@article {8915,
	title = {A novel copula-based approach for parametric estimation of univariate time series through its covariance decay},
	volume = {65},
	year = {2024},
	note = {n/a},
	month = {2024},
	pages = {1041 - 1063},
	abstract = {<p>In this note we develop a new technique for parameter estimation of univariate time series by means of a parametric copula approach. The proposed methodology is based on a relationship between a process{\textquoteright} covariance decay and parametric bivariate copulas associated to lagged variables. This relationship provides a way for estimating parameters that are identifiable through the process{\textquoteright} covariance decay, such as in long range dependent processes. We provide a rigorous asymptotic theory for the proposed estimator. We also present a Monte Carlo simulation study to asses the finite sample performance of the proposed estimator.</p>
},
	isbn = {1613-9798},
	url = {https://doi.org/10.1007/s00362-023-01418-z},
	author = {Pumi, Guilherme and Prass, Taiane S. and Lopes, S{\'\i}lvia R.C.}
}
