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Regression modelEconometrics / time series

Bayesian Dynamic Conditional Correlation GARCH (Bayesian DCC-GARCH)

Bayesian DCC-GARCH inakadiria uhusiano unaobadilika kadiri muda unavyokwenda katika mfululizo mbalimbali wa kifedha au kiuchumi kwa kuchanganya muundo wa DCC-GARCH wa Engle na hitimisho la Bayesian. Badala ya kuongeza uwezekano, huweka usambazaji wa awali (prior distributions) juu ya vigezo vyote na hutumia sampuli ya Markov Chain Monte Carlo (MCMC) kutoa usambazaji kamili wa baada (posterior distributions), na hivyo kutoa kipimo kikubwa zaidi cha kutokuwa na uhakika kuliko DCC-GARCH ya kawaida.

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Vyanzo

  1. Engle, R. F. (2002). Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business and Economic Statistics, 20(3), 339-350. DOI: 10.1198/073500102288618487
  2. Virbickaite, A., Ausin, M. C., & Galeano, P. (2015). Bayesian inference methods for univariate and multivariate GARCH models: A survey. Journal of Economic Surveys, 29(1), 76-96. DOI: 10.1111/joes.12046

Jinsi ya kunukuu ukurasa huu

ScholarGate. (2026, June 3). Bayesian Dynamic Conditional Correlation GARCH Model. ScholarGate. https://scholargate.app/sw/econometrics/bayesian-dcc-garch

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ScholarGateBayesian DCC-GARCH (Bayesian Dynamic Conditional Correlation GARCH Model). Imepatikana 2026-06-15 kutoka https://scholargate.app/sw/econometrics/bayesian-dcc-garch · Seti ya data: https://doi.org/10.5281/zenodo.20539026