Stock index returns' density prediction using GARCH models: Frequentist or Bayesian estimation?

L.F. Hoogerheide; D. Ardia; N. Corre

doi:10.1016/j.econlet.2012.03.026

Stock index returns' density prediction using GARCH models: Frequentist or Bayesian estimation?

L.F. Hoogerheide, D. Ardia, N. Corre

Econometrics and Data Science

Research output: Contribution to Journal › Article › Academic › peer-review

Abstract

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. © 2012 Elsevier B.V.

Original language	English
Pages (from-to)	322-325
Number of pages	4
Journal	Economics Letters
Volume	116
Issue number	3
DOIs	https://doi.org/10.1016/j.econlet.2012.03.026
Publication status	Published - 2012

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10.1016/j.econlet.2012.03.026

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@article{34079e9831e64a31bfbb6d485bed33ea,

title = "Stock index returns' density prediction using GARCH models: Frequentist or Bayesian estimation?",

abstract = "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. {\textcopyright} 2012 Elsevier B.V.",

author = "L.F. Hoogerheide and D. Ardia and N. Corre",

year = "2012",

doi = "10.1016/j.econlet.2012.03.026",

language = "English",

volume = "116",

pages = "322--325",

journal = "Economics Letters",

issn = "0165-1765",