# Copula-GARCH模型下的两资产期权定价

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## Forecasting for DCC Copula GARCH model in R

I'm trying to forecast the Copula Garch Model. I have tried to use the dccforecast function with the cGARCHfit but it turns out to be error saying that there is Copula-GARCH模型下的两资产期权定价 no applicable method for 'dccforecast' applied to an object 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 Copula-GARCH模型下的两资产期权定价 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 Copula-GARCH模型下的两资产期权定价 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 Copula-GARCH模型下的两资产期权定价 values. The ‘rmgarch.tests’ folder contains >specific examples which illustrate 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 particular point.

## 1 模拟数据

B-GARCH(1，1)的Copula形式：

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## The Copula GARCH Model

In this 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 pointing this out).

## 1 Simulate data

First, Copula-GARCH模型下的两资产期权定价 we simulate the innovation distribution. Note that, for demonstration purposes, we choose a small sample size. Ideally, the sample size 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 Copula-GARCH模型下的两资产期权定价 &= \mu + \sum_^ \phi_k (X_-\mu) + \sum_^ \theta_k (X_-\mu_),\\ \sigma_t^2 &= \alpha_0 + \sum_^ \alpha_k (X_-\mu_)^2 + \sum_^ \beta_k \sigma_^2. \end

## 2 Fitting procedure based on the simulated Copula-GARCH模型下的两资产期权定价 data

We now show how to fit an ARMA(1,1)Copula-GARCH模型下的两资产期权定价 -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 Copula-GARCH模型下的两资产期权定价 estimation is omitted here.

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Date and Copula-GARCH模型下的两资产期权定价 time: Fri, 19 Aug 2022 16:47:10 GMT