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

Karanasos, Menelaos; Yfanti, Stavroula; Karoglou, Michail

doi:10.1016/j.irfa.2014.09.002

Cited by 40 publications

(21 citation statements)

References 38 publications

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“…Our empirical results are in parallel with the existing literature; some studies document a significant increase in the cross-market correlations during the GFC (Syllignakis and Kouretas, 2011;Dimitriou et al, 2013;Bekiros, 2014;Karanasos et al, 2014). As for the ESDC, we find the continued increase in the correlation paths due to the possible crisis spillover effects from the US to the Eurozone, which is partially in line with the limited literature on the contagion effects of the ESDC (see Ahmad et al, 2013).…”

Section: Resultssupporting

confidence: 89%

“…Since, as postulated by Cho and Parhizgari () and Karanasos et al . (), DCC‐based models estimate volatility adjusted correlations which account for dynamic behavior of the volatility pattern. We perform t ‐test in difference of the means of the correlations generated from the ADCC model to detect any significant increase in crisis periods…”

Section: Resultsmentioning

confidence: 99%

“…In this paper, we directly use ADCC coefficients instead of the adjusted correlations. Since, as postulated by Cho and Parhizgari (2008) and Karanasos et al (2014), DCC-based models estimate volatility adjusted correlations which account for dynamic behavior of the 14 According to the report Financial globalization: Retreat or reset? prepared by McKinsey Global Institute, global cross-border capital flows fell sharply from the peak of $11.8 trillion in 2007 to $4.6 trillion as of 2012, which corresponds to 60 per cent below the peak, for the complete report, see Lund et al (2013).…”

Section: Behavior Of the Conditional Correlations During The Crisesmentioning

confidence: 99%

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How Has the Behavior of Cross‐Market Correlations Altered During Financial and Debt Crises?

Demiralay

Ulusoy

2016

The Manchester School

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In this paper, we analyze the time‐varying behavior of cross‐market correlations between emerging and developed markets. For this, we conduct the Asymmetric Dynamic Conditional Correlation ‐EGARCH model, which captures asymmetries both in the conditional variances and correlations. Our main results suggest the upward patterns of correlations, reflecting the increased equity market integration over time, and also provide that asymmetric behavior in the correlations is not as common as in the volatilities. We further analyze time‐paths of the correlations during the two latest crises; namely global financial crisis (GFC) and European Sovereign Debt Crisis to investigate whether the crises induce contagion effects and significant structural shifts. The empirical findings reveal that the correlation levels significantly increase and the emerging markets recouple in the latest two turmoil episodes, providing evidence of contagion incidences. Moreover, we examine whether the dynamic correlations possess abrupt changes in times of the crisis via multiple structural breakpoint tests and demonstrate that substantial regime shifts in the conditional correlations are present during both the GFC and the ESDC. Our results provide insights and have potential implications for global investors to optimize their portfolios as well as for authorities to handle contagion risk and prevent its adverse impacts.

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Section: Resultssupporting

confidence: 89%

Section: Resultsmentioning

confidence: 99%

Section: Behavior Of the Conditional Correlations During The Crisesmentioning

confidence: 99%

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How Has the Behavior of Cross‐Market Correlations Altered During Financial and Debt Crises?

