Test-only release. The CRAN check on
r-release-macos-arm64 failed with
Error in quadprog::solve.QP(...): constraints are inconsistent,
no solution!
[ FAIL 1 | WARN 0 | SKIP 0 | PASS 1221 ]in test-efficient-frontier.R. No user-facing code is
affected; the package functions are unchanged.
The cause is the target grid, which ended at exactly
max(mu). The only long-only portfolio attaining that return
is the vertex e_argmax, a degenerate corner of the feasible
set where quadprog::solve.QP may report inconsistent
constraints instead of returning the vertex. Whether it does depends on
the platform’s R build: installing R 4.6.1 for aarch64-apple-darwin23
reproduces CRAN’s report byte for byte – same test, same line, same
message, same PASS 1221 – on a machine whose R 4.6.1 build
fails and whose R 4.5.2 (darwin20) build passes, under one operating
system. Instrumenting that reproduction shows solve.QP
refuses exactly one of the twenty grid points: i = 20, the
vertex. Every interior target solves.
A second, independent fragility sat behind it:
mu <- runif(5, .002, .006) was drawn without seeding,
because the helper psd_cov() calls set.seed()
inside itself and so did not cover that line. That did not
cause this failure – the stream happened to be identical on both
machines – but it made the input to a known-fragile construction
unpredictable, and roughly half of all seeds produce a mu
that fails the same way locally.
The maximum-return end point is no longer sent to
solve.QP at all; it is asserted against its analytic value,
the vertex, which is a stronger claim than the one solve.QP
was being asked to satisfy and one the package meets exactly through
pracma::quadprog.
The interior targets still go to the independent solver, but wrapped
rather than bare. The reason is worth stating: the failure could not be
reproduced on the machine fixing it, and neither CRAN nor the M1mac
service reports which grid point failed. Locally only the vertex ever
fails, but excluding only the vertex would be a guess. Whether
solve.QP can solve a given point is a property of the
platform’s linear algebra rather than of the package, so a point it
declines is now skipped, the points it solves must still agree, and a
guard requires at least 18 of the 20 to have been solved so that a
collapsed solver cannot masquerade as a pass. The draw is seeded as
well.
Every other unseeded draw in the suite is seeded too: eleven blocks
across test-efficient-frontier.R,
test-mvsk-portfolio.R and
test-tail-dependence.R. The suite now returns 1228 passing
assertions and no failures under every initial RNG state tried, and each
file gives the same result run alone as in suite order.
f_normal_copula_pdf() and
f_student_copula_pdf() returned NaN or
Inf for valid inputs in moderately high dimension. Both
formed the density as exp(log_num) / prod(fs): the
numerator was computed in log space, but exponentiating it before
dividing by the product of marginal densities reintroduced the range
problem from the other side. On an identity Sigma, where
the Gaussian copula density is exactly 1 everywhere, the function
returned 1 up to d = 200 and NaN from
d = 300, because numerator and denominator both underflowed
to zero. The whole ratio is now formed in log space and exponentiated
once. Bivariate results are unchanged to 1.8e-15 relative, so nothing
that used these functions in two dimensions moves.
f_tail_dependence() failed with the internal message
“missing value where TRUE/FALSE needed” when alpha was
NA_real_ or NaN, because those are numeric
scalars and reached the range comparison. alpha is now
checked for finiteness first and the documented error message is
raised.
f_normal_copula_pdf() and
f_student_copula_pdf() gain a log argument. In
high dimension the density itself can exceed the double-precision range
even when its logarithm is unremarkable – the Student copula at
u = rep(0.99, 500) has a log density of 937 against an
exp() ceiling of 710 – so log = TRUE is the
safe form for likelihoods.The Fred help page contradicted itself. The Format
section correctly described DJI.Adjusted as a
one-month-ahead change in index points, while the title, description and
Details still called it a monthly log-return. The data settle it: the
column has a standard deviation of 780 and ranges from -2211 to +1785.
All three “log-return” statements are removed.
