没有合适的资源?快使用搜索试试~ 我知道了~
“rugarch”包用于在险价值的回测,是金融行业时间序列分析中常用的一个方法,能够有效刻画金融机构面临的风险敞口。
资源推荐
资源详情
资源评论
Package ‘rugarch’
February 8, 2020
Type Package
Title Univariate GARCH Models
Version 1.4-2
Date 2020-02-06
Depends R (>= 3.5.0), methods, parallel
LinkingTo Rcpp (>= 0.10.6), RcppArmadillo (>= 0.2.34)
Imports Rsolnp, nloptr, ks, numDeriv, spd, xts, zoo, chron,
SkewHyperbolic, expm, Rcpp,graphics, stats, grDevices, utils
Description ARFIMA, in-mean, external regressors and various GARCH flavors, with meth-
ods for fit, forecast, simulation, inference and plotting.
Collate rugarch-imports.R rugarch-cwrappers.R rugarch-solvers.R
rugarch-lossfn.R rugarch-distributions.R rugarch-kappa.R
rugarch-helperfn.R rugarch-numderiv.R rugarch-series.R
rugarch-startpars.R rugarch-tests.R rugarch-armafor.R
rugarch-graphs.R rugarch-classes.R rugarch-sgarch.R
rugarch-figarch.R rugarch-csgarch.R rugarch-fgarch.R
rugarch-egarch.R rugarch-gjrgarch.R rugarch-aparch.R
rugarch-igarch.R rugarch-mcsgarch.R rugarch-realgarch.R
rugarch-multi.R rugarch-plots.R rugarch-rolling.R
rugarch-uncertainty.R rugarch-bootstrap.R rugarch-methods.R
rugarch-benchmarks.R arfima-classes.R arfima-multi.R
arfima-main.R arfima-methods.R rugarch-cv.R zzz.R
LazyLoad yes
URL http://www.unstarched.net, https://bitbucket.org/alexiosg
License GPL-3
RoxygenNote 6.0.1
NeedsCompilation yes
Author Alexios Ghalanos [aut, cre],
Tobias Kley [ctb]
Maintainer Alexios Ghalanos <alexios@4dscape.com>
Repository CRAN
Date/Publication 2020-02-08 06:10:03 UTC
1
2 R topics documented:
R topics documented:
rugarch-package . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
ARFIMA-class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
arfimacv . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
ARFIMAdistribution-class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
arfimadistribution-methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
ARFIMAfilter-class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
arfimafilter-methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
ARFIMAfit-class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
arfimafit-methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
ARFIMAforecast-class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
arfimaforecast-methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
ARFIMAmultifilter-class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
ARFIMAmultifit-class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
ARFIMAmultiforecast-class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
ARFIMAmultispec-class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
ARFIMApath-class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
arfimapath-methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
ARFIMAroll-class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
arfimaroll-methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
ARFIMAsim-class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
arfimasim-methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
ARFIMAspec-class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
arfimaspec-methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
autoarfima . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
BerkowitzTest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
DACTest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
DateTimeUtilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
dji30ret . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
dmbp . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
ESTest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
GARCHboot-class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
GARCHdistribution-class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
GARCHfilter-class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
GARCHfit-class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
GARCHforecast-class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
GARCHpath-class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
GARCHroll-class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
GARCHsim-class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
GARCHspec-class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
GARCHtests-class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
ghyptransform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
GMMTest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
HLTest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
mcsTest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
multifilter-methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
multifit-methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
rugarch-package 3
multiforecast-methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
multispec-methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
rGARCH-class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
rgarchdist . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
sp500ret . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
spyreal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
ugarchbench . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
uGARCHboot-class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
ugarchboot-methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
uGARCHdistribution-class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
ugarchdistribution-methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
uGARCHfilter-class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
ugarchfilter-methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
uGARCHfit-class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
ugarchfit-methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
uGARCHforecast-class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
ugarchforecast-methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
uGARCHmultifilter-class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
uGARCHmultifit-class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
uGARCHmultiforecast-class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
uGARCHmultispec-class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
uGARCHpath-class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
ugarchpath-methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
uGARCHroll-class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
ugarchroll-methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
uGARCHsim-class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
ugarchsim-methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
uGARCHspec-class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
ugarchspec-methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
VaRDurTest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
VaRloss . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
VaRplot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
VaRTest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
Index 101
rugarch-package The rugarch package
Description
The rugarch package aims to provide a flexible and rich univariate GARCH modelling and testing
environment. Modelling is a simple process of defining a specification and fitting the data. Infer-
ence can be made from summary, various tests and plot methods, while the forecasting, filtering and
simulation methods complete the modelling environment. Finally, specialized methods are imple-
mented for simulating parameter distributions and evaluating parameter consistency, and a bootstrap
forecast method which takes into account both parameter and predictive distribution uncertainty.
The testing environment is based on a rolling backtest function which considers the more general
4 rugarch-package
context in which GARCH models are based, namely the conditional time varying estimation of
density parameters and the implication for their use in analytical risk management measures.
The mean equation allows for AR(FI)MA, arch-in-mean and external regressors, while the vari-
ance equation implements a wide variety of univariate GARCH models as well as the possibility
of including external regressors. Finally, a set of feature rich distributions are used for modelling
innovations and documented in the vignette.
This package is part of what used to be the rgarch package, which was split into univariate (rugarch)
and multivariate (rmgarch) models for easier maintenance and use, both of which are now hosted
on CRAN (stable) and bitbucket (development).
