Numerical recipes: the art of scientific computing
Numerical recipes: the art of scientific computing
Numerical methods of statistics
Numerical methods of statistics
Analytical Derivates of the APARCH Model
Computational Economics
Computational Statistics & Data Analysis
Efficient estimation of a semiparametric dynamic copula model
Computational Statistics & Data Analysis
Joint forecasts of Dow Jones stocks under general multivariate loss function
Computational Statistics & Data Analysis
Time-varying joint distribution through copulas
Computational Statistics & Data Analysis
Efficient maximum likelihood estimation of copula based meta t-distributions
Computational Statistics & Data Analysis
Efficient Bayesian inference for stochastic time-varying copula models
Computational Statistics & Data Analysis
Vine copulas with asymmetric tail dependence and applications to financial return data
Computational Statistics & Data Analysis
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An iterative (fixed-point) algorithm for the maximum-likelihood estimation of copula-based models that circumvents the need to compute second-order derivatives of the full likelihood function is adapted and examined. The algorithm exploits the structure of copula-based models that yield a natural decomposition of a potentially complicated likelihood function into two parts. The first part is a working likelihood that only involves the parameters of the marginals and the residual part is used to update estimates from the first part. A modified algorithm based on a working likelihood that accounts for some degree of correlation between the marginals is proposed. Compared to the original algorithm based on the working likelihood with the independent correlation, the modified one provides a better approximation to the full likelihood and overcomes convergence difficulties. A numerical example illustrates the efficiency gains of the estimation algorithms in the context of a benchmark copula-GARCH model. The modified algorithm is illustrated by an application to daily returns on two major stock market indices.