Volatility Spillovers and Contagion from Mature to Emerging Stock Markets

Beirne, John; Caporale, GM; Schulze‐Ghattas, Marianne; Spagnolo, Nicola

doi:10.1111/roie.12091

Cited by 104 publications

(50 citation statements)

References 34 publications

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“…This infers existing evidence is also pertinent to forecast the future volatility for a long horizon as concluded in previous literature (e.g., Neaime 2016;Auer 2016;Guris, Sacildi 2016;Sarwar, Khan 2016;Beirne et al 2013;Wang et al 2005;Li, Majerowska 2008;etc.). The sum of GARCH coefficients for all the developed and emerging stock markets approaches to one that is the basic condition required for mean reversion process.…”

Section: Discussionsupporting

confidence: 87%

“…The research studies focusing on emerging equity markets such as, Neaime (2016), Auer (2016), Guris and Sacildi (2016), Sarwar and Khan (2016), Worthington and Higgs (2004), Wang et al (2005), Li and Majerowska (2008), Beirne et al (2013), andErb et al (1997). Among these studies, Wang et al (2005) conducted empirical analysis using EGARCH model found the confirmation of both returns and shock transmission from developed stock markets to emerging equity markets of the Asia.…”

Section: Theoretical Substantiationmentioning

confidence: 99%

“…Their investigation emerged with statistically significant findings confirming the existence of spillover effect of volatility and its influence on stock returns of the US and DAX equity markets. Beirne et al (2013) investigated the flow of shocks among newly emerged markets and developed markets. By using tri-variate GARCH model, they established the existence of clear substantiation regarding shock transmission from developed markets to emerging markets.…”

Section: Theoretical Substantiationmentioning

confidence: 99%

“…Several studies making developed equity markets as subject of interest such as: (e.g., Xiao, Dhesi 2010;Diebold, Yilmaz 2009;Harris, Pisedtasalasai 2006;Wagner, Szimayer 2004;Ng 2000;Chou et al 1999;Hu et al 1997;Li et al 2016;Cakici et al 2016;etc.). The research studies about developing and emerging equity markets have been concluded by Mirza et al (2016), Ghosh and Saggar (2016), Günay (2016), Worthington and Higgs (2004), Beirne et al (2013), Mukherjee and Mishra (2010), Choo et al (2011), Joshi (2011, Sakthivel et al (2012). These studies offered significant insights regarding assimilation among equity markets and offer capitalists efficient venture chances in various portfolios.…”

Section: Introductionmentioning

confidence: 99%

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Stock Returns, Volatility and Mean Reversion in Emerging and Developed Financial Markets

Ahmed

Vveinhardt

Štreimikienė

et al. 2018

Technological and Economic Development of Economy

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Abstract. The objective of this research is to measure and examine volatilities between important emerging and developed stock markets and to ascertain a relationship between volatilities and stock returns. This research paper also analyses the Mean reversion phenomenon in emerging and developed stock markets. For this purpose, seven emerging markets and five developed markets were considered. Descriptive statistics showed that the emerging markets have higher returns with the higher risk-return trade-off. In contrast, developed markets have low annual returns with a low risk-return trade-off. Correlation analysis indicated the significant positive correlation among the developed markets, but emerging and developed markets have shown relatively insignificant correlation. Results of ARCH and GARCH revealed that the value of likelihood statistics ratio is large, that entails the GARCH (1,1) model is a lucrative depiction of daily return pattern, that effectively and efficiently capturing the orderly reliance of volatility. The findings of the study showed that the estimate 'β' coefficients given in conditional variance equation are significantly higher than the 'α' , this state of affair entails that bigger market surprises tempt comparatively small revision in future volatility. Lastly, the diligence of the conditional variance estimated by α + β is significant and proximate to integrated GARCH (1,1) model, thus, this indicates, the existing evidence is also pertinent in order to forecast the future volatility. The results signified that the sum of GARCH (1,1) coefficients for all the equity returns' is less than 1 that is an important condition for mean reversion, as the sum gets closer to 1, hence the Mean reversion process gets slower for all the emerging and developed stock markets.

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Section: Discussionsupporting

confidence: 87%

Section: Theoretical Substantiationmentioning

confidence: 99%

Section: Theoretical Substantiationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Stock Returns, Volatility and Mean Reversion in Emerging and Developed Financial Markets

Ahmed

Vveinhardt

Štreimikienė

et al. 2018

Technological and Economic Development of Economy

View full text Add to dashboard Cite

show abstract

“…Similar to volatility, much of the recent research on volatility spillovers employs versions of the GARCH model (for example Beirne et al, 2013;Li and Giles, 2015;and Lin, 2013, among others). However, the ability to measure spillovers by those types of models is limited, namely in their lack of spillover dynamics.…”

Section: Online Firstmentioning

confidence: 99%

Survey of Volatility and Spillovers on Financial Markets

Kočenda¹

2018

Prague Economic Papers

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Abstractin this survey article, we present a rich extent of literature on volatility and its propagation on financial markets via spillovers. We document how new approaches or improved existing methodologies lead to results that offer richer insights than those derived from standard econometric techniques. moreover, the implications of the results can be related to a wide set of markets as the surveyed articles cover emerging and developed european markets as well as the United states.

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Volatility impulse response analysis for DCC‐GARCH models: The role of volatility transmission mechanisms

Gabauer

2020

Journal of Forecasting

107

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This study introduces volatility impulse response functions (VIRF) for dynamic conditional correlation–generalized autoregressive conditional heteroskedasticity (DCC‐GARCH) models. In addition, the implications with respect to network analysis—using the connectedness approach of Diebold and Y lmaz (Journal of Econometrics, 2014, 182(1), 119–134)—is discussed. The main advantages of this framework are (i) that the time‐varying dynamics do not underlie a rolling‐window approach and (ii) that it allows us to test whether the propagation mechanism is time varying or not. An empirical analysis on the volatility transmission mechanism across foreign exchange rate returns is illustrated. The results indicate that the Swiss franc and the euro are net transmitters of shocks, whereas the British pound and the Japanese yen are net volatility receivers of shocks. Finally, the findings suggest a high degree of comovement across European currencies, which has important portfolio and risk management implications.

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Volatility Spillovers and Contagion from Mature to Emerging Stock Markets

Abstract: 4Non-technical summary 5

Cited by 104 publications

References 34 publications

Stock Returns, Volatility and Mean Reversion in Emerging and Developed Financial Markets

Stock Returns, Volatility and Mean Reversion in Emerging and Developed Financial Markets

Survey of Volatility and Spillovers on Financial Markets

Volatility impulse response analysis for DCC‐GARCH models: The role of volatility transmission mechanisms

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