Details
While the package has implemented some safeguards, both during pre-estimation as well as the
estimation phase, there is no guarantee of convergence in the fitting procedure. As a result, the fit
method allows the user to input starting parameters as well as keep any parameters from the spec as
fixed (including the case of all parameters fixed).
The functionality of the packages is contained in the main methods for defining a specification
ugarchspec, fitting ugarchfit, forecasting ugarchforecast, simulation from fit object ugarchsim,
path simulation from specification object ugarchpath, parameter distribution by simulation ugarchdistribution,
bootstrap forecast ugarchboot and rolling estimation and forecast ugarchroll. There are also
some functions which enable multiple fitting of assets in an easy to use wrapper with the option
of multicore functionality, namely multispec, multifit, multifilter and multiforecast. Ex-
planations on the available methods for the returned classes can be found in the documentation for
those classes.
A separate subset of methods and classes has been included to calculate pure ARFIMA models with
constant variance. This subset includes similar functionality as with the GARCH methods, with the
exception that no plots are yet implemented, and neither is a forecast based on the bootstrap. These
may be added in the future. While there are limited examples in the documentation on the ARFIMA
methods, the interested user can search the rugarch.tests folder of the source installation for some
tests using ARFIMA models as well as equivalence to the base R arima methods (particularly repli-
cation of simulation). Finally, no representation is made about the adequacy of ARFIMA models,
particularly the statistical properties of parameters when using distributions which go beyond the
Gaussian.
The conditional distributions used in the package are also exposed for the benefit of the user through
the rgarchdist functions which contain methods for density, distribution, quantile, sampling and
fitting. Additionally, ghyptransform function provides the necessary parameter transformation and
scaling methods for moving from the location scale invariant ‘rho-zeta’ parametrization with mean
and standard deviation, to the standard ‘alpha-beta-delta-mu’ parametrization of the Generalized
Hyperbolic Distribution family.
The type of data handled by the package is now completely based on the xts package, and only data
which can be coerced to such will be accepted by the package. For the estimation and filter routines,
some of the main extractors methods will now also return xts objects.
Some benchmarks (published and comparison with commercial package), are available through the
ugarchbench function. The ‘inst’ folder of the source distribution also contains various tests which
can be sourced and run by the user, also exposing some finer details of the functionality of the pack-
age. The user should really consult the examples supplied in this folder which are quite numerous
and instructive with some comments.
Since version 1.0-14, all parallel estimation is carried out through a user-supplied cluster object,
created from the parallel package, meaning that the user is now in control of managing the cluster
rugarch-package 5
lifecycle. This greatly simplifies the parallel estimation process and adds a layer of flexibility to the
type of resources supported.
Finally, the global extractor functions sigma and fitted will now work with almost all returned
classes and the return the conditional sigma and mean values, whether these are from an estimated,
filtered, forecast, or simulated object (and their multi- function equivalents).
How to cite this package
Whenever using this package, please cite as
@Manual{Ghalanos_2014,
author = {Alexios Ghalanos},
title = {{rugarch}: Univariate GARCH models.},
year = {2014},
note = {R package version 1.4-0.},}
License
The releases of this package is licensed under GPL version 3.
Author(s)
Alexios Ghalanos
References
Baillie, R.T. and Bollerslev, T. and Mikkelsen,H.O. 1996, Fractionally integrated generalized au-
toregressive conditional heteroskedasticity, Journal of Econometrics, 3–30 .
Berkowitz, J. 2001, Testing density forecasts, with applications to risk management, Journal of
Business and Economic Statistics, 19(4), 465–474.
Bollerslev, T. 1986, Generalized Autoregressive Conditional Heteroskedasticity 1986, Journal of
Econometrics, 31, 307–327.
Ding, Z., Granger, C.W.J. and Engle, R.F. 1993, A Long Memory Property of Stock Market Returns
and a New Model, Journal of Empirical Finance, 1, 83–106.
Engle, R.F. and Ng, V. K. 1993, Measuring and Testing the Impact of News on Volatility, Journal
of Finance, 48, 1749–1778.
Engle, R. F., and Sokalska, M. E. 2012, Forecasting intraday volatility in the US equity market.
Multiplicative component GARCH. Journal of Financial Econometrics, 10(1), 54–83.
Fisher, T. J., and Gallagher, C. M. 2012, New weighted portmanteau statistics for time series good-
ness of fit testing, Journal of the American Statistical Association, 107(498), 777–787.
Glosten, L.R., Jagannathan, R. and Runkle, D.E. 1993, On the Relation between the Expected Value
and the Volatility of the Nominal Excess Return on Stocks, Journal of Finance, 48(5), 1779–1801.
Hansen, B.E. 1990, Langrange Multiplier Tests for Parameter Instability in Non-Linear Models,
mimeo.
Hentschel, Ludger. 1995, All in the family Nesting symmetric and asymmetric GARCH models,
Journal of Financial Economics, 39(1), 71–104.
剩余107页未读,继续阅读
资源评论
刘国聪
- 粉丝: 0
- 资源: 1
上传资源 快速赚钱
- 我的内容管理 展开
- 我的资源 快来上传第一个资源
- 我的收益 登录查看自己的收益
- 我的积分 登录查看自己的积分
- 我的C币 登录后查看C币余额
- 我的收藏
- 我的下载
- 下载帮助
安全验证
文档复制为VIP权益,开通VIP直接复制
信息提交成功