tsgarch 1.0.5

• Added a jointly estimated ARMA(p,q)-X mean equation, via new arma, xreg and xreg_type arguments to garch_modelspec (defaulting to c(0,0) and NULL respectively, so fully backward compatible). Available for all 8 GARCH flavors, including igarch and ewma. The constant argument remains independent of both the ARMA order and the regressors, and continues to control only whether the unconditional mean mu is estimated or fixed at zero.
• Regressors enter the conditional mean through xreg (an xts matrix aligned to y), with xreg_type selecting between two conventions: xreg_type = "arma_errors" (the default) runs the ARMA recursion on y_t - mu - x_t'tau, so tau is the long-run marginal effect - this matches stats::arima’s xreg semantics and the sibling tsarma package; xreg_type = "armax" (the rugarch convention) adds x_t'tau to the conditional mean at time t only, so tau is the impact effect and the long-run effect is tau/(1 - sum(phi)). The two are algebraically identical whenever the AR order is zero. Both are restrictions of the same distributed lag model and neither nests the other; see the “ARMA Mean Equation” section of vignettes/garch_models.Rmd for guidance on choosing between them. The coefficients are named tau1, tau2, … in the parmatrix (xi is already the variance-regressor coefficient in this package; note that tsarma calls its mean-equation coefficients xi, which is tsgarch’s tau). An xreg containing NA/NaN/Inf values, or which is rank deficient (including a constant column when constant = TRUE), is rejected at specification time.
• Mean regressors are supported throughout the model lifecycle: estimate, tsfilter (via newxreg), predict (via newxreg, and both parametric and bootstrap predictive distributions), simulate (via xreg, a raw matrix with h + burn rows - unlike the legacy vreg argument which is a pre-multiplied vector) and garch_backtest (sliced per rolling window). When the model includes xreg but the future regressors are not supplied, a zero matrix is substituted with a warning; the variance-regressor argument newvreg in predict() deliberately retains its existing hard-error behaviour. Note that future regressors are treated as a single deterministic path shared across all simulated paths, so regressor uncertainty is not propagated into the predictive distribution.
• The AR/MA coefficients are guaranteed stationary/invertible by construction, via a Durbin-Levinson/Jones partial-autocorrelation (PACF) reparameterization of the raw optimization parameters, so no additional nonlinear constraints are required; exact (autodiff, not finite-difference) Jacobians of the ARMA mean equation are available throughout via TMB. The transformed AR/MA coefficients can be extracted with the new arma_coefficients() function.
• fitted() now returns the genuinely time-varying conditional mean whenever the mean equation has ARMA dynamics or regressors (previously always a constant mu, now completing the documented “vector the size of y” contract), and residuals() is now always exactly y - fitted(object) for every model, including a fix for models with no ARMA (a small, purely internal simplification with no behavior change there). A pure regression with GARCH errors (arma = c(0,0) with xreg) therefore reports the regression fit rather than a constant.
• predict(), simulate(), tsfilter(), garch_backtest() and tsprofile() all account for the full ARMA-X mean equation: point forecasts, both the parametric and bootstrap predictive distributions, simulated paths, incremental filtering, each rolling window’s refit and the simulate/re-estimate profile all reflect the AR/MA dynamics and the regressor contribution. Variance targeting uses the unconditional variance of the ARMA innovations rather than deviations from the constant mu, so the R side unconditional()/target_omega agree with the TMB side. Mean regressors are refused by tsprofile(), as vreg already was, since its simulation step does not carry them into the simulated sample. The combined pre-sample/burn-in length used internally is max(garch order, arma order).
• summary() (and its console print()/as_flextable() methods) now displays the transformed ar/ma coefficients with their delta-method standard errors, rather than the raw (not directly interpretable) Durbin-Levinson parameters used internally during estimation.
• An entire AR and/or MA polynomial in the ARMA mean equation can now be fixed at target coefficients rather than estimated: set value to the desired ar/ma coefficients (not the internal pacf parameterization) and estimate = 0 on every arpacf#/mapacf# row of the relevant group in the spec’s parmatrix; the correct internal Durbin-Levinson transform is then applied automatically wherever needed (estimation, arma_coefficients(), predict(), simulate(), tsfilter()), and the fixed coefficients are validated for stationarity/invertibility up front. Fixing only some (not all) of the lags of a given polynomial is not supported, since the reparameterization couples all of a polynomial’s lags together, and raises an informative error, as does mixing estimate values within one group.
• Added a new ARMA diagnostic panel to plot.tsgarch.estimate() (plot(object, type = "arma")), for models with a non-zero arma order. The panel shows: inverse AR/MA roots against the unit circle (with visual flags for near common-factor cancellation), the impulse response function (plotted from lag 1, since psi_0 = 1 identically and carries no information about the fitted model; the optional cumulative line is accumulated from lag 1 as well, so both stay on the same scale), and the ACFs of the standardized residuals z_t and z_t^2. All four panels are shown by default (which = NULL); individual panels can be selected via which. The residual ACF panels support three envelopes: the default asymptotic "bartlett" band, a "simulate" band from the fitted innovation distribution at the point parameter estimates (no parameter uncertainty), and a "parametric" band that perturbs the full parameter vector using vcov() and re-filters the observed data to reflect parameter estimation uncertainty. type = "garch" (the original volatility/news-impact/QQ panel) remains the default and is unchanged. New computational helpers arma_inverse_roots(), arma_irf() and arma_near_cancellation() are exported for programmatic use.
