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040 |aCaOODSP|beng|erda|cCaOODSP
0410 |aeng|beng|bfre
0861 |aFB3-5/2026-8E-PDF
1001 |aRodriguez Rondon, Gabriel, |eauthor.
24510|aEstimation and inference for stochastic volatility models with heavy-tailed distributions / |cGabriel Rodriguez Rondon, Jean-Marie Dufour, Md. Nazmul Ahsan.
264 1|a[Ottawa] : |bBank of Canada = Banque du Canada, |cMarch 6, 2026.
264 4|c©2026
300 |a1 online resource (47 pages) : |bgraphs.
336 |atext|btxt|2rdacontent
337 |acomputer|bc|2rdamedia
338 |aonline resource|bcr|2rdacarrier
4901 |aStaff working paper = |aDocument de travail du personnel, |x1701-9397 ; |v2026-8
500 |aCover title.
504 |aIncludes bibliographical references (pages 34-36).
5203 |a"Statistical inference-both estimation and testing-for stochastic volatility (SV) models is known to be challenging and computationally demanding. We propose simple and efficient estimators for SV models with conditionally heavy-tailed error distributions, particularly the Student's t and Generalized Exponential Distributions (GED). The estimators rely on a small set of moment conditions derived from ARMA-type representations of SV models, with an option to apply "winsorization" to improve stability and finite-sample performance. Except for the degrees-of-freedom parameter, closed-form expressions are available for all other parameters-extending-thus eliminating the need for numerical optimization or initial values. We derive the estimators' asymptotic distribution and show that, due to their analytical tractability, they support reliable-and even exact-simulation based inference via Monte Carlo bootstrap methods. We assess their performance through extensive simulations and demonstrate their practical relevance in financial return data, which strongly reject the normality assumption in favor of heavy-tailed models"--Abstract.
546 |aIncludes abstracts in English and French.
650 0|aMoments method (Statistics)
650 0|aMonte Carlo method.
650 0|aStochastic analysis.
650 0|aAsymptotic distribution (Probability theory)
650 6|aMéthodes des moments (Statistique)
650 6|aMéthode de Monte-Carlo.
650 6|aAnalyse stochastique.
650 6|aDistribution asymptotique (Théorie des probabilités)
655 7|aStatistics|2lcgft
655 7|aStatistiques|2rvmgf
7102 |aBank of Canada, |eissuing body.
830#0|aStaff working paper (Bank of Canada)|x1701-9397 ; |v2026-8.|w(CaOODSP)9.806221
85640|qPDF|s967 KB|uhttps://publications.gc.ca/collections/collection_2026/banque-bank-canada/FB3-5-2026-8-eng.pdf