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GARCHProcess[κ,{α1,…,αq},{β1,…,βp}]

表示阶数为 p 和 q，通过标准白噪声驱动的广义自回归条件异方差过程.

GARCHProcess[κ,{α1,…,αq},{β1,…,βp},init]

表示初始数据为 init 的 GARCH 过程.

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Details and Options Details and Options
范例  
基本范例  
范围  
基本用法  
过程切片性质  
属性和关系  
参见
相关指南
历史
按以下格式引用：

GARCHProcess

GARCHProcess[κ,{α1,…,αq},{β1,…,βp}]

表示阶数为 p 和 q，通过标准白噪声驱动的广义自回归条件异方差过程.

GARCHProcess[κ,{α1,…,αq},{β1,…,βp},init]

表示初始数据为 init 的 GARCH 过程.

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范例

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基本范例  (3)

模拟 GARCHProcess：

Wolfram Language code: RandomFunction[GARCHProcess[2, {.1}, {.2}], {0, 10}]
Wolfram Language code: %["Path"]
Wolfram Language code: ListPlot[RandomFunction[GARCHProcess[2, {.1}, {.2}], {0, 100}], Filling -> Axis]

弱平稳过程的无条件均值和方差：

Wolfram Language code: Mean[GARCHProcess[κ, {Subscript[α, 1], Subscript[α, 2]}, {Subscript[β, 1], Subscript[β, 2], Subscript[β, 3]}][t]]
Wolfram Language code: Variance[GARCHProcess[κ, {Subscript[α, 1], Subscript[α, 2]}, {Subscript[β, 1], Subscript[β, 2], Subscript[β, 3]}][t]]

带固定初始值：

Wolfram Language code: Mean[GARCHProcess[1, {1 / 3}, {1 / 4}, {Subscript[x, -1]}][t]]
Wolfram Language code: Variance[GARCHProcess[1, {1 / 3}, {1 / 4}, {Subscript[x, -1]}][t]]

观测结果是不相关的，但相互依赖：

Wolfram Language code: data = RandomFunction[GARCHProcess[1, {.1, .3}, {.2}], {10 ^ 3}]; corr = CorrelationFunction[data, {20}];
Wolfram Language code: ListPlot[corr, Filling -> Axis, PlotRange -> All]

数据的平方值是相关的：

Wolfram Language code: corr2 = CorrelationFunction[data ^ 2, {20}];
Wolfram Language code: ListPlot[corr2, Filling -> Axis, PlotRange -> All]

范围  (13)

基本用法  (8)

模拟一组路径：

Wolfram Language code: data = RandomFunction[GARCHProcess[.3, {.5}, {.2}], {0, 30}, 4]
Wolfram Language code: ListLinePlot[data, Filling -> Axis]

使用任意精度模拟：

Wolfram Language code: RandomFunction[GARCHProcess[1 / 3, {1 / 10}, {1 / 4}], {5}, WorkingPrecision -> 20]["Path"]

模拟具有给定初始值的弱平稳过程：

Wolfram Language code: sproc[x_] := GARCHProcess[.03, {.4}, {.3}, {x}];
Wolfram Language code: pts = {-1, 0, 2};
Wolfram Language code: samples = Table[SeedRandom[4];RandomFunction[sproc[x], {20}], {x, pts}];
Wolfram Language code: ListLinePlot[samples, DataRange -> {0, 12}, PlotLegends -> (StringJoin["x = ", ToString[#]]& /@ pts)]

非弱平稳过程：

Wolfram Language code: tproc[x_] := GARCHProcess[.03, {.7}, {.8}, {x}];
Wolfram Language code: tsamples = Table[SeedRandom[4];RandomFunction[tproc[x], {20}], {x, pts}];
Wolfram Language code: ListLinePlot[tsamples, DataRange -> {0, 12}, PlotRange -> All, PlotLegends -> (StringJoin["x = ", ToString[#]]& /@ pts)]

一个集成的 GARCHProcess：

Wolfram Language code: α = .4; proc = GARCHProcess[1, {α}, {1 - α}, {}];
Wolfram Language code: ListPlot[RandomFunction[proc, {0, 100}], Filling -> Axis]

发散的 GARCHProcess：

Wolfram Language code: proc = GARCHProcess[2, {.3, .7}, {.4, .2}, {}];
Wolfram Language code: ListPlot[RandomFunction[proc, {0, 100}], Filling -> Axis]

这样的过程不是二阶平稳过程：

Wolfram Language code: WeakStationarity[proc]

GARCHProcess 成为协方差平稳过程的条件：

Wolfram Language code: WeakStationarity[GARCHProcess[κ, {Subscript[α, 1], Subscript[α, 2]}, {Subscript[β, 1], Subscript[β, 2]}]]

GARCHProcess[1,1] 具有二阶平稳性的区域：

Wolfram Language code: cond = WeakStationarity[GARCHProcess[κ, {α}, {β}]]
Wolfram Language code: RegionPlot[cond, {α, 0, 1}, {β, 0, 1}, FrameLabel -> Automatic]

估计 GARCHProcess：

Wolfram Language code: SeedRandom[34]; data = RandomFunction[GARCHProcess[.3, {.2}, {.4}], {6 10 ^ 2}];
Wolfram Language code: tsm = TimeSeriesModelFit[data, {"GARCH", {1, 1}}]
Wolfram Language code: tsm["Process"]

使用最大条件似然：

Wolfram Language code: EstimatedProcess[data, GARCHProcess[1, 1], ProcessEstimator -> "MaximumConditionalLikelihood"]

预测：

Wolfram Language code: proc = GARCHProcess[.3, {.5}, {.4}]; data = RandomFunction[proc, {10 ^ 2}];

