Density prediction of stock index returns using GARCH models: Frequentist or Bayesian estimation?

Using GARCH models for density prediction of stock index returns, a comparison is provided between frequentist and Bayesian estimation. No significant difference is found between qualities of whole density forecasts, whereas the Bayesian approach exhibits significantly better left-tail forecast accuracy.

► We compare frequentist and Bayesian density predictions of GARCH models. ► We test the overall density and the left tail using KLIC and censored likelihood. ► Bayesian estimation outperforms its frequentist counterpart.