NEWS

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RiskPortfolios 2.1.8 (2026-08-21)

• optimalPortfolio(type = 'erc'): the equal-risk-contribution weights are now obtained exactly for the 'none' and 'lo' constraints, by solving y_i [Sigma y]_i = 1/N (Spinu, 2013), instead of minimising the squared deviations of the risk contributions with a general-purpose optimizer. The latter objective is not convex and has local minima on the boundary at which entire blocks of assets are assigned a zero weight; those were returned silently whenever the correlation matrix had sizeable negative entries.
• meanEstimation(type = 'bs'): the sample size used in the Bayes-Stein shrinkage intensity was the number of assets instead of the number of observations, so the estimator did not depend on the length of the sample and over-shrunk toward the minimum-variance mean.
• optimalPortfolio(type = 'maxdec', constraint = 'none') solved the problem with the covariance matrix instead of the correlation matrix, and therefore returned the minimum-variance portfolio.
• optimalPortfolio(type = 'mv', constraint = 'none') is now the solution of the mean-variance problem under the summability constraint. The weights used to be rescaled in a way that cancelled 'gamma' altogether and flipped the sign of the portfolio when 1'Sigma^-1 mu was negative.
• covEstimation(type = 'ewma') is now normalised: the recursion is started at zero rather than at the sample covariance matrix, which used to inflate the covariance by a factor (1 + lambda^T) -- 30% for T = 20.
• covEstimation(type = 'large') is an alias of 'lw'. The two estimators were numerically identical up to the truncation of the shrinkage intensity to [0, 1], which 'large' omitted.
• optimalPortfolio(type = 'riskeff') works for portfolios of fewer than ten assets, and tolerates ties in the semideviations.
• The convergence status returned by slsqp is now inspected instead of being discarded, and the non-convex problems are solved from several starting values.
• The default ctr.slsqp gains ftol_rel = 1e-12. The previous default set only xtol_rel = 1e-18, which is below double precision and can never be met, so every gross-constrained problem ran to maxeval and reported a non-converged status irrespective of whether it had converged. With an attainable criterion the mean-variance problem with a gross constraint on 25 assets stops after 126 iterations instead of 2000, and the solver's convergence status becomes informative. It now applies only to the non-convex problems ('erc' under bounds, 'maxdiv' and 'riskeff'), the convex ones having been moved to an exact quadratic program.
• 'gross.c' is validated (it was not, because the test was made on the wrong variable), 'lambda' is validated, 'K' required to be a positive integer, and the dimensions of 'mu' and 'semiDev' are checked against Sigma.
• optimalPortfolio(type = 'invvol') projects its weights onto 'LB'/'UB' when they bind, with a warning, instead of returning weights that violate them.
• 'LB' and 'UB' are validated whenever they are supplied, not only under the 'user' constraint: they are passed to the optimizer under 'gross' as well, and bounds incompatible with the summability constraint are now rejected.
• The starting value is projected onto the bounds, so a feasible problem such as constraint = 'gross' with LB = c(0.9, 0, 0, 0) no longer fails outright because the equally-weighted default violates them.
• The returned weights are projected onto {LB <= w <= UB, w'1 = 1} rather than clipped and rescaled, which could push a weight back outside the bounds it had just been clipped into.
• The default risk aversion is 0.89, as documented (it was 0.8773).
• covEstimation(type = 'const' / 'diag') works for a single asset. The factor estimator still requires at least three assets, as factanal() does.
• The equal-risk-contribution solver now measures convergence on its defining condition, y_i [Sigma y]_i = 1/N, relative to that target, and warns if it is not met. It previously stopped on the size of the last step, normalised by the largest component of y, and gave up after a thousand sweeps: on ill-conditioned covariance matrices -- about 1% of ordinary sample covariance matrices -- it returned risk contributions differing by several percentage points without any indication, and on the worst cases it was less accurate than the optimizer it replaced.
• The gross exposure of the returned weights is verified, and the starting value is required to respect the gross budget. A truncated optimization could otherwise hand back a portfolio violating gross.c.
• meanEstimation() and semidevEstimation() accept lambda = 1 again, the no-decay limit that gives equal weights; 2.1.8-dev had wrongly excluded it for all three estimators, although only the ewma covariance divides by 1 - lambda^T.
• The returns matrix is validated: non-finite entries used to propagate silently into an all-NA covariance matrix and on into the optimizers. optimalPortfolio() likewise requires finite Sigma, mu and semiDev, and invvol requires strictly positive variances instead of returning NaN.
• 'gamma' must be a single positive number, as documented.
• Documentation: the risk-efficient portfolio's stabilizing bounds of 1/(2N) and 2/N are now documented, including the fact that they apply under every constraint and make gross.c inoperative for that portfolio; the invertibility of the shrinkage estimators is no longer overstated; the ewma formula and its prose agree; the plain-text rendering of the risk-efficient objective divides rather than multiplies; the JOSS reference is corrected to 2(10), 171.
• Every supplied bound now enters the optimization. 'LB'/'UB' passed together with constraint = 'lo' or 'none' used to be left out of the quadratic program and applied afterwards by projecting its solution onto the box, which restores feasibility but does not minimise the objective: minimum variance on diag(1, 4, 9) with UB = c(.4, .8, .8) returned variance 1.210779 where the bounded program gives 1.156923.
• The mean-variance, minimum-variance and maximum-decorrelation portfolios under the 'gross' constraint are now solved exactly as quadratic programs, by splitting w into positive and negative parts so that the gross budget becomes linear. They were handed to a nonlinear optimizer that had no attainable stopping criterion on them, and stopped up to 1e-6 short of the optimum after exhausting its evaluation budget.
• Sigma is required to be positive semidefinite. An indefinite matrix used to be accepted, so that 'minimum variance' was reported for a problem whose objective is unbounded below.
• The maximum-diversification portfolio is refused when Sigma is singular and nothing bounds the weights: the diversification ratio has no maximum there, and the optimizer used to return weights of order 1e5 without saying so.
• covEstimation(type = 'lw' / 'large') reports the degenerate case in which the equally-weighted market factor has zero variance, instead of returning NaN off-diagonal entries. covEstimation(type = 'const' / 'cor') likewise reports assets with zero variance rather than returning NA/NaN.
• The Bayes-Stein estimators state their T > N requirement and report a singular sample covariance matrix in their own terms instead of passing on a LAPACK message.
• The ewma semideviation weights are computed on the log scale, rebased on the newest selected observation: lambda^T underflowed to zero on a long sample and the selected weights then normalised to 0/0.
• 'semiDev' must be non-negative.
• CITATION uses bibentry(); MASS removed from Imports; documentation fixes.

