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Unpublished Paper
Time Series Copulas for Heteroskedastic Data
MBS Working Paper (2017)
  • Michael S Smith
  • Worapree Maneesoonthorn, Melbourne Business School
  • Ruben Loaiza-Maya, Melbourne Business School
We propose parametric copulas that capture serial dependence in stationary heteroskedastic time series. We develop our copula for first order Markov series, and 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. 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 GARCH models, and produce more accurate value at risk forecasts.
  • Foreign Exchange Returns,
  • Mixture Copulas,
  • Multivariate Time Series,
  • Volatility Spillover and Comovement,
  • Value at Risk Forecasting
Publication Date
January 25, 2017
Citation Information
Michael S Smith, Worapree Maneesoonthorn and Ruben Loaiza-Maya. "Time Series Copulas for Heteroskedastic Data" MBS Working Paper (2017)
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