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Copula-GARCH模型下的两资产期权定价

05-21 1万+

Portfolio optimization based on GARCH-EVT-Copula forecasting models

The decomposition of the univariate and multivariate models of the target model reveal the necessity to allow for high order and thus long-lasting autoregressive modelling as well as asymmetric tail dependence and rotated copulae across different portfolios. Expand

To optimize the investment portfolio, conditional value at risk is a new approach. To amend the non-normal distribution of return on assets, and the non-linear correlation between return, 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 the … Expand

Revisiting Fama–French factors' predictability with Bayesian modelling and copula‐based portfolio optimization

This study is investigating the predictability of the five Fama–French factors and explores their optimal Copula-GARCH模型下的两资产期权定价 portfolio allocation for factor investing during 2000–2017. Firstly, we forecast each factor … Expand

The ARMA-APARCH-EVT Models Based on HAC in Dependence Modeling and Risk Assessment of a Financial Portfolio

This study aims to widen Copula-GARCH模型下的两资产期权定价 the sphere of practical applicability of the Copula-GARCH模型下的两资产期权定价 HAC model combined with the ARMA-APARCH volatility forecast model and the extreme values theory (EVT) to improve the performance and accuracy of modeling based on HACs. Expand

Dependence Modeling and Risk Assessment of a Financial Portfolio with ARMA-APARCH-EVT models based on HACs

This study aims to widen the sphere of pratical applicability of the HAC model combined with the ARMA-APARCH volatility forecast model and the extreme values theory (EVT) Copula-GARCH模型下的两资产期权定价 to improve the performance and accuracy Copula-GARCH模型下的两资产期权定价 of modeling based on HACs. Expand

  • View 4 excerpts, cites methods and background

This work extends the Black-Litterman (BL) approach to Copula-GARCH模型下的两资产期权定价 incorporate tail dependency in portfolio optimization and estimate the posterior joint distribution of returns using vine copulas to lead to Copula-GARCH模型下的两资产期权定价 flexibility in modeling returns symmetric and asymmetric multivariate distribution from a range of copula Copula-GARCH模型下的两资产期权定价 families. Expand

F orecasting the volatility of Copula-GARCH模型下的两资产期权定价 a financial asset has wide implications in finance. Conditional variance extracted from the GARCH framework could be a suitable proxy Copula-GARCH模型下的两资产期权定价 of financial asset … Expand

Modeling Stock Returns Using Asymmetric Garch-Icapm with Mixture and Heavy-Tailed Distributions: An Application to COVID-19 Pandemic Copula-GARCH模型下的两资产期权定价 Forecasts

COVID-19 pandemic is an extreme event that created a turmoil in stock markets around the world. This unexpected circumstance poses a critical question whether the prevailing models can help predict … Expand

  • View 2 excerpts, cites methods and background

A new approach Copula-GARCH模型下的两资产期权定价 based on the copula theory is employed in the analysis and forecasting of hospitality and tourism-related stock return volatility (HTSRV), showing that copulas well specify both linear and nonlinear serial dependence structures, which lead to forecasting results as good as or even better than those of the benchmark models. Expand

Copula-GARCH模型下的两资产期权定价

Classes (S4) of commonly used elliptical, Archimedean, extreme-value and other copula families, as well as their rotations, mixtures and asymmetrizations. Nested Archimedean copulas, related tools and special functions. Methods for density, distribution, random number generation, bivariate dependence measures, Rosenblatt transform, Kendall distribution function, perspective and contour plots. Fitting of copula models Copula-GARCH模型下的两资产期权定价 with potentially partly fixed parameters, including standard errors. Serial independence tests, copula specification tests (independence, exchangeability, radial symmetry, extreme-value dependence, Copula-GARCH模型下的两资产期权定价 goodness-of-fit) and model selection based on cross-validation. Empirical copula, smoothed versions, and non-parametric estimators of the Pickands dependence function.

我精通Copula、CoVaR、GARCH、ARIMA、协整、VAR、DCC、BEKK、MES、SRISK、最优组合权重、模拟预测等模型

金融市场联动及风险 于 2021-12-24 22:29:08 发布 545 收藏 3

若需要帮助交流可sixin或535844430

1.收益率相关、均值溢出:ARIMA、协整检验、格兰杰因果检验、向量自回归VAR、向量误差修正VECM、脉冲响应、方差分解。

2.波动率相关、波动溢出:GARCH族、随机波动SV、极端风险VaR、CVaR、ES、DCC-GARCH动态相关、BEKK波动溢出、CoVaR、MES风险溢出、SRISK系统性风险、HARRV跳跃、分形。

3.非线性相关、尾部相关、上下行风险溢出:时变动态Copula(DCC、Patton)、藤Vinecopula、条件藤Vinecopula、时变混合Copula、上下行时变尾部风险溢出CoVaR、MES、COES、系统性风险SRISK。

SJCcopula matlab,Patton_copula_toolbox 时间序列模型在金融领域的应用 matlab 238万源代码下载- www.pudn.com.

辉煌之欢 于 2021-03-27 12:08:49 发布 323 收藏 1

详细说明:copula时间序列模型在金融领域的应用-copula time series model applied in the financial sector

[PSO.rar] - 粒子群算法,也称粒子群优化算法(Particle Swarm Optimization),缩写为 PSO,

[Dynamic_copula_Toolbox_3.0.zip] - 用来估计dynanmic copula的参数和copula Copula-GARCH模型下的两资产期权定价 vine中的相关参数,并进行拟合。

[StructuredTcopulaMLE.zip] - 结构化t copula函数的极大似然估计

[mvcoprnd.rar] - 使用Matlab软件计算Normal copula,Student t Copula,Clayton Copula,Gumbel Copula的程序

[copula-theory.rar] - copula理论及matlab应用实例 内含详细代码 已经过调试运行

12-19 213

1.数据和软件1.Data and Software现金融数据来源很多,常用有国际著名公司Bloomberg,以及国内Wind,东方财富,聚源和聚宽等等。感兴趣朋友可以根据自身情况选择数据来。本文中数据来自雅虎金融(Yahoo!Finance)。Nowadays, there are many ways to get financial data. The most we...

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05-21 1万+

% Copula-GARCH模型下的两资产期权定价 Example code for some copula functions % Copula-GARCH模型下的两资产期权定价 % Andrew Patton % % 27 February 2006 load 'c:\core\teaching\lse_teaching\lecture_notes\ibm_ccola_rets.txt' -ascii; ibm = ibm_ccola_rets(:,1); ccola = ibm_ccola_rets(:,2); % exceedence correlations inc = 0.

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04-19 868

Mathworks-Dynamic Copula Toolbox 3.0下载地址 1 概述 根据介绍,Dynamic copula工具箱支持以下一般类型模型Copula-GRACH models Copula Vines 1.1 Copula-GRACH 根据【Dynamic Copula Toolbox.】一文中介绍: Copula – GARCH models is the class of models Copula-GARCH模型下的两资产期权定价 where some of the parameters are potentiall

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