Time series copulas for heteroskedastic data
提出了捕捉平稳异方差时间序列中序列依赖的参数连接函数，从一阶马尔可夫扩展到高阶和多元序列，并基于波动率代理推导出新的波动依赖度量，在汇率数据中比GARCH模型更准确地刻画边际分布并改进风险价值预测。
Summary We propose parametric copulas that capture serial dependence in stationary heteroskedastic time series. We suggest copulas for first‐order Markov series, and then extend them to higher orders and multivariate series. We derive the copula of a volatility proxy, based on which we propose new measures of volatility dependence, including co‐movement and spillover in multivariate series. In general, these depend upon the marginal distributions of the series. Using exchange rate returns, we show that the resulting copula models can capture their marginal distributions more accurately than univariate and multivariate generalized autoregressive conditional heteroskedasticity models, and produce more accurate value‐at‐risk forecasts.