Asymmetric connectedness on the U.S. stock market: Bad and good volatility spillovers

Baruník, Jozef; Kočenda, Evžen; Vácha, Lukáš

doi:10.1016/j.finmar.2015.09.003

Cited by 356 publications

(186 citation statements)

References 70 publications

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“…The research studies focusing on developing countries' markets documented their empirical findings regarding confirmation of volatility transmission and its impact on stock returns (e.g., Borland 2016; Baruník et al 2016;Singh et al 2016;Sakthivel et al 2012;Choo et al 2011;Mukherjee, Mishra 2010;Joshi 2011;etc.). Mukherjee and Mishra (2010) have carried out an important study; they took 12 equity markets of the Asia, and major financial markets of the India.…”

Section: Theoretical Substantiationmentioning

confidence: 99%

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: Theoretical Substantiationmentioning

confidence: 99%

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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show abstract

“…Baruník et al (2016Baruník et al ( , 2015 extend the spillover index methodology of Dieboled andYilmaz (2009, 2012) by employing the concept of realized semivariances from Barndorff-Nielsen et al (2010). This new approach enables to account for asymmetries in volatility spillovers.…”

Section: Asymmetries In Volatility Spilloversmentioning

confidence: 99%

“…In order to better quantify the extent of volatility spillovers, Baruník et al (2015Baruník et al ( , 2016 suggest to employ realized semivariances to compute the DY indices in a way that would distinguish asymmetries in the volatility source and the extent of their propagation in terms of volatility spillovers. They introduce a spillover asymmetry measure (SAM) that is defined as the difference between negative and positive spillovers:…”

Section: Asymmetries In Volatility Spilloversmentioning

confidence: 99%

“…This new approach effectively enables accounting for dynamics of the asymmetries in volatility spillovers (time subscripts are omitted). As in the case of the DY index, directional and net effects are available as well (for details see Baruník et al, 2016).…”

Section: Asymmetries In Volatility Spilloversmentioning

confidence: 99%

“…First, Baruník et al (2016) employ it for the analysis of individual U.S. stocks and detect ample asymmetric connectedness at the sectoral…”

Section: Asymmetries In Volatility Spilloversmentioning

confidence: 99%

See 2 more Smart Citations

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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Forecasting realized volatility of oil futures market: A new insight

Wei

Liu

et al. 2018

Journal of Forecasting

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In this study we propose several new variables, such as continuous realized semi‐variance and signed jump variations including jump tests, and construct a new heterogeneous autoregressive model for realized volatility models to investigate the impacts that those new variables have on forecasting oil price volatility. In‐sample results indicate that past negative returns have greater effects on future volatility than that of positive returns, and our new signed jump variations have a significantly negative influence on the future volatility. Out‐of‐sample empirical results with several robust checks demonstrate that our proposed models can not only obtain better performance in forecasting volatility but also garner larger economic values than can the existing models discussed in this paper.

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Asymmetric connectedness on the U.S. stock market: Bad and good volatility spillovers

Cited by 356 publications

References 70 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

Forecasting realized volatility of oil futures market: A new insight

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