• tsequation(), and the as_flextable summary footer built from it, now render the conditional mean equation eq_mean first in the equation block, covering the constant, the ARMA terms and the mean regressors under both xreg_type conventions.
• Fixed tsbacktest() silently refitting the wrong model inside its rolling windows: the inner garch_modelspec() call passed no model, so e.g. an egarch spec was backtested as a vanilla garch. The spec now records the user-facing model name in model$model_name, necessary because ewma is coerced to igarch internally, and each window is refit with it.
• Fixed spec re-specification dropping the ewma restriction. ewma is coerced to igarch internally, the difference being only the fixed omega row of the parmatrix, and both .spec2newspec() and garch_profile() rebuilt specs from the coerced name. A filtered ewma model therefore carried a spurious free omega, leaving npars and AIC/BIC out by one parameter’s worth, and garch_profile() re-fit an igarch model. Filtered sigma is unchanged; filtered ewma AIC/BIC shift accordingly.
• simulate() now raises an error instead of silently returning zeros when the implied initial variance is not positive and finite. For an ewma specification omega is fixed at zero, so the seed was zero and the variance recursion stayed there at every step, returning sigma identically zero and a series equal to mu. Supply var_init for such models; predict() always did, and every other flavor has a positive implied seed and is unaffected.
• simulate() on a spec taken from an estimated object (mod$spec, which carries parmatrix = NULL) no longer aborts the R session, raising a clean error asking for the estimated parmatrix to be assigned onto the spec first. A specification serialized by a version up to 1.0.4 predates the arpacf, mapacf and tau rows of the parmatrix and is likewise refused with a message naming garch_modelspec() as the remedy, rather than failing with Error when reading the variable: 'arpacf'.
• Fixed tsfilter()’s zero fill for a missing newvreg being built with as.matrix(0, ...), a 1x1 matrix, instead of matrix(0, nrow, ncol).
• Corrected the pre-sample initialization of the ARCH recursion for the asymmetric flavors (egarch, gjrgarch, aparch, fgarch). Whether a lag lookback falls inside the pre-sample was tested against the ARCH order rather than against the pre-sample length, and the two coincide only when the ARCH order is the larger. Observations just past the pre-sample therefore took a zeroed residual in place of the initial ARCH value, in the likelihood and in simulate() alike; both now test the pre-sample length. This is reachable through a GARCH order with q > p, so logLik and the coefficients shift for these four flavors in that case. order = c(1,1) is arithmetically unchanged, and garch, igarch and ewma are unaffected at every order, their pre-sample ARCH input being constant.
• Corrected the pre-sample initialization of simulate(), which now reproduces a fitted model’s sigma to machine precision for every flavor, where the discrepancy previously reached 5e-2. var_init and innov_init are documented as seeding every sample path identically, but were filled column-major for several flavors, rotating them across the paths instead of broadcasting them; the lag-indexed ARCH initialization was paired with the oldest pre-sample period rather than the most recent, mismatching each lag with its coefficient; aparch and fgarch recycled their per-lag gamma (and fgarch its eta) across paths rather than across lags; and an arch_initial shorter than the pre-sample was collapsed onto its first element rather than padded, putting the lag 1 value into every lag. These are observable with more than one sample path, or a pre-sample longer than one period.
• Corrected the default ARCH initialization of a simulation, which is the expectation of each flavor’s ARCH equation. egarch instead evaluated its equation at the zeroed pre-sample innovations literally, contributing -gamma_j kappa where both the expectation and the likelihood give zero, which biased the opening steps downwards. aparch raised its already power-transformed initial variance to delta/2 a second time, the identity only at delta = 2, so a default simulation opened away from its own unconditional level by 3 to 10 percent for any other delta. A default simulation now opens exactly at the unconditional level for every flavor that has one.
• A pre-sample innovation supplied to an egarch simulation through innov_init now carries its leverage effect; only its magnitude was used, the alpha_j z_{t-j} term of the ARCH equation being absent from the pre-sample. The egarch predictive distribution and the simulation-approximated higher order forecast both move, and at a horizon of one the mean of the simulated distribution now agrees with the closed form forecast to machine precision, where it was out by around 3 percent.
• tsbacktest() no longer warns that its iterations drew random numbers without declaring a seed. They do draw them, through predict(), so they are now given parallel-safe streams.
• A standard error that is legitimately undefined is now reported as NaN without the low-level warning behind it. When a parameter is estimated at one of its bounds, or for ewma, whose unit persistence constraint leaves its two coefficients perfectly dependent, the Hessian has no positive definite direction there and the delta method variance is negative. kkt1/kkt2 in the estimated object’s conditions remain the signal that the solution sits on a bound.
• Documented the matrix form of var_init accepted by simulate() for a cgarch model: a max(order, arma) by 2 matrix whose first column initializes the permanent (long run) component and whose second column initializes the total conditional variance.
• Fixed the build against Eigen 5.0, which removed the free elementwise pow(ArrayBase, scalar) for non-arithmetic scalar types such as TMB’s CppAD::AD<double>. The ARCH initialization of the aparch and fgarch flavors in src/TMB/garchextra.h now raises to the power delta through a small pow_cwise() helper built on unaryExpr, which compiles under both Eigen 3.4 and 5.0 and leaves the results unchanged. Thanks to Dirk Eddelbuettel for the fix (PR #9).

