Academic Journal
Nonparametric Autoregressive Copula Forecasting via Boundary-Reflected Kernel Estimation.
| Τίτλος: | Nonparametric Autoregressive Copula Forecasting via Boundary-Reflected Kernel Estimation. |
|---|---|
| Συγγραφείς: | Colombo Soares, Guilherme1 (AUTHOR), Poletti Laurini, Márcio1 (AUTHOR) laurini@fearp.usp.br |
| Πηγή: | Econometrics (2225-1146). Jun2026, Vol. 14 Issue 2, p17. 26p. |
| Θεματικοί όροι: | *Forecasting, *Statistical correlation, Probability density function |
| Περίληψη: | We propose a fully nonparametric empirical autoregressive copula framework for univariate time series, designed to capture nonlinear and asymmetric serial dependence while exactly preserving the empirical marginal distribution. The method decouples marginal behavior from temporal dependence by (i) constructing a shape-preserving empirical marginal via monotone interpolation and mapping observations to the unit interval, and (ii) estimating the lag–lead dependence through a nonparametric conditional AR(1) copula density on (0 , 1) 2 . To ensure stable estimation near the boundaries, we employ reflection-based kernel methods that mitigate edge effects and yield well-behaved conditional densities on the unit support. Forecasts are obtained from the implied conditional predictive density: we compute point forecasts either as conditional modes (maximum a posteriori) on the copula scale or as conditional means, and then back-transform exactly using the empirical quantile function, guaranteeing marginal fidelity and support-respecting predictions. Empirically, we evaluate the approach on three CBOE volatility indices (VIX, VXD, and RVX) and benchmark it against linear ARMA models, copula-based parametric competitors, and state-space/heteroskedasticity baselines (Local level, TVP–AR, and ARMA–GARCH). The results highlight that modeling the full conditional transition density nonparametrically can deliver competitive—often best or near-best—forecast accuracy across horizons, particularly in the presence of pronounced volatility regimes and asymmetric adjustments. [ABSTRACT FROM AUTHOR] |
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| Βάση Δεδομένων: | Business Source Index |
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| Items | – Name: Title Label: Title Group: Ti Data: Nonparametric Autoregressive Copula Forecasting via Boundary-Reflected Kernel Estimation. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Colombo+Soares%2C+Guilherme%22">Colombo Soares, Guilherme</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Poletti+Laurini%2C+Márcio%22">Poletti Laurini, Márcio</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> laurini@fearp.usp.br</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Econometrics+%282225-1146%29%22">Econometrics (2225-1146)</searchLink>. Jun2026, Vol. 14 Issue 2, p17. 26p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Forecasting%22">Forecasting</searchLink><br />*<searchLink fieldCode="DE" term="%22Statistical+correlation%22">Statistical correlation</searchLink><br /><searchLink fieldCode="DE" term="%22Probability+density+function%22">Probability density function</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: We propose a fully nonparametric empirical autoregressive copula framework for univariate time series, designed to capture nonlinear and asymmetric serial dependence while exactly preserving the empirical marginal distribution. The method decouples marginal behavior from temporal dependence by (i) constructing a shape-preserving empirical marginal via monotone interpolation and mapping observations to the unit interval, and (ii) estimating the lag–lead dependence through a nonparametric conditional AR(1) copula density on (0 , 1) 2 . To ensure stable estimation near the boundaries, we employ reflection-based kernel methods that mitigate edge effects and yield well-behaved conditional densities on the unit support. Forecasts are obtained from the implied conditional predictive density: we compute point forecasts either as conditional modes (maximum a posteriori) on the copula scale or as conditional means, and then back-transform exactly using the empirical quantile function, guaranteeing marginal fidelity and support-respecting predictions. Empirically, we evaluate the approach on three CBOE volatility indices (VIX, VXD, and RVX) and benchmark it against linear ARMA models, copula-based parametric competitors, and state-space/heteroskedasticity baselines (Local level, TVP–AR, and ARMA–GARCH). The results highlight that modeling the full conditional transition density nonparametrically can deliver competitive—often best or near-best—forecast accuracy across horizons, particularly in the presence of pronounced volatility regimes and asymmetric adjustments. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Econometrics (2225-1146) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/econometrics14020017 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 26 StartPage: 17 Subjects: – SubjectFull: Forecasting Type: general – SubjectFull: Statistical correlation Type: general – SubjectFull: Probability density function Type: general Titles: – TitleFull: Nonparametric Autoregressive Copula Forecasting via Boundary-Reflected Kernel Estimation. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Colombo Soares, Guilherme – PersonEntity: Name: NameFull: Poletti Laurini, Márcio IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 22251146 Numbering: – Type: volume Value: 14 – Type: issue Value: 2 Titles: – TitleFull: Econometrics (2225-1146) Type: main |
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