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Title: Time series copulas for heteroskedastic data Authors:  Ruben Loaiza-Maya - Monash University (Australia) [presenting]
Michael Smith - University of Melbourne (Australia)
Worapree Ole Maneesoonthorn - University of Melbourne (Australia)
Abstract: New parametric copulas are proposed that capture serial dependence in stationary heteroskedastic time series. We develop our copula for first order Markov series, and then extend it 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. Using daily exchange rate returns, we show that the copula models can both capture their marginal distributions more accurately than univariate and multivariate GARCH models, as well as produce more accurate value at risk forecasts.