rugarch-package(rugarch)
rugarch-package()所属R语言包：rugarch
The rugarch package
rugarch包
译者：生物统计家园网 机器人LoveR
描述----------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. Inference 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 implemented for simulating parameter distributions and evaluating parameter consistency, and a bootstrap forecast method which takes into account both parameter and predictive distribution uncertainty.<br> The testing environment is based on a rolling backtest function which considers the more general 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.<br> The mean equation allows for AR(FI)MA, arch-in-mean and external regressors, while the variance equation implements a wide variety of univariate GARCH models as well as the possibility of including external regressors. Finally, a set of rich distributions from the “fBasics” package and Johnson's reparametrized SU from the “gamlss” package are used for modelling innovations.<br> 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. The rmgarch package is still under re-write so the old rgarch package should be used in the meantime for multivariate models (and hosted on r-forge).
rugarch包的目的是提供一个灵活和丰富的单变量GARCH模型和测试环境。模型定义的规范和装修的数据是一个简单的过程。可以推断从摘要，各种测试和图方法，而预测，过滤和完成建模环境的模拟方法。最后，专门的方法来实现为模拟参数分布和评估参数的一致性，和自举的预测方法，这需要考虑两个参数和分布预测的不确定性。<BR>的测试环境是基于一个滚动的回溯测试功能，考虑更一般的情况下GARCH模型的基础，即有条件的时间变密度参数估计与意义，它们在分析的风险管理措施。<BR>的均值方程允许AR（FI）马，拱均值和外部的回归系数，而方差方程实现了单变量GARCH模型以及多种可能性，包括外部的回归系数。最后，一组丰富的“fBasics”的包和约翰逊的reparametrized的SU从的“gamlss的”包分配用于建模的创新。参考这个包是什么是rgarch包，这是分裂的一部分进入单因素（rugarch）和的多元（rmgarch）模型更容易维护和使用。 rmgarch包仍在重新写使老rgarch包的同时，应采用多变量模型（R-伪造和托管）。
Details
详细信息----------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).<br> 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. Explanations on the available methods for the returned classes can be found in the documentation for those classes.<br> 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 replication 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.<br> 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.<br> The type of data handled by the package is quite varied, accepting “timeSeries”, “xts”, “zoo”, “zooreg”, “data.frame” with dates as rownames, “matrix” and “numeric” vector with dates as names. For the “numeric” vector and “data.frame” with characterdates in names or rownames, the package tries a variety of methods to try to recognize the type and format of the date else will index the data numerically. The package holds dates internally as class Date. This mostly impacts the plots and forecast summary methods. For high frequency data, the user should make use of a non-named representation such as “matrix” or “numeric” as the package has yet to implement methods for checking and working with frequencies higher than daily (and is unlikely to do so). Finally, the functions ForwardDates and WeekDayDummy offer some simple Date manipulation methods for working with forecast dates and creating day of the week dummy variables for use in GARCH modelling.<br> 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 package. The user should really consult the examples supplied in this folder which are quite numerous and instructive with some comments.
