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Copula-GARCH模型下的两资产期权定价

11-28 417

Value-At-Risk (VaR) curve with Copula-GARCH model (R)

I'm trying to creave a VaR curve with the Copula-GARCH model in R. Here's what I have:

Getting stock prices for Boeing & Airbus and calculating yields:

Creating an optimal portfolio:

2D distribution of yields:

The problem is about obtaining a VaR curve (here's splitting the sample into a test and examinating sample):

How can I combine GARCH and copulas for fitting the model and creating a VaR curve?

Copula、CoVaR、Garch、DCC、藤Vine、BEKK、SV、ECM

金融市场联动及风险 于 2021-03-08 13:00:47 发布 2049 收藏 18

金融市场联动相关、风险测度、风险溢出

这个主题一直是金融论文关注的重点,主要包含以下几类。
1.从收益率的角度,也就是一阶矩的角度。这类方法主要包括协整、格兰杰因果检验、向量自回归(VAR)、误差修正(ECM)、脉冲响应、方差分解等。
2.从波动率的角度,也就是二阶矩的角度。这类方法主要包括一些波动率模型,比如GARCH、SV等,以及DCC时变相关和BEKK、CoVaR等波动溢出模型。
3.从非线性相依结构的角度。这类方法主要包括copula、vinecopula及其时变模型等,风险溢出包括CoVaR、CoES等。

我目前精通以上所有研究方法,若需要帮助欢迎交流

基于vine copula模型的全球证券市场间投资组合优化及风险度量 ,赵子然,王斌会,文章引入vine copula模型来描述全球8个股票市场指数在2008年金融危机期间及其前后3个时期的联合分布,并采用蒙特卡洛模拟给出了CVaR值最

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04-05 1万+

原文链接: 一模型介绍 二资料介绍 实现对相关期刊论文进行论文重现,解决实证分析中的技术操作问题。 里面包含了每一步详细的步骤,可以方便的利用这个手册解决大部分DCC-GARCHCoVaR相关的论文模型的实现问题。即从数据下载到模型实现一整条操作步骤。 关键词:【动态CoVaR】【DCC-GARCH模型】【DCC-GARCH-CoVaR】 部分代码示例,查看统计值: '一.

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11-15 471

这里写自定义目录标题欢迎使用Markdown编辑器新的改变功能快捷键合理的创建标题,有助于目录的生成如何改变文本的样式插入链接与图片如何插入一段漂亮的代码片生成一个适合你的列表创建一个表格设定内容居中居左居右SmartyPants创建一个自定义列表如何创建一个注脚注释也是必不可少的KaTeX数学公式新的甘特图功能,丰富你的文章UML 图表FLowchart流程图导出与导入导出导入 欢迎使用Markdown编辑器 你好! 这是你第一次使用 Markdown编辑器 所展示的欢迎页。如果你想学习如何使用Mar

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03-22 1万+

当定点仿真完成后,就需要使用FPGA实现。这时候需要把之前仿好的滤波器参数或者输入信号输出为coes文件:%% output coe file Ff = fimath('CastBeforeSum', 0, 'OverflowMode', 'Saturate', . 'RoundMode', 'round', 'ProductMode', 'SpecifyPrecisi.

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若需要帮助指导可留言或sixin 擅长的CoVaR方法: 1.静态/时变Copula 2.上行/下行Copula 3.静态/时变VineCopula 4.GARCH族/DCC-GARCH 5.静态/动态分位数回归 若需要帮助指导可留言或sixin

以分位数CoVaR和分位数VaR评估CoVaR值。 描述 用不同类型的Copula和边际分布计算条件分位数或CoVaR。 在该软件包中,包括了几个双变量系动词族,用于双变量分析。 它提供椭圆形(高斯和学生t)以及阿基米德(Clayton,Gumbel,Frank,Plackett,BB1,SCJ,旋转的Clayton和旋转的Gumbel)copula的功能,以覆盖可能的依赖结构的较大带宽。 作者 Andrea Ugolini \ Juan Carlos Reboredo Noguiera 参考 Reboredo,JC和Ugolini,A.(2016)。 石油价格走势和股票收益的分位数依赖性。 能源经济学,54,33-49。 例子 RCoVaRopula 负载(“ Data_demo.Rdata”)源(“ CoVaR.R”)源(“ DynCopulaCoVaR.R”)源(“ DynCo

03-18 5369

基于ARMA-偏tGARCHDCC-GARCH模型测算CoVaR——R语言实现 CoVaR是目前金融学界和管理实践中较为主流的测量一个机构(系统)对另一个机构(系统)风险溢出的指标,计算CoVaR的方法主要有分位数回归法Coupla模型和DCC-GARCH型。本文主要介绍如何利用DCC-GARCH模型对CoVaR进行计算并利用R实现。代码见文末。 CoVaR CoVaR这一概念由VaR衍生而来,其经济含义是当某一个机构发生风险时,另一机构所承担的总体在险价值。VaR则是指在一定的持有期内,在一定的置信水

