Comparar métodos

	Modelo GARCH Bayesiano	Modelo de Volatilidade Estocástica (Heston)
Área	Econometria	Finanças
Família	Regression model	Regression model
Ano de origem	1989–2000	1993
Autor original	Geweke (1989); further developed by Nakatsuma (2000) and Bauwens & Lubrano (1998)	Steven L. Heston
Tipo	Bayesian volatility model	Continuous-time stochastic volatility model
Fonte seminal	Geweke, J. (1989). Exact predictive densities for linear models with ARCH disturbances. Journal of Econometrics, 40(1), 63–86.	Heston, S. L. (1993). A Closed-Form Solution for Options with Stochastic Volatility with Applications to Bond and Currency Options. Review of Financial Studies, 6(2), 327-343.
Outros nomes	Bayesian GARCH, BGARCH, GARCH with Bayesian inference, Bayesian volatility model	Heston model, SV model, continuous-time stochastic volatility, Stokastik Volatilite Modeli (Heston, SV)
Relacionados	4	5
Resumo	The Bayesian GARCH model combines the GARCH framework for time-varying volatility with Bayesian posterior inference. Instead of maximising a likelihood, it specifies prior distributions for the GARCH parameters and draws from the resulting posterior — typically via Markov chain Monte Carlo (MCMC) — to quantify both point estimates and full uncertainty about volatility dynamics.	The stochastic volatility model is a continuous-time option-pricing and risk framework in which volatility follows its own random process rather than staying constant. The Heston model, introduced by Steven Heston in 1993, gives the variance a mean-reverting square-root (CIR) dynamic and yields a closed-form option price; it is the continuous-time counterpart of GARCH.