虽然包已经实施了一些保障措施，无论是在预估计，以及评估阶段，也不能保证在装修过程中的收敛。作为一个结果，拟合方法允许用户输入初始参数，以及保持为固定（包括固定的所有参数的情况下）从规范的任何参数。参考的软件包的功能中所载的方法主要合适的对象定义的规范ugarchspec，装修ugarchfit，预测ugarchforecast，模拟ugarchsim，模拟从规范对象ugarchpath，参数分布的路径模拟 X>中，引导预测ugarchdistribution和滚动估计和预测ugarchboot。也有一些功能，使多个配件的资产在一个易于使用的包装与多核功能的选项，即ugarchroll，multispec，multifit和multifilter。上的可用方法返回的类的说明可以发现，在这些类的文档。<BR>一个单独的子集，已被列入计算方法和类的纯ARFIMA模型与恒定方差。该子集包含与GARCH方法，但尚未实施的例外，不图类似的功能，也不是预测的基础上的引导。这些可以在将来添加。 ARFIMA方法在文档中的例子虽然是有限的，有兴趣的用户可以搜索rugarch.tests的文件夹中源安装一些测试使用ARFIMA模型，以及等效碱基r自回归 - 求和 - 移动平均“方法（特别是仿真复制）。最后，没有表示是否有足够的ARFIMA模型，特别包中使用的统计特性的参数时，使用高斯。参考超越的分布，条件分布也暴露了用户的利益，通过 multiforecast功能，包含密度，分布，分位数，采样和拟合的方法。此外，rgarchdist函数提供了必要的参数变换，均值和标准差从位置规模不变的RHO-Zeta的参数化与移动和缩放方法，标准的“α-β-Δ-亩的参数化广义双曲分布族。参考的数据包处理的类型是多种多样的，接受“时间序列”，“XTS”，“动物园”中，“zooreg”，“数据框”日期为行名，“黑客帝国”和“数字”向量的日期作为名称。对于“数字”向量“数据框”characterdates名或行名，包尝试了各种方法，尝试识别的类型和日期格式，否则将索引的数字数据。包日期内部类ghyptransform。这主要影响的图和预测总结的方法。对于高频数据，用户应该使用的一个未命名的，如“矩阵”或“数字”表示，作为包检查和工作频率高于每天（尚未实施方法，是不可能做左右）。最后，功能Date和ForwardDates提供了一些简单的日期预测日期和日的一周虚拟变量GARCH模型中使用的操作方法。<BR>一些基准测试（出版和比较，商业包），可用的，通过WeekDayDummy功能。的源代码分发的“出师表”文件夹中还包含各种测试，可以由用户采购和运行，也暴露出一些更精细的细节功能的包。用户真的应该咨询在此文件夹中提供的一些意见和指导性，是相当多的例子。
如何引用这个包----------How to cite this package----------
Whenever using this package, please cite as<br>
每当使用这个包，请举出作为<BR>
许可证----------License----------
The releases of this package is licensed under GPL version 3.
这个包的版本的授权下GPL第3版。
（作者）----------Author(s)----------
Alexios Ghalanos
参考文献----------References----------
integrated generalized autoregressive conditional heteroskedasticity, Journal of Econometrics, 3–30 .<br> Berkowitz, J. 2001, Testing density forecasts, with applications to risk management, Journal of Business and Economic Statistics, 19(4), 465–474.<br> Bollerslev, T. 1986, Generalized Autoregressive Conditional Heteroskedasticity 1986, Journal of Econometrics, 31, 307–327.<br> 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.<br> Engle, R.F. and Ng, V. K. 1993, Measuring and Testing the Impact of News on Volatility, Journal of Finance, 48, 1749–1778.<br> 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.<br> Hansen, B.E. 1990, Langrange Multiplier Tests for Parameter Instability in Non-Linear Models, mimeo.<br> Hentschel, Ludger. 1995, All in the family Nesting symmetric and asymmetric GARCH models, Journal of Financial Economics, 39(1), 71–104.<br> Nelson, D.B. 1991, Conditional Heteroskedasticity in Asset Returns: A New Approach, Econometrica, 59, 347–370.<br> Pascual, L., Romo, J. and Ruiz, E. 2004, Bootstrap predictive inference for ARIMA processes, Journal of Time Series Analysis.<br> Pascual, L., Romo, J. and Ruiz, E. 2006, Bootstrap prediction for returns and volatilities in GARCH models, Computational Statistics and Data Analysis.<br> Vlaar, P.J.G. and Palm, F.C. 1993, The Message in Weekly Exchange Rates in the European Monetary System: Mean Reversion Conditional Heteroskedasticity and Jumps, Journal of Business and Economic Statistics, 11, 351–360.<br>
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注：
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发表于 2012-9-28 23:37:44