预测接下来的20步：

Wolfram Language code: forecast = TimeSeriesForecast[proc, data, {20}];
Wolfram Language code: forecast["Path"]

求预测的均方误差：

Wolfram Language code: errors = forecast["MeanSquaredErrors"]

所预测的状态都等于零，因此预测标准偏差范围是：

Wolfram Language code: ubound = TimeSeriesMap[Sqrt, errors]; lbound = TimeSeriesMap[-Sqrt[#]&, errors];

绘制值与均方误差：

Wolfram Language code: ListLinePlot[{data, forecast, lbound, ubound}, PlotStyle -> {Automatic, Automatic, Red, Red}, Filling -> {3 -> {4}}]

过程切片性质  (5)

阶数为 的弱平稳 GARCH 的矩：

Wolfram Language code: proc = GARCHProcess[k, {α}, {β}];
Wolfram Language code: Moment[proc[t], 4]//Simplify
Wolfram Language code: Cumulant[proc[2], 4]

具有给定初始条件的 GARCH 过程的矩：

Wolfram Language code: DiscretePlot[Moment[GARCHProcess[1, {.4}, {.1}, {}][t], 4], {t, 0, 4}]
Wolfram Language code: Moment[GARCHProcess[.06, {0.5}, {.3}, {}][2], 4]
Wolfram Language code: Cumulant[GARCHProcess[.3, {.1, .4}, {.2}, {}][2], 4]

偏度：

Wolfram Language code: Skewness[GARCHProcess[κ, {α}, {β}][t]]
Wolfram Language code: Skewness[GARCHProcess[κ, {α}, {β}, {x}][t]]

峰度：

Wolfram Language code: Kurtosis[GARCHProcess[κ, {α}, {β}][t]]

峰度有定义的区域：

Wolfram Language code: RegionPlot[Not[β ≤ 0 || 105 α^4 + 60 α^3 β + 18 α^2 β^2 + 4 α β^3 + β^4 ≥ 1], {α, 0, 1}, {β, 0, 1}, FrameLabel -> Automatic]

模拟切片分布：

Wolfram Language code: proc[α_] := GARCHProcess[.3, {α}, {.2}]; sample[α_] := RandomVariate[proc[α][3], 10 ^ 4];

样本概率密度函数：

Wolfram Language code: r = {.1, .3, .5, .7};Histogram[sample[#], Automatic, "PDF", PlotLabel -> StringJoin["α = ", ToString[#]]]& /@ r

用蒙特卡罗方法计算切片分布的 NProbability：

Wolfram Language code: proc = GARCHProcess[1, {.3}, {.2}];
Wolfram Language code: NProbability[x[1] > .3, xproc, Method -> {"MonteCarlo", "SamplingIncrement" -> 10 ^ 4}]

计算 NExpectation：

Wolfram Language code: NExpectation[x[2] ^ 2, xproc, Method -> {"MonteCarlo", "SamplingIncrement" -> 10 ^ 4}]

与二阶 Moment 比较：

Wolfram Language code: Moment[proc[2], 2]

属性和关系  (3)

GARCHProcess 的值是互不相关的：

Wolfram Language code: Correlation[GARCHProcess[2, {.3, .2}, {.2, .1}][{1, 2, 3}]]//MatrixForm

对应的 ARMAProcess：

Wolfram Language code: ARMAProcess[GARCHProcess[2, {.3}, {.1}]]

对于初始值已知的过程：

Wolfram Language code: ARMAProcess[GARCHProcess[2, {.3}, {.1}, {3, 2}]]

GARCHProcess 的平方值服从 ARMAProcess：

Wolfram Language code: proc = GARCHProcess[1, {.2, .3}, {.4}];
Wolfram Language code: data = RandomFunction[proc, {10 ^ 6}];

平方值的 CorrelationFunction 和 PartialCorrelationFunction：

Wolfram Language code: dataSQ = data ^ 2; ListPlot[#[dataSQ, {1, 30}], Filling -> Axis, PlotRange -> {-.2, 1}, PlotLabel -> #]& /@ {CorrelationFunction, PartialCorrelationFunction}

对应的 ARMA 过程：

Wolfram Language code: arma = ARMAProcess[proc]

ARMA 过程的 CorrelationFunction 和 PartialCorrelationFunction：

Wolfram Language code: ListPlot[#[arma, {1, 30}], Filling -> Axis, PlotRange -> {-.2, 1}, PlotLabel -> #]& /@ {CorrelationFunction, PartialCorrelationFunction}
Wolfram Research (2014)，GARCHProcess，Wolfram 语言函数，https://reference.wolfram.com/language/ref/GARCHProcess.html.

文本

Wolfram Research (2014)，GARCHProcess，Wolfram 语言函数，https://reference.wolfram.com/language/ref/GARCHProcess.html.

CMS

Wolfram 语言. 2014. "GARCHProcess." Wolfram 语言与系统参考资料中心. Wolfram Research. https://reference.wolfram.com/language/ref/GARCHProcess.html.

APA

Wolfram 语言. (2014). GARCHProcess. Wolfram 语言与系统参考资料中心. 追溯自 https://reference.wolfram.com/language/ref/GARCHProcess.html 年

BibTeX

@misc{reference.wolfram_2026_garchprocess, author="Wolfram Research", title="{GARCHProcess}", year="2014", howpublished="\url{https://reference.wolfram.com/language/ref/GARCHProcess.html}", note=[Accessed: 18-September-2026]}

BibLaTeX

@online{reference.wolfram_2026_garchprocess, organization={Wolfram Research}, title={GARCHProcess}, year={2014}, url={https://reference.wolfram.com/language/ref/GARCHProcess.html}, note=[Accessed: 18-September-2026]}