Multivariate FIAPARCH modelling of financial markets with dynamic correlations in times of crisis

Menelaos Karanasos*, Stavroula Yfanti, Michail Karoglou

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

This paper applies the vector AR-DCC-FIAPARCH model to eight national stock market indices' daily returns from 1988 to 2010, taking into account the structural breaks of each time series linked to the Asian and the recent Global financial crisis. We find significant cross effects, as well as long range volatility dependence, asymmetric volatility response to positive and negative shocks, and the power of returns that best fits the volatility pattern. One of the main findings of the model analysis is the higher dynamic correlations of the stock markets after a crisis event, which means increased contagion effects between the markets. The fact that during the crisis the conditional correlations remain on a high level indicates a continuous herding behaviour during these periods of increased market volatility. Finally, during the recent Global financial crisis the correlations remain on a much higher level than during the Asian financial crisis.

Original languageEnglish
Pages (from-to)332-349
JournalInternational Review of Financial Analysis
Volume45
Early online date16 Oct 2014
DOIs
Publication statusPublished - May 2016

Bibliographical note

© 2014 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).

Keywords

  • contagion effects
  • dynamic conditional correlation
  • financial crisis
  • GARCH
  • long memory
  • multivariate
  • structural breaks

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