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

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Multivariate FIAPARCH modelling of financial markets with dynamic correlations in times of crisis. / Karanasos, Menelaos; Yfanti, Stavroula; Karoglou, Michail.

In: International Review of Financial Analysis, Vol. 45, 05.2016, p. 332-349.

Research output: Contribution to journalJournal article

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Karanasos, M, Yfanti, S & Karoglou, M 2016, 'Multivariate FIAPARCH modelling of financial markets with dynamic correlations in times of crisis', International Review of Financial Analysis, vol. 45, pp. 332-349. https://doi.org/10.1016/j.irfa.2014.09.002

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Karanasos, Menelaos ; Yfanti, Stavroula ; Karoglou, Michail. / Multivariate FIAPARCH modelling of financial markets with dynamic correlations in times of crisis. In: International Review of Financial Analysis. 2016 ; Vol. 45. pp. 332-349.

Bibtex

@article{0c9b440f266d4df6ab23786b070fb0f7,
title = "Multivariate FIAPARCH modelling of financial markets with dynamic correlations in times of crisis",
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.",
keywords = "Contagion effects, Dynamic conditional correlation, Financial crisis, Long memory, Multivariate, GARCH, Structural breaks",
author = "Menelaos Karanasos and Stavroula Yfanti and Michail Karoglou",
year = "2016",
month = may
doi = "10.1016/j.irfa.2014.09.002",
language = "English",
volume = "45",
pages = "332--349",
journal = "International Review of Financial Analysis",
issn = "1057-5219",
publisher = "Elsevier Inc.",

}

RIS

TY - JOUR

T1 - Multivariate FIAPARCH modelling of financial markets with dynamic correlations in times of crisis

AU - Karanasos, Menelaos

AU - Yfanti, Stavroula

AU - Karoglou, Michail

PY - 2016/5

Y1 - 2016/5

N2 - 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.

AB - 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.

KW - Contagion effects

KW - Dynamic conditional correlation

KW - Financial crisis

KW - Long memory

KW - Multivariate

KW - GARCH

KW - Structural breaks

U2 - 10.1016/j.irfa.2014.09.002

DO - 10.1016/j.irfa.2014.09.002

M3 - Journal article

VL - 45

SP - 332

EP - 349

JO - International Review of Financial Analysis

JF - International Review of Financial Analysis

SN - 1057-5219

ER -