Is the correlation in international equity returns constant: 1960–1990?

Longin, François; Solnik, Bruno

doi:10.1016/0261-5606(94)00001-h

Cited by 1,539 publications

(821 citation statements)

References 44 publications

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“…A number of studies make use of the cointegration technique to test for contagion by determining the long-run relationship between markets in the presence of financial crises (Longin and Solnik, 1995;Fahami, 2011). For example, Fahami (2011) used this method to test the structure of linkages and the causal relationships between BRIC and other developed countries during the 2007 US subprime crisis.…”

Section: Introductionmentioning

confidence: 99%

Uncovering equity market contagion among BRICS countries: An application of the multivariate GARCH model

Bonga‐Bonga

2018

The Quarterly Review of Economics and Finance

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This paper assesses the extent of the transmission of financial shocks between South Africa and other members of the BRICS grouping in order to infer the degree of contagion during the period 1996-2012. The paper makes use of a multivariate VAR-DCC-GARCH model for this end. The paper finds evidence of cross-transmission and dependence between South Africa and Brazil. However, the empirical results show that South Africa is more affected by crises originating from China, India and Russia than these countries are by crises originating from South Africa. The findings of this paper should be of interest to policy makers in the BRICS grouping should they be considering the possibility of full capital market liberalization and to the international investor who is looking at diversifying portfolios in the BRICS grouping..

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Section: Introductionmentioning

confidence: 99%

Uncovering equity market contagion among BRICS countries: An application of the multivariate GARCH model

Bonga‐Bonga

2018

The Quarterly Review of Economics and Finance

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“…Economists have been studying the reasons why markets crash, and why there is propagation of volatility from one market to another, since a long time. After the crash of 1987, many studies have been published on transmision of volatility (contagion) between markets using econometric models [1]- [15], on how the correlation between world markets change with time [16]- [19], and how correlation tends to increase in times of high volatility [20]- [34]. This issue is of particular importance if one wishes to build portfolios of international assets which can withstand times of crisis [35]- [47].…”

Section: Introductionmentioning

confidence: 99%

Correlation of financial markets in times of crisis

Sandoval

Franca

2012

Physica A: Statistical Mechanics and its Applications

218

106

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Using the eigenvalues and eigenvectors of correlations matrices of some of the main financial market indices in the world, we show that high volatility of markets is directly linked with strong correlations between them. This means that markets tend to behave as one during great crashes. In order to do so, we investigate financial market crises that occurred in the years 1987 (Black Monday), 1998 (Russian crisis), 2001 (Burst of the dot-com bubble and September 11), and 2008 (Subprime Mortgage Crisis), which mark some of the largest downturns of financial markets in the last three decades.

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“…Tsui and Yu [297], adopting the Information Matrix test, examined the China stock market and found that the constant conditional correlation hypothesis is not supported. Longin and Solnik [214] rejected the hypothesis of constant conditional correlation in international equity returns against three alternative sources of variability of the correlation such as a time trend, the presence of threshold and asymmetry and the influence of information variables.…”

Section: Multivariate Arch Modelsmentioning

confidence: 99%

Autoregressive Conditional Heteroscedasticity (ARCH) Models: A Review

Degiannakis

Xekalaki

2004

Quality Technology & Quantitative Management

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Autoregressive Conditional Heteroscedasticity (ARCH) models have successfully been employed in order to predict asset return volatility. Predicting volatility is of great importance in pricing financial derivatives, selecting portfolios, measuring and managing investment risk more accurately. In this paper, a number of univariate and multivariate ARCH models, their estimating methods and the characteristics of financial time series, which are captured by volatility models, are presented. The number of possible conditional volatility formulations is vast. Therefore, a systematic presentation of the models that have been considered in the ARCH literature can be useful in guiding one's choice of a model for exploiting future volatility, with applications in financial markets.

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Is the correlation in international equity returns constant: 1960–1990?

Cited by 1,539 publications

References 44 publications

Uncovering equity market contagion among BRICS countries: An application of the multivariate GARCH model

Uncovering equity market contagion among BRICS countries: An application of the multivariate GARCH model

Correlation of financial markets in times of crisis

Autoregressive Conditional Heteroscedasticity (ARCH) Models: A Review

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