Stock Index Returns' Density Prediction using GARCH Models: Frequentist or Bayesian Estimation?
January 2011
Research Paper
This publication is part of collection
| Related Files |
|---|
View PDF Version
(2011-0204.pdf, 0.1MB) |
Using well-known GARCH models for density prediction of daily S&P 500 and Nikkei 225 index returns, a comparison is provided between frequentist and Bayesian estimation. No significant difference is found between the qualities of the forecasts of the whole density, whereas the Bayesian approach exhibits significantly better left-tail forecast accuracy.
Keywords
Classifications using
Journal of Economic Literature (JEL) Classification System
- C52 : Model Evaluation and Testing
- C53 : Forecasting and Other Model Applications
- C11 : Bayesian Analysis
Automatically Extracted Terms
- model
- density
- bayesian
- garch
- forecast
- frequentist
- bayesian approach
- approach
- density forecasts
- egarch
- yt +1
- garch models
- bayesian estimation
- return
- estimation
- distribution
- parameter
- accuracy
- value
- result
