SEMIFARMA-HYGARCH Modeling of Dow Jones Return Persistence
type: Dataset
description: This paper analyzes the cyclical behavior of Dow Jones by testing the existence of long memory through a new class of semiparametric ARFIMA models with HYGARCH errors (SEMIFARMA-HYGARCH); this class includes nonparametric deterministic trend, stochastic trend, short-range and long-range dependence and long memory heteroscedastic errors. We study the daily returns of the Dow Jones from 1896 to 2006. We estimate several models and we find that the coefficients of the SEMIFARMA-HYGARCH model, including long memory coefficients for the equations of the mean and the conditional variance, are highly significant. The forecasting results show that the informational shocks have permanent effects on volatility and the SEMIFARMA-HYGARCH model has better performance over some other models for long and/or short horizons. The predictions from this model are also better than the predictions of the random walk model; accordingly, the weak efficiency assumption of financial markets seems violated for Dow Jones returns studied over a long period.
landing page: https://shs.hal.science/halshs-00793203
keyword: hygarch-model
keyword: infoeu-reposemanticspreprint
keyword: kernel-methodology
keyword: long-memory
keyword: nonparametric-deterministic-trend
keyword: preprints-working-papers-
keyword: semifarma-model
keyword: shsecohumanities-and-social-scienceseconomics-and-finance
issued: 2026-05-14T05:51:42.148145
modified: 2026-05-14T05:51:42.148151
language: en
contact point: Organization
  fn: CCSD

test_moissonnage_selune
type: Agent

SEMIFARMA-HYGARCH Modeling of Dow Jones Return Persistence
type: Distribution
format: HTML
access URL: https://shs.hal.science/halshs-00793203
issued: 2026-05-14T05:51:42.156810
modified: 2026-05-14T05:51:42.133161