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| Bayesiansk Dynamisk Betinget Korrelations-GARCH (Bayesiansk DCC-GARCH)× | Bayesiansk VAR-model (BVAR)× | |
|---|---|---|
| Fagområde | Økonometri | Økonometri |
| Familie | Regression model | Regression model |
| Oprindelsesår≠ | 2002 (DCC); 2000s (Bayesian extension) | 1984 |
| Ophavsperson≠ | Engle (2002) for DCC; Bayesian extension via MCMC literature (2000s onwards) | Doan, Litterman & Sims |
| Type≠ | Multivariate volatility model | Multivariate time-series model |
| Oprindelig kilde≠ | 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 ↗ | Doan, T., Litterman, R., & Sims, C. (1984). Forecasting and conditional projection using realistic prior distributions. Econometric Reviews, 3(1), 1–100. DOI ↗ |
| Aliasser | Bayesian DCC-GARCH, Bayesian Dynamic Conditional Correlation, MCMC DCC-GARCH, Bayesian multivariate volatility model | BVAR, Bayesian VAR, Bayesian vector autoregressive model, BVAR model |
| Relaterede≠ | 6 | 5 |
| Resumé≠ | Bayesian DCC-GARCH estimates time-varying correlations across multiple financial or economic series by combining Engle's DCC-GARCH structure with Bayesian inference. Rather than maximising a likelihood, it places prior distributions over all parameters and uses Markov Chain Monte Carlo (MCMC) sampling to produce full posterior distributions, yielding richer uncertainty quantification than classical DCC-GARCH. | The Bayesian Vector Autoregression (BVAR) model extends the classical VAR framework by incorporating prior beliefs about the model coefficients. Priors — most commonly the Minnesota prior — shrink VAR coefficients toward economically sensible values, dramatically reducing overfitting and improving out-of-sample forecast accuracy even when the number of variables is large. |
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