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Journal ArticleDOI

Dynamic correlation analysis of financial contagion: Evidence from the Central and Eastern European markets☆

TL;DR: This article applied the Dynamic Conditional Correlation (DCC) multivariate GARCH model to examine the time-varying conditional correlations to the weekly index returns of seven emerging stock markets of Central and Eastern Europe.
About: This article is published in International Review of Economics & Finance.The article was published on 2011-10-01. It has received 353 citations till now. The article focuses on the topics: Financial contagion & Eastern european.
Citations
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Journal ArticleDOI
TL;DR: In this article, the authors employ a VAR-GARCH model to investigate the return links and volatility transmission between the S&P 500 and commodity price indices for energy, food, gold and beverages over the turbulent period from 2000 to 2011.

368 citations

Journal ArticleDOI
TL;DR: In this paper, the contagion effects of the global financial crisis in a multivariate Fractionally Integrated Asymmetric Power ARCH (FIAPARCH) dynamic conditional correlation (DCC) framework during the period 1997-2012 were investigated.

304 citations


Cites methods from "Dynamic correlation analysis of fin..."

  • ...Using a DCC–GARCH model, Syllignakis and Kouretas (2011) capture contagion effects among US and German stock markets and seven emerging Central and Eastern Europe markets....

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Journal ArticleDOI
TL;DR: In this article, the authors test the existence of financial contagion between foreign exchange markets of several emerging and developed countries during the U.S. subprime crisis and find that emerging markets seem to be the most influenced by the contagion effects.

203 citations

Journal ArticleDOI
TL;DR: In this paper, the contagion effects of GIPSI (Greece, Ireland, Portugal, Spain and Italy), USA, UK and Japan markets on BRIICKS (Brazil, Russia, India, Indonesia, China, South Korea and South Africa) stock markets were examined.

172 citations


Cites background from "Dynamic correlation analysis of fin..."

  • ...…which in general term defined as the spread of financial shocks from one country to others (see Ang and Bekaert, 1999; Chiang et al., 2007; Dooley and Hutchison, 2009; Forbes and Rigobon, 2002; Lessard, 1973; Longin and Solnik, 1995, 2001; Solnik, 1974; Syllignakis and Kouretas, 2011 etc.)....

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Journal ArticleDOI
TL;DR: In this paper, the authors used Chinese-developed data based on long-standing influenza indices, and the more recently developed coronavirus and face mask indices, to test for the presence of volatility spillovers from Chinese financial markets upon a broad number of traditional financial assets during the outbreak of the COVID-19 pandemic.

141 citations


Cites background from "Dynamic correlation analysis of fin..."

  • ...Much of this work has been guided by research relating to the direct effects of volatility transmission and contagion as measured through dynamic correlation analyses (Syllignakis and Kouretas (2011)) and broad behaviour through extreme financial market events....

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References
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Journal ArticleDOI
TL;DR: In this article, a new class of multivariate models called dynamic conditional correlation models is proposed, which have the flexibility of univariate generalized autoregressive conditional heteroskedasticity (GARCH) models coupled with parsimonious parametric models for the correlations.
Abstract: Time varying correlations are often estimated with multivariate generalized autoregressive conditional heteroskedasticity (GARCH) models that are linear in squares and cross products of the data. A new class of multivariate models called dynamic conditional correlation models is proposed. These have the flexibility of univariate GARCH models coupled with parsimonious parametric models for the correlations. They are not linear but can often be estimated very simply with univariate or two-step methods based on the likelihood function. It is shown that they perform well in a variety of situations and provide sensible empirical results.

5,695 citations


"Dynamic correlation analysis of fin..." refers background or methods in this paper

  • ...We conducted our analysis with the application of the Dynamic Conditional Correlation (DCC) multivariate GARCH models developed by Engle (2002)....

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  • ...In this paper we apply the multivariate GARCH model proposed by Engle (2002), to estimate dynamic conditional correlations (DCC) between the Central and Eastern European stock market returns and those of the US, Germany and Russia respectively....

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  • ...As proposed by Engle (2002), the log-likelihood of the estimators can be written as: 2 1 1 1 1 1 ( ) [( log(2 ) log ) (log )] (4) 2 T t t t t t t t t t t t t L n D D D R R...

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  • ...We conducted our analysis with the application of the Dynamic Conditional Correlation (DCC) multivariate GARCH models developed by Engle (2002). We then looked into the impact that the 1997-1998 Asian and...

