Linganisha mbinu

	Kielelezo cha GARCH cha Bayesian	Mchanganuo wa Kutokuwa na Utulivu wa Kimahesabu (Heston)
Nyanja	Ekonometriki	Fedha
Familia	Regression model	Regression model
Mwaka wa asili	1989–2000	1993
Mwanzilishi	Geweke (1989); further developed by Nakatsuma (2000) and Bauwens & Lubrano (1998)	Steven L. Heston
Aina	Bayesian volatility model	Continuous-time stochastic volatility model
Chanzo asilia	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.
Majina mbadala	Bayesian GARCH, BGARCH, GARCH with Bayesian inference, Bayesian volatility model	Heston model, SV model, continuous-time stochastic volatility, Stokastik Volatilite Modeli (Heston, SV)
Zinazohusiana	4	5
Muhtasari	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.