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

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journal contribution
posted on 20.07.2018 by Menelaos Karanasos, Stavroula Yfanti, Michail Karoglou
© 2014 The Authors. 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.

Funding

Open Access funded by European Research Council

History

School

  • Business and Economics

Department

  • Business

Published in

International Review of Financial Analysis

Volume

45

Pages

332 - 349

Citation

KARANASOS, M., YFANTI, S. and KAROGLOU, M., 2016. Multivariate FIAPARCH modelling of financial markets with dynamic correlations in times of crisis. International Review of Financial Analysis, 45, pp. 332-349.

Publisher

© The authors. Published by Elsevier

Version

VoR (Version of Record)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 Unported (CC BY-NC-ND 3.0) licence. Full details of this licence are available at: http://creativecommons.org/licenses/by-nc-nd/3.0/

Publication date

2016

Notes

This is an Open Access Article. It is published by Elsevier under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 Unported (CC BY-NC-ND 3.0) licence. Full details of this licence are available at: http://creativecommons.org/licenses/by-nc-nd/3.0/

ISSN

1057-5219

Language

en

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