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Bayes' EGARCH-mudel

Bayes' EGARCH-mudel ühendab Nelsoni (1991) eksponentsiaalse GARCH-spetsifikatsiooni – mis modelleerib tingliku dispersiooni logaritmi ja püüab kinni finantseerimise efekti – Bayes' järeldusliku inferentsiga Markovi ahelate Monte Carlo (MCMC) abil. See võimaldab täielikult kvantifitseerida kogu volatiilsusparameetrite ebakindluse, sealhulgas asümmeetriakoefitsiendi, ilma et oleks vaja hinnangute suurte valimite normaaljaotust.

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Allikad

  1. Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370. DOI: 10.2307/2938260
  2. Nakatsuma, T. (2000). Bayesian analysis of ARMA-GARCH models: A Markov chain sampling approach. Journal of Econometrics, 95(1), 57–69. DOI: 10.1016/S0304-4076(99)00029-9

Kuidas sellele lehele viidata

ScholarGate. (2026, June 3). Bayesian Exponential Generalized Autoregressive Conditional Heteroscedasticity Model. ScholarGate. https://scholargate.app/et/econometrics/bayesian-egarch

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Sellele viitavad

ScholarGateBayesian EGARCH (Bayesian Exponential Generalized Autoregressive Conditional Heteroscedasticity Model). Loetud 2026-06-15 aadressilt https://scholargate.app/et/econometrics/bayesian-egarch · Andmestik: https://doi.org/10.5281/zenodo.20539026