolios. <https://arxiv.org/pdf/1311.4057.pdf>
# NOT RUN {
library(riskParityPortfolio)
# create covariance matrix
N <- 5
V <- matrix(rnorm(N^2), ncol = N)
Sigma <- cov(V)
# risk parity portfolio
res <- riskParityPortfolio(Sigma)
names(res)
#> [1] "w" "risk_contribution"
res$w
#> [1] 0.04142886 0.38873465 0.34916787 0.09124019 0.12942842
res$relative_risk_contribution
#> [1] 0.2 0.2 0.2 0.2 0.2
# risk budggeting portfolio
res <- riskParityPortfolio(Sigma, b = c(0.4, 0.4, 0.1, 0.05, 0.05))
res$relative_risk_contribution
#> [1] 0.40 0.40 0.10 0.05 0.05
# }
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