tsgarch 1.0.4

• Now returning the series name of the data in the spec object for use in multivariate models.
• If the hessian cannot be calculated in the first or second step for the scaling, then a numerical approximation is used instead. There seems to be some instability in the egarch-nig model for some datasets.
• Added R (>= 4.1.0) requirement for use of |> pipe.
• Added additional documentation on expected index for y which must be Date or POSIXct (not yearmon or yearqtr).
• Replaced Rf_error with (Rf_error) per Rcpp team instructions.
• Fixed the macOS C++20/TMB build issue by redefining INFINITY as a double infinity before including TMB.hpp in src/TMB/tsgarch_TMBExports.cpp.
• Made test fixtures lazy in tests/testthat/helper-global.R with delayedAssign(), so expensive model estimates are only computed when needed.
• Reduced the heavy vignette simulation workload in vignettes/demonstration.Rmd from 500 x 10000 to 100 x 1000.
• Cleaned 1:nrow(…) usage in edited files by switching to seq_len(nrow(…)).

tsgarch 1.0.3

• Added the log-likelihood vector to the returned fitted and filtered object as this will be needed in the calculation of the standard errors in the upcoming multivariate GARCH package.
• Added an extra option to the estimation method which adds the TMB object to the returned estimation object. This can then be used to directly vary the parameters and quickly extract information from the filtration. This could be also achieved by the tsfilter method with a specification object but is much slower. Use case is for the multivariate GARCH partitioned hessian calculation.
• Added plus overload method to combine together GARCH specifications to generate a multi-specification object which can then be estimated in parallel. This is required for 2-stage multivariate GARCH models. A separate to_multi_estimate function is also added to instead convert a list of estimated objects to a validated multi_estimate class. Extractors include fitted, residuals and sigma.
• Removed RcppArmadillo dependency and converted code to RcppEigen since it is already in use by TMB.
• Switched to using simulate for the parametric simulation for the predict method. The bootstrap still remains the most valid approach as the out of sample distribution is best approximated by the bootstrapped residuals rather than the imposed parametric distribution with estimated parameters.
• For the bootstrap simulated prediction, the re-sampled standardized innovations are now scaled to avoid bias.
• Fix to h=1 and nsim. Previously when h=1, nsim was set to zero.

tsgarch 1.0.2

• Moved a unit tests back to original folder and added a tolerance to the expectation per CRAN maintainers directions.

tsgarch 1.0.1

• Moved a couple of unit tests to other folder to avoid checking on CRAN since the M1 mac had different rounding errors than other architectures for simulation tests.

tsgarch 1.0.0

• Initial CRAN submission.
• Changes to initialization of recursion in the ARCH equation to be more consistent with the literature. This leads to a more than doubling in the accuracy against the FCP benchmark.
• Correction to EGARCH forecast to account for log bias.
• Added a couple more data series for benchmarking.
• Added a pdf vignette.
• Added demo html vignettes.
• Added extensive unit tests.

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