提出了系统性风险的衡量标准:CoVaR,金融系统的风险价值(VaR),以机构陷入困境为条件。 认为一个机构对系统性风险的贡献是CoVaR之间的差异条件 关于受困的机构和机构中间状态的CoVaR。 根据我们对公开交易金融机构领域的CoVaR的估计,我们量化了杠杆等特征的程度, 规模和期限错配预测系统性风险贡献。 Copula-GARCH模型下的两资产期权定价 我们表明,预测的系统性风险贡献是反周期的,并且基于这些特征预测系统性风险贡献的程度,主张宏观审慎监管。

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README.md

CoVaR value evaluting at quantile CoVaR and quantile VaR.

Calculate the conditional quantile or CoVaR with different type of Copula and marginal distribution. In this package several bivariate copula families are included for bivariate analysis. It provides functionality of elliptical (Gaussian and Student t) as well as Archimedean (Clayton, Gumbel, Frank, Plackett, BB1, SCJ, rotated clayton and rotated Gumbel) copulas to cover a large bandwidth of possible dependence structures.

Reboredo, J. C., & Ugolini, A. (Copula-GARCH模型下的两资产期权定价 2016). Quantile dependence of oil price movements and stock returns. Energy Economics, 54, 33-49.

load("Data_demo.Rdata") source("CoVaR.R") source("DynCopulaCoVaR.R") source("DynCopulaCoVaRUpper.R") source("skewtdis_inv.R") require("pracma") require("copula")

CoVaR1part = CoVaR(0.05,0.05,par=par1_1,par2=par2_1,dof=tailBrazil,gamma=asyBrazil,Copula-GARCH模型下的两资产期权定价 cond.mean=meanBrasil1, cond.sigma=sigmaBrasil1,dist="tskew",type="Student")

CoVaR2part = CoVaR(0.05,0.Copula-GARCH模型下的两资产期权定价 05,par=par1_2,par2=par2_2,dof=tailBrazil,gamma=asyBrazil,cond.mean=meanBrasil2, cond.sigma=sigmaBrasil2,dist="tskew",type="Student")

CoVaR1partUp = CoVaR(0.95,0.95,par=par1_1,par2=par2_1,Copula-GARCH模型下的两资产期权定价 dof=tailBrazil,gamma=asyBrazil,cond.mean=meanBrasil1, cond.sigma=sigmaBrasil1,dist="tskew",type="StudentUp")

CoVaR2partUp = CoVaR(0.95,0.95,par=par1_2,par2=par2_2,dof=tailBrazil,gamma=asyBrazil,cond.mean=meanBrasil2, cond.sigma=sigmaBrasil2,dist="tskew",type="StudentUp")

CoVaR1D = CoVaR1part$CoVaR CoVaR2D = CoVaR2part$CoVaR CoVaR1U = CoVaR1partUp$CoVaR CoVaR2U = CoVaR2partUp$CoVaR

TimeCoVaRD = rbind(CoVaR1D,CoVaR2D) TimeCoVaRU = rbind(CoVaR1U,CoVaR2U)

plot(as.matrix(TimeCoVaRD),type="l",col="blue", Copula-GARCH模型下的两资产期权定价 Copula-GARCH模型下的两资产期权定价 ylim=c(-0.5,0.5),xlab="Time",ylab="") lines(VaR,col="black",lty=2) lines(TimeCoVaRU,Copula-GARCH模型下的两资产期权定价 col="red",lty=4) lines(VaRup,col="green",Copula-GARCH模型下的两资产期权定价 lty=3) abline(h=0,col="gray33")

Copula-GARCH模型下的两资产期权定价

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Abstract

This paper Copula-GARCH模型下的两资产期权定价 minimizes the risk of Brent oil in a multivariate portfolio, with three risk-minimizing goals: variance, Copula-GARCH模型下的两资产期权定价 parametric value-at-risk (VaR), and semiparametric value-at-risk. Brent oil is combined with five emerging ASEAN (Association of Southeast Asian Nations) stock indexes and five more developed non-ASEAN indexes. The preliminary dynamic equiciorrelation estimates indicate that the ASEAN stock indexes are less integrated and thus potentially better for diversification purposes. The portfolio results show that the ASEAN indexes are better hedges for oil in terms of minimum variance and minimum VaR. However, although the ASEAN indexes have higher extreme risk, we find that a portfolio with these indexes has slightly lower modified VaR than a portfolio with the non-ASEAN indexes. The reason is probably the higher variance and higher equicorrelation of the non-ASEAN indexes, because these inputs affect the value of the modified downside risk of a portfolio. As a complementary analysis, we put a 50 percent constraint on Brent in the portfolios, and then the portfolios with the non-ASEAN indexes have better risk-minimizing results.