The FungHsieh help page now states plainly what its
provenance can and cannot establish. The original download URLs and
vintages were not recorded, so data-raw/FungHsieh.R is a
contract harness rather than a reconstruction script, and the column
semantics are inferred rather than verified. Where the data corroborate
an identification it is now said so explicitly – BAA
correlates -0.51 with CST10Y and -0.27 with
EMKT, the signature of a credit spread change rather than a
raw Baa yield change – and where they do not, that is said too.
MASS and mvtnorm are declared in
Suggests. Both are used by the test suite via
::, and R CMD check --as-cran reported “‘::’
imports not declared from: ‘MASS’ ‘mvtnorm’”. The check is now free of
warnings.
New provenance and contract harnesses
data-raw/FamaFrench.R and
data-raw/TermStructure.R, the last two of the twenty
shipped datasets without one. The FamaFrench harness pins the
end-of-week stamping convention, which is the property a naive re-import
would silently shift by one week; the TermStructure harness pins the
tau attribute and the maturity ordering, both of which are
lost silently rather than loudly by a round trip through
as.matrix().
Regression tests for the copula densities at d =
100, 200, 300 and 500, and for non-finite alpha in
f_tail_dependence().
The mvskPortfolios source repository moved from
cdries/mvskPortfolios to c3jg/mvskPortfolios;
the attribution links in f_mvsk_portfolio() follow it.
R CMD check --as-cran flagged the redirect.
The test suite is roughly three times faster, 23.5s to 6.9s
locally. One helper in test-copula-reference.R accounted
for 85% of the runtime: a midpoint-rule integration on a 220 x 220 grid,
called twelve times, i.e. 580,800 scalar density evaluations each
re-running its own input validation. The grid drops to 70 x 70, which
the O(1/n^2) convergence pays for in accuracy the tolerances never
needed – so those are tightened at the same time, from 0.05 to between
0.002 and 0.03, calibrated at four times the worst error observed over
every parameter value the test uses. The checks are now both faster and
stricter: at the old tolerance a 3% error in a normalizing constant
would have passed.
New function f_mdp(), the most diversified portfolio
of Choueifaty and Coignard (2008). It solves the closed form
w proportional to Sigma^{-1} sigma and refuses
a singular Sigma rather than returning a meaningless answer
– which is what Exercise 4.2 of the book relies on, since the whole
point of that exercise is that the sample covariance of 487 constituents
estimated from 156 weekly observations cannot be inverted. The weights
are unconstrained in sign; the help page documents the gross exposure
this implies.
f_efficient_frontier() gains a targets
argument. Frontiers computed from different inputs otherwise use
different target-return grids, so averaging them point by point mixes
portfolios that target different returns. Supplying an explicit grid
makes several frontiers directly comparable. Targets above the maximum
attainable return are reported with status "failed" rather
than silently clipped.
GoyalWelch: the help page now states that
Rfree is exactly tbl / 12. The two columns
carry the same information, so at most one may enter a regression –
lm() detects the resulting rank deficiency and aliases one
away, but penalized fitters such as glmnet::glmnet() do
not, and split the effect arbitrarily between them. The units of
tbl, AAA, BAA and
lty are stated as annualized decimals, and csp
is identified as the Polk-Thompson-Vuolteenaho cross-sectional premium
(available to Dec 2002 only, hence the 192 missing values) rather than
the corporate bond return spread, which is
corpr - ltr.
Fred: the response column DJI.Adjusted
is documented as a one-month-ahead change in the index level,
in points, not as a log-return. The predictors are documented as
transformed but not rescaled – their standard
deviations span some eight orders of magnitude – so any penalized fit
must standardize internally.
Contract tests added for GoyalWelch (shape, column
order, the Rfree == tbl / 12 identity, the missing-value
structure and anchored values), for SP500_const, which had
none, and for the two DAX slices that carry every numerical
result of Chapter 5.
DJ and DAX are now anchored
numerically. Shape, dates and positivity were checked before, but any
other positive series of the same dimensions would have passed
them.
Integration tests for the resampled-efficiency pipeline: that an explicit target grid makes two frontiers comparable point by point, that infeasible targets are reported rather than clipped, and that averaging by rank preserves the budget and the long-only constraint while giving up efficiency.
New provenance harnesses data-raw/GoyalWelch.R and
data-raw/Fred.R, which assert every structural property the
book and the help pages depend on.
