How dynamic copula capture time varying dependency?
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Dynamic copulas capture time-varying dependency by incorporating temporal patterns and modeling the changing relationships between variables over time. These copulas use various techniques such as autoregressive processes, filtration, and nonparametric smoothing to capture the temporal dependence of random variables. They allow for the estimation of copula parameters that change with time, enabling a more accurate representation of the evolving dependence structure. The use of time-varying copulas has been shown to improve the forecasting of volatility and dependency between different assets, including cryptocurrencies and fiat currencies . They also provide accurate risk measures for portfolios that include cryptocurrencies . Time-varying copulas have been applied to various financial time series, such as crude oil and natural gas return rates, and have demonstrated superior performance compared to time-invariant copulas . These models allow for flexible and accurate modeling of time-varying dependence patterns in multivariate time series .
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21 Citations | The paper proposes a model that captures time-varying dependency by first addressing the temporal patterns in each series and then modeling the interdependencies among their innovations using a time-varying vine copula model. |
| The paper discusses time-varying copulas, which have parameters that change over time based on lagged variables and past observations, allowing them to capture time-varying dependency. | |
01 Mar 2021 | The paper explains that a time-varying copula captures time-varying dependency by incorporating a function of a latent variable following an autoregressive process, which represents the influence of previous parameters and the relationship between marginal variables. |
4 Citations | The paper proposes a copula-based time series model that captures time-varying dependency by removing nonstationarity via filtration and then capturing nonlinear temporal dependence using a flexible Markov copula. |
| The paper uses Generalized Autoregressive Score (GAS) time-varying copulas to capture the time-varying dependency between cryptocurrencies and major fiat currencies. |
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