rmgarch (version 1.2-9)

rmgarch-package: The rmgarch package

Description

The rmgarch provides a selection of multivariate GARCH models with methods for fitting, filtering, forecasting and simulation with additional support functions for working with the returned objects. At present, the Generalized Orthogonal GARCH using Independent Components Analysis (ICA) and Dynamic Conditional Correlation (with multivariate Normal, Laplace and Student distributions) models are fully implemented, with methods for spec, fit, filter, forecast, simulation, and rolling estimation and forecasting, as well as specialized functions to calculate and work with the weighted portfolio conditional density. The Copula-GARCH model is also implemented with the multivariate Normal and Student distributions, with dynamic (DCC) and static estimation of the correlation.

How to cite this package

Whenever using this package, please cite as @Manual{Ghalanos_2014, author = {Alexios Ghalanos}, title = {{rmgarch}: Multivariate GARCH models.}, year = {2014}, note = {R package version 1.2-8.}}

License

The releases of this package is licensed under GPL version 3.

Details

ll{ Package: rmgarch Type: Package Version: 1.2-8 Date: 2014-06-11 License: GPL LazyLoad: yes Depends: R (>= 3.0.2), methods, rugarch LinkingTo: Rcpp (>= 0.10.6), RcppArmadillo (>= 0.2.34) Imports: Rsolnp, MASS, parallel, Matrix, zoo, xts, Bessel, ff, shape, pcaPP, spd, Rcpp Description: Feasible multivariate GARCH models including DCC, GO-GARCH and Copula-GARCH. } The main package functionality, currently supports the GO-GARCH with ICA method, and is available through the gogarchspec, gogarchfit, gogarchfilter, gogarchforecast, gogarchsim and gogarchroll functions. The DCC with multivariate Normal, Laplace and Student distributions is also supported with the main functionality in dccspec, dccfit, dccfilter, dccforecast, dccsim and dccroll. The Normal and Student Copula-GARCH, with dynamic or static correlation, is implemented with the main functionality in cgarchspec, cgarchfit, cgarchfilter, and cgarchsim. Usual extractor and support methods for the multivariate GARCH models are documented in the class of the returned objects. .

References

Bollerslev, T. 1990, Modelling the coherence in short-run nominal exchange rates: a multivariate generalized ARCH model, The Review of Economics and Statistics, 72(3), 498--505. Broda, S.A. and Paolella, M.S. 2009, CHICAGO: A Fast and Accurate Method for Portfolio Risk Calculation, Journal of Financial Econometrics 7(4), 412--436 . Cappiello, L., Engle, R.F. and Sheppard, K. 2006, Asymmetric dynamics in the correlations of global equity and bond returns, Journal of Financial Econometrics 4, 537--572. Croux, C. and Joossens, K. 2008, Robust estimation of the vector autoregressive model by a least trimmed squares procedure, COMPSTAT, 489--501. Chen, Y., Hardle, W., and Spokoiny, V. 2010, GHICA - Risk analysis with GH distributions and independent components, Journal of Empirical Finance, 17(2), 255--269. de Athayde, G.M. and Flores Jr, R.G. 2002, On Certain Geometric Aspects of Portfolio Optimisation with Higher Moments, mimeo. Engle, R.F. 2002, Dynamic conditional correlation, Journal of Business and Economic Statistics 20, 339--350. Engle, R.F. and Sheppard, K. 2001, Theoretical and empirical properties of dynamic conditional correlation multivariate GARCH, NBER Working Paper. Genest, C., Ghoudi, K. and Rivest, L. 1995, A semiparametric estimation procedure of dependence parameters in multivariate families of distributions, Biometrika, 82, 543--552. Ghalanos, A., Rossi, E., and Urga, G. (2014). Independent Factor Autoregressive Conditional Density Model, Econometric Reviews. Paolella, M.S. 2007, Intermediate Probability - A Computational Approach, Wiley-Interscience. Schmidt, R., Hrycej, T. and Stutzle 2006, Multivariate distribution models with generalized hyperbolic margins, Computational Statistics & Data Analysis 50(8), 2065--2096.