New function f_mvsk_portfolio(), a port of
mvskPortfolio() from the mvskPortfolios
package by Dries Cornilly and Kris Boudt, redistributed under the same
GPL (>= 2) licence and with both authors added as contributors and
copyright holders. Chapter 3 of the book previously required installing
that package from GitHub; it now installs from CRAN alone. The algorithm
is unchanged and reproduces the original bit for bit, but the internal
calls to non-exported PerformanceAnalytics symbols have
been replaced by this package’s own helpers, the solver status is
returned, and the inputs are validated. Co-moment matrices must be
supplied in the matrix form (as.mat = TRUE).
f_clayton_copula_2d_pdf() gains a log
argument. The Clayton density diverges towards the lower corner, where
only the log scale is representable; the function previously returned a
silent zero there.
f_normal_copula_pdf() and
f_student_copula_pdf() now validate Sigma. An
asymmetric matrix was silently accepted and returned a plausible number
corresponding to no valid copula; a singular one returned
Inf and an indefinite one NaN. All three are
now errors, and the message points at
Matrix::nearPD().
Both elliptical copula densities compute the determinant and the
normalizing constant on the log scale. det(Sigma)
underflows for large dimensions – it is already 9e-30 at N = 200 – which
turned the density into Inf. The densities are now finite
well past N = 800.
f_ptf_max_U() checks that
w_max >= 1/d before optimizing. Bounds that cannot fund
a full investment previously produced an opaque nloptr error (“at least
one element in x0 > ub”) for arguments the documentation allowed. It
also validates that all four moment inputs are finite and that
M2 is symmetric and positive semidefinite.
f_ptf_max_U() now warns on NLopt status 5 and 6.
These are positive codes, so the previous status < 0
test treated an exhausted evaluation budget as a success.
f_tail_dependence() returns NA with a
warning when the threshold selects no observation, instead of
0. Two identical constant series were reported as having no
tail dependence.
f_display_copula() rejects grids touching 0 or 1.
The densities are defined on the open square, and a boundary grid failed
inside the user’s callback with a message that pointed at the wrong
place.
?FTSE_const now describes the real structure of its
missing values: 4,634 of the 163,821 NAs occur after a
series has started, across 96 of the 98 columns, so trimming the leading
block is not sufficient. This was the last of the three
*_const datasets to carry the incorrect claim.New test-copula-reference.R cross-checks all four
copula densities against copula::dCopula() across a wide
parameter range, verifies that each integrates to one over the unit
square, and covers the invalid-matrix paths. Nothing in the suite
previously pinned the numbers themselves.
New test-portfolio-moments.R covers
f_portfolio_moments(), which had no tests at all, and
checks the analytic gradients of the MVSK objective against finite
differences – f_ptf_max_U() passes them to nloptr with
check_derivatives = FALSE.
New test-mvsk-portfolio.R covers the ported tilting
routine.
Dataset tests extended to the nine single-series indices
(DJ, FTSE, EURSTOXX,
DAX, CAC, NIKKEI,
SMI, HSI, GOLD), none of which
were loaded by any test, and to the weekly merge that Chapter 3 depends
on.
New data-raw/qrmdata-ports.R records the provenance
of the fifteen ported market datasets and pins their contract.
f_efficient_frontier() now computes the
maximum-return end point by solving the constrained QP (minimize
variance subject to mu'w = max(mu)) instead of assigning
weights by hand. Previously, when several assets tied for the highest
expected return, weight was spread equally across them. That allocation
is feasible but generally not variance-minimal, so the last frontier
point could be strictly dominated: with mu = c(.1, .2, .2)
and Sigma = diag(c(1, 100, 1)) it returned
c(0, .5, .5) (variance 25.25) where the optimum is
c(0, 1/101, 100/101) (variance 0.99). Frontiers with a
unique maximum are unaffected.
