Bayesian estimation of the Gaussian mixture GARCH model
Computational Statistics & Data Analysis
Copula, marginal distributions and model selection: a Bayesian note
Statistics and Computing
Computational Statistics & Data Analysis
Efficient estimation of copula-GARCH models
Computational Statistics & Data Analysis
Computational Statistics & Data Analysis
Computational Statistics & Data Analysis
An Introduction to Copulas
Joint forecasts of Dow Jones stocks under general multivariate loss function
Computational Statistics & Data Analysis
Efficient Bayesian inference for stochastic time-varying copula models
Computational Statistics & Data Analysis
A review of copula models for economic time series
Journal of Multivariate Analysis
Modelling multi-output stochastic frontiers using copulas
Computational Statistics & Data Analysis
Vine copulas with asymmetric tail dependence and applications to financial return data
Computational Statistics & Data Analysis
| Hi-index | 0.03 |
The analysis of temporal dependence in multivariate time series is considered. The dependence structure between the marginal series is modelled through the use of copulas which, unlike the correlation matrix, give a complete description of the joint distribution. The parameters of the copula function vary through time, following certain evolution equations depending on their previous values and the historical data. The marginal time series follow standard univariate GARCH models. Full Bayesian inference is developed where the whole set of model parameters is estimated simultaneously. This represents an essential difference from previous approaches in the literature where the marginal and the copula parameters are estimated separately in two consecutive steps. Moreover, a Bayesian procedure is proposed for the estimation of several measures of risk, such as the variance, Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR) of a portfolio of assets, providing point estimates and predictive intervals. The proposed copula model enables to capture the dependence structure between the individual assets which strongly influences these risk measures. Finally, the problem of optimal portfolio selection based on the estimation of mean-variance, mean-VaR and mean-CVaR efficient frontiers is also addressed. The proposed approach is illustrated with simulated and real financial time series.