RiskPortfolios 2.1.7 (2021-05-16)

• Doc fixes

RiskPortfolios 2.1.6

• Update references

RiskPortfolios 2.1.5 (2021-04-21)

• Update references
• DOI fixed

RiskPortfolios 2.1.4 (2020-04-20)

• Fix description and citation

RiskPortfolios 2.1.3 (2020-04-19)

• Fix description and citation

RiskPortfolios 2.1.2 (2018-08-30)

• References updated
• Gradient added
• Max decorrelation portfolio added

RiskPortfolios 2.1.0

• CRAN release
• documentation updated
• THANKS file added

RiskPortfolios 2.00.11

• Documentation improved and finalized

RiskPortfolios 2.00.10

• LB and UB added properly
• doc modified

RiskPortfolios 2.00.09

• dataset added
• doc RiskPortfolios added

RiskPortfolios 2.00.08

• tests added
• doc fixed
• positivity fixed

RiskPortfolios 2.00.07

• Roxygen documentation
• testthat added

RiskPortfolios 2.00.06

• update CITATION

RiskPortfolios 2.00.05

• update informations and references

RiskPortfolios 2.00.04

• serveral error fixes
• new Imports instead of Depends

RiskPortfolios 2.00.03

• erc initialized at 1/sigma normlized

RiskPortfolios 2.00.02

• w0 starting value can now be passed in controls
• references updated

RiskPortfolios 2.00.01

• Renaming of the package with emphasis of risk-based portfolios
• Added inverse volatility portfolio

RiskPortfolios 1.02.02

• DESCRIPTION file adapted to new standard

RiskPortfolios 1.02.01

• major revision of the package

RiskPortfolios 1.01.02

• bug fixd thanks to Samo Pahor

RiskPortfolios 1.01.01

• package's name
• package's version

RiskPortfolios 1-00.01

• first release