# Copula-GARCH模型下的两资产期权定价

Details Since there is no Copula-GARCH模型下的两资产期权定价 explicit forecasting routine, the user should use 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 this method >for incrementally building up n-ahead forecasts by simulating 1-ahead, >obtaining the means of the returns, sigma, Rho etc and feeding them to the next Copula-GARCH模型下的两资产期权定价 >round of simulation as starting values. The ‘rmgarch.tests’ folder contains >specific examples which illustrate this particular point.

## Forecasting for DCC Copula GARCH model in R

I'm trying to forecast the Copula Copula-GARCH模型下的两资产期权定价 Garch Model. I have tried to use the dccforecast function with the cGARCHfit Copula-GARCH模型下的两资产期权定价 but it turns out to be error saying that there is no applicable method for 'dccforecast' applied to an object 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 of class cGARCHfit. So how do actually we forecast the dcc copula garch model?

I have the following reproducible code.

DCC forecasts only work with dccfits. You can try the function cGARCHsim or let go of the Kendall method and go for a dccfit. Though forecasting using cGARCHsim can be a pain if you want to forecast for a longer period ahead.

Details

Since there is no explicit forecasting routine, the user should use this method >for incrementally building up n-ahead forecasts by simulating 1-ahead, >obtaining the means of the returns, sigma, Rho etc and feeding them to the next >round of simulation as starting values. The ‘rmgarch.tests’ folder contains >specific examples which illustrate this particular point.Copula-GARCH模型下的两资产期权定价

## The Copula GARCH Model

In this Copula-GARCH模型下的两资产期权定价 vignette, we demonstrate the copula GARCH approach (in general). Note that a special case (with normal or student $$t$$ residuals) is also available in the rmgarch package (thanks to Alexios Ghalanos for Copula-GARCH模型下的两资产期权定价 pointing this out).

## 1 Simulate data

First, we simulate the innovation distribution. Note that, for demonstration purposes, we choose a small sample size. Ideally, the sample size Copula-GARCH模型下的两资产期权定价 should be larger to capture GARCH effects.

Now we simulate two ARMA(1,1)-GARCH(1,1) processes with these copula-dependent innovations. To this end, recall that an ARMA( $$p_1$$ , $$q_1$$ )-GARCH( $$p_2$$ , $$q_2$$ ) model is given by $\begin X_t &= \mu_t + \epsilon_t\ \text\ \epsilon_t = \sigma_t Z_t,\\ \mu_t &= \mu + \sum_^ \phi_k (X_-\mu) + \sum_^ \theta_k \epsilon_,\\ \sigma_t^2 &= \alpha_0 + \sum_^ \alpha_k (X_-\mu_)^2 + \sum_^ \beta_k \sigma_^2. \end$

## 2 Fitting procedure based on the simulated data

We now show how to fit Copula-GARCH模型下的两资产期权定价 an ARMA(1,1)-GARCH(1,1) process to X (we remove the argument fixed.pars from the above specification for estimating these parameters):

Check the (standardized) Z , i.e., the pseudo-observations of the residuals Z :

Fit a $$t$$ copula to the standardized residuals Z . For the marginals, we also assume $$t$$ distributions but with different degrees of freedom; for simplicity, the estimation is omitted here.

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Date and time: Fri, 19 Aug 2022 16:46:59 GMT

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

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。

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