Demiralay

Ulusoy

2016

The Manchester School

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“…Last global financial crisis has proved the growing influence of "financisation" of economy on the situation on labour markets (Glazar and Strielkowski, Michał Bernard Pietrzak, Marcin Fałdziński, Adam P. Balcerzak, Tomáš Meluzín, Marek Zinecker Müller-Frączek and Pietrzak, 2011;Pietrzak and Balcerzak, 2016b), macroeconomic fiscal stability (Hadaś-Dyduch, 2015;Miklaszewicz, 2016;Majerová, 2016;Lajtkepová, 2016; and sustainability of whole economies (Pietrzak et al, 2014;Pietrzak and Balcerzak, 2016c;Balcerzak and Pietrzak, 2016b). As a result, identification of international linkages among markets and analysis of their variability over time is the condition for effective management of risk associated to potential negative influence of global capital markets (Arshanapalli et al, 1995;Andersen et al, 1999;Baur, 2003;Pericoli and Sbracia, 2003;Engle, 2009;Billio and Caporin, 2010;Syllignakis and Kouretas, 2011;Baek and Jun, 2011;Karanasos et al, 2014;Reboredo et al, 2015;Heryán and Ziegelbauer, 2016;Gawrońska-Nowak and Grabowski, 2016). It is of great importance both for policy makers at macroeconomic level and managers operating in international environment from microeconomic perspective.…”

Section: Introductionmentioning

confidence: 99%

Short-term Shocks and Long-term Relationships of Interdependencies Among Central European Capital Markets

Pietrzak¹,

Fałdziński²,

Balcerzak³

et al. 2017

Economics &Amp; Sociology

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ABSTRACT. The article focuses on the problem of interdependences among Central European capital markets. The main aim of this research is to identify longterm interdependences among Austrian, Czech, Hungarian and Polish capital markets and the market of Germany. Additionally, the impact of short-term shocks on these markets is under evaluation. In the first step of the research the interdependencies among the capital markets in the years 1997-2015 were verified. For this purpose the DCC-GARCH model with the conditional t-distribution was used. In the second step, an analysis of cointegration for the interdependencies among the markets was carried out. The authors proposed to include conditional variances of the analysed markets as additional explanatory variables in the cointegration analysis. As the conditional variance most often reflects the impact of short-term shocks, the proposed approach allowed to take into account shortterm market shocks in the cointegration analysis. The results enabled to identify long-term path for the course of the interdependences among markets of Germany, Austria, Czech Republic, Hungary and Poland. The mentioned Central European capital markets make a group of markets characterized with similar long-term path, which are focused around the dominant market of Germany.

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“…Following and Rittler (2012), we impose the UEDCC-AGARCH(1, 1) structure on the conditional variances (multivariate fractionally integrated APARCH models could also be used, as in Conrad et al, 2011or Karanasos et al, 2014, and we also amend it by allowing the shock and volatility spillovers parameters to be time varying:…”

Section: Bivariate Modelsmentioning

confidence: 99%

Modelling Returns and Volatilities During Financial Crises: A Time Varying Coefficient Approach

et al. 2014

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We examine how the most prevalent stochastic properties of key financial time series have been affected during the recent financial crises. In particular we focus on changes associated with the remarkable economic events of the last two decades in the mean and volatility dynamics, including the underlying volatility persistence and volatility spillovers structure. Using daily data from several key stock market indices we find that stock market returns exhibit time varying persistence in their corresponding conditional variances. Furthermore, the results of our bivariate GARCH models show the existence of time varying correlations as well as time varying shock and volatility spillovers between the returns of FTSE and DAX, and those of NIKKEI and Hang Seng, which became more prominent during the recent financial crisis. Our theoretical considerations on the time varying model which provides the platform upon which we integrate our multifaceted empirical approaches are also of independent interest. In particular, we provide the general solution for low order time varying specifications, which is a long standing research topic. This enables us to characterize these models by deriving, first, their multistep ahead predictors, second, the first two time varying unconditional moments, and third, their covariance structure. for suggestions and comments on a closely related work (see , which greatly improved many aspects of the current paper as well. We are grateful to seminar participants at

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

Cited by 40 publications

References 38 publications

How Has the Behavior of Cross‐Market Correlations Altered During Financial and Debt Crises?

How Has the Behavior of Cross‐Market Correlations Altered During Financial and Debt Crises?

Short-term Shocks and Long-term Relationships of Interdependencies Among Central European Capital Markets

Modelling Returns and Volatilities During Financial Crises: A Time Varying Coefficient Approach

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