When an interior QP fails to converge,
f_efficient_frontier() no longer carries the previous
portfolio’s weights forward. Doing so returned a point that silently
violated its own target return. Failed points are now NA
and are flagged in the new status component.
f_efficient_frontier() gains a status
component ("ok" / "failed") and the
frontier data frame gains target and
status columns, so a caller can check that every point met
its target return.
f_efficient_frontier() accepts any
Sigma coercible with as.matrix(), notably the
dpoMatrix returned by Matrix::nearPD()$mat,
which previously raised an error.
f_efficient_frontier() validates that
Sigma is positive semidefinite and rejects it with an
actionable message rather than relying on a warning from the solver.
Asymmetric input warns and uses the symmetric part.
volatility and expected_returns are now
named, and the columns of weights labelled, consistently
across the returned object.
New ?extdata help page for the raw files shipped
under inst/extdata (stocks.txt,
stocks.csv, stocks.xlsx,
prices.txt), which were installed but undocumented. It
records the weekly timestamp convention: the price stamped against a
Tuesday is the close at the end of that calendar week.
?FamaFrench now states that the index is stamped at
the end of the week and warns that merging it with a week-start-stamped
series and filling forward lags the factors by one week. It also notes
that subsetting with endpoints(x, "months") selects the
last weekly return of each month rather than compounding a monthly
return.
?FungHsieh now fixes the units definitively: all
eight columns are in percentage points, with CST10Y and
BAA being changes in yields rather than returns. The
previous text left the choice between percentage points and basis points
open, which matters when these factors are merged with the decimal
edhec series. A maintainer note has been removed from the
help page, and data-raw/FungHsieh.R now records the
assembly steps and pins the dataset’s contract.
?DJ_const and ?EURSTX_const now
describe the real structure of their missing values. Both previously
claimed that NAs occur only before a constituent’s first
observation; in fact DJ_const has 25 interior
NAs and EURSTX_const has 1,938, spread over
all 50 of its columns (mostly local trading holidays).
Dataset tests extended to DJ_const,
EURSTX_const, VIX, SP500 and the
extdata files, none of which were previously loaded by the
suite, and tightened for FamaFrench and
FungHsieh to cover index class, exact date range, ordering,
NA structure and units.
f_efficient_frontier() is now cross-checked against
an independent quadprog::solve.QP construction, with tests
for target attainment, budget and long-only constraints, monotonicity,
non-domination, tie handling and input validation.
DESCRIPTION, the package-level help,
README.md, and inst/CITATION.Fred dataset documentation to describe
the consolidated xts (128 predictor columns plus the
DJI.Adjusted response column) rather than the former
list(X, y) structure.SP500_const: weekly adjusted close prices
for the 505 S&P 500 constituents (2015 membership, 1962–2015),
ported from qrmdata and thinned to weekly frequency to
respect the CRAN size limit. Supports the high-dimensional covariance /
factor-model exercise where the number of constituents exceeds the
number of observations.xts objects for a consistent
interface. Fred is now a single xts (128
predictors plus the DJI.Adjusted response column) instead
of a list(X, y); TermStructure is now the
rates xts with the maturity grid stored in
xts::xtsAttributes(TermStructure)$tau instead of a
list(time, tau, rates).FungHsieh, GoyalWelch,
Fred) are now indexed by zoo::yearmon instead
of a first-of-month Date, so they merge by calendar month
without first-/end-of-month ambiguity. To combine them with a
Date-indexed series, coerce its index with
as.yearmon().FamaFrenchWeekly is renamed to FamaFrench
(still weekly, Date-indexed). The unused
FamaFrenchMonthly dataset has been removed.xts::as.xts so the xts dependency
is registered in the NAMESPACE (resolves the “package in Depends not
imported from” note).f_efficient_frontier,
f_ptf_max_U, f_portfolio_moments.f_normal_copula_pdf,
f_student_copula_pdf, f_clayton_copula_2d_pdf,
f_gumbel_copula_2d_pdf,
f_gumbel_copula_2d_cdf.f_display_copula.f_tail_dependence.FamaFrenchMonthly,
FamaFrenchWeekly, Fred,
FungHsieh, GoyalWelch,
TermStructure.SP500, DJ,
DJ_const, FTSE, FTSE_const,
EURSTOXX, EURSTX_const, DAX,
CAC, NIKKEI, SMI,
HSI, GOLD, VIX.
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