# 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.