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  • ...In this paper we apply the multivariate GARCH model proposed by Engle (2002), to estimate dynamic conditional correlations (DCC) between the Central and...

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Journal ArticleDOI
TL;DR: The authors showed that correlation coefficients are conditional on market volatility, and that there was virtually no increase in unconditional correlation coefficients (i.e., no contagion) during the 1997 Asian crisis, 1994 Mexican devaluation, and 1987 U.S. market crash.
Abstract: Heteroskedasticity biases tests for contagion based on correlation coefficients. When contagion is defined as a significant increase in market comovement after a shock to one country, previous work suggests contagion occurred during recent crises. This paper shows that correlation coefficients are conditional on market volatility. Under certain assumptions, it is possible to adjust for this bias. Using this adjustment, there was virtually no increase in unconditional correlation coefficients (i.e., no contagion) during the 1997 Asian crisis, 1994 Mexican devaluation, and 1987 U.S. market crash. There is a high level of market comovement in all periods, however, which we call interdependence.

3,389 citations


"Dynamic correlation analysis of fin..." refers background in this paper

  • ...1 Forbes and Rigobon (2002) define contagion as significant increases in cross market co-movement, while any continued high degree of market correlation suggests strong linkages between the two economies and is considered to be interdependence....

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Posted Content
TL;DR: In this article, the authors examined stock market co-movements and applied these concepts to test for stock market contagion during the 1997 East Asian crises, the 1994 Mexican peso collapse, and the 1987 U.S. stock market crash.
Abstract: This paper examines stock market co-movements. It begins with a discussion of several conceptual issues involved in measuring these movements and how to test for contagion. Standard tests examine if cross-market correlation in stock market returns increase during a period of crisis. The measure of cross-market correlations central to this standard analysis, however, is biased. The unadjusted correlation coefficient is conditional on market movements over the time period under consideration, so that during a period of turmoil when stock market volatility increases, standard estimates of cross-market correlations will be biased upward. It is straightforward to adjust the correlation coefficient to correct for this bias. The remainder of the paper applies these concepts to test for stock market contagion during the 1997 East Asian crises, the 1994 Mexican peso collapse, and the 1987 U.S. stock market crash. In each of these cases, tests based on the unadjusted correlation coefficients find evidence of contagion in several countries, while tests based on the adjusted coefficients find virtually no contagion. This suggests that high market co-movements during these periods were a continuation of strong cross-market linkages. In other words, during these three crises there was no contagion, only interdependence.

3,038 citations

Journal ArticleDOI
TL;DR: This article showed that correlation is not related to market volatility per se but to the market trend and that correlation increases in bear markets, but not in bull markets, and they also showed that the distribution of extreme correlation for a wide class of return distributions can be derived using extreme value theory.
Abstract: Testing the hypothesis that international equity market correlation increases in volatile times is a difficult exercise and misleading results have often been reported in the past because of a spurious relationship between correlation and volatility. Using “extreme value theory” to model the multivariate distribution tails, we derive the distribution of extreme correlation for a wide class of return distributions. Empirically, we reject the null hypothesis of multivariate normality for the negative tail, but not for the positive tail. We also find that correlation is not related to market volatility per se but to the market trend. Correlation increases in bear markets, but not in bull markets. INTERNATIONAL EQUITY MARKET CORRELATION has been widely studied. Previous studies 1 suggest that correlation is larger when focusing on large absolutevalue returns, and that this seems more important in bear markets. The conclusion that international correlation is much higher in periods of volatile markets ~large absolute returns! has indeed become part of the accepted wisdom among practitioners and the financial press. However, one should exert great care in testing such a proposition. The usual approach is to condition the estimated correlation on the observed ~or ex post! realization of market returns. Unfortunately correlation is a complex function of returns and such tests can lead to wrong conclusions, unless the null hypothesis and

2,204 citations


"Dynamic correlation analysis of fin..." refers background in this paper

  • ...According to Ang and Bekaert (1999) and Longin and Solnik (1995, 2001), correlations among market returns tend to decline in bull markets and to rise in bear markets....

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Journal ArticleDOI
TL;DR: In this article, the authors studied the correlation of monthly excess returns for seven major countries over the period 1960-90 and found that the international covariance and correlation matrices are unstable over time.

1,998 citations