A parsnip backend for GARCH models in the tidymodels framework.
Copula-GARCH模型下的两资产期权定价
Method for creating a Copula-GARCH fit object.
Usage
Arguments
A cGARCHspec A cGARCHspec object created by calling cgarchspec .
A multivariate xts data object or Copula-GARCH模型下的两资产期权定价 one which can be coerced to such.
A positive integer indicating the number of periods before the last to keep for out of sample forecasting.
Either “nlminb”, “solnp”, “gosolnp” or “lbfgs”. It can also optionally be a vector of length 2 with the first solver being used for the first stage univariate GARCH estimation (in which case the option of “hybrid” is also available).
Control arguments list passed to optimizer.
Control arguments passed to the fitting routine. The ‘eval.se’ option determines whether standard errors are calculated (see details below). The ‘scale’ option is for the first stage univariate Copula-GARCH模型下的两资产期权定价 GARCH fitting routine.
A cluster object created by calling makeCluster from the parallel package. If it is not NULL, then this will Copula-GARCH模型下的两资产期权定价 be used for parallel estimation (remember to stop the cluster on Copula-GARCH模型下的两资产期权定价 completion).
(optional) A previously estimated univariate uGARCHmultifit object (see details).
(optional) A previously estimated VAR list returned from calling the varxfit function.
If the spd transformation was chosen in the specification, the spd.control passes its arguments to the spdfit routine of Copula-GARCH模型下的两资产期权定价 the spd package.
Required xts matrix for the realGARCH model.
Details
The Copula-GARCH模型下的两资产期权定价 Copula-GARCH models implemented can either be time-varying of DCC variety else Copula-GARCH模型下的两资产期权定价 static. The multivariate Normal and Student distributions are used in the construction of the copulas, and 3 transformation methods are available (parametric, semi-parametric, and empirical). For the semi-parametric case the ‘spd’ package of the author is available to download from CRAN and fits a Gaussian kernel in the interior and gpd distribution for the tails (see that package for more details).
The static copula allows for the estimation of the correlation matrix either by Maximum Likelihood or Copula-GARCH模型下的两资产期权定价 the Kendall method for the multivariate Student.
Note that the ‘cgarchfit’ method will assign to the global environment the uGARCHmultifit once that is estimated in order to allow the routine to be restarted should something go wrong (it should show up as ‘.fitlist’).
Value
A cGARCHfit Object containing details of the Copula-GARCH fit.
There is no check on the VAR.fit list passed to the method so particular care should be exercised so that the same data used in the fitting routine is also used in the VAR fit routine. This must have been called with the option postpad ‘constant’. The ability to pass this list of the pre-calculated VAR model is particularly useful when comparing different models (such as DCC GARCH, GO GARCH etc) using the same dataset and VAR method (i.e. the same first stage conditional mean filtration). Though the Copula-GARCH模型下的两资产期权定价 classical VAR estimation is very fast and may not require this extra step, the robust method is slow and therefore benefits from calculating this only once.
For extensive examples look in the ‘rmgarch.Copula-GARCH模型下的两资产期权定价 tests’ folder.
garchmodels
A parsnip backend for GARCH models in the tidymodels framework.
Tutorials
Getting Started with Garchmodels: A walkthrough of the tidy modeling approach with the package.
Tuning Univariate Garch Models: Learn how to tune parameters of univariate garch models.
Installation
Why Garchmodels?
Garchmodels unlocks Copula-GARCH模型下的两资产期权定价 univariate and multivariate GARCH models in one framework.
In a single framework you will be able to find what you need:
Univariate Methods: garchmodels connects to the rugarch package.
Multivariate Methods: garchmodels connects to the rugarch and rmgarch packages. Available methods include DCC-Garch (Dynamic Conditional Correlation Garch), Copula Garch and GO-Garch models.
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README.md
Method of 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 calculating VaR ( Value at risk) using ARMA-GJR_GARCH and COPULA method
Value at Risk (VaR) is one of the most widely used risk measure in risk management. It is defined as the worst loss to be expected of a portfolio over a given time horizon at Copula-GARCH模型下的两资产期权定价 a given confidence level. We estimate portfolio VaR using an approach combin- ing Copula functions, Extreme Value Theory (EVT) and GARCH models. We apply this approach to a portfolio consisting of stock indices from CTG, MSN, VIC, VNM (Vietnam). To estimate the VaR of this portfolio, we first use an asymmetric GARCH model and an Copula-GARCH模型下的两资产期权定价 EVT method to model the marginal distributions of each log returns series and then use Copula functions (Gaussian, Student’s t, Clayton, Gumbel and Frank) to link the marginal distributions together into a multivariate distribution. We then use Monte Carlo Simulation (MCS) approach to find estimates of the portfolio VaR. To check the goodness of fit of the approach we use Backtesting methods. From the results, we conclude that, in general the GARCH-EVT-Copula approach performs well and specifically the GARCH-EVT-Student’s t Copula outperforms all other GARCH-EVT-Copulas and traditional methods such as Historical Simulation (HS) and Variance Covariance (VC).
Keywords: Value at Risk (VaR), Copula, GARCH, Extreme Value Theory (EVT), Backtesting.
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Method of calculating VaR ( Value at risk) using ARMA-GJR_GARCH and COPULA method
Copula-GARCH模型下的两资产期权定价
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Abstract
This paper minimizes the risk of Brent oil in a multivariate portfolio, with three risk-minimizing goals: variance, 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 Copula-GARCH模型下的两资产期权定价 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.