Quantile coherency networks of international stock markets

Baumöhl, Eduard; Shahzad, Syed Jawad Hussain

doi:10.1016/j.frl.2019.04.022

Cited by 53 publications

(13 citation statements)

References 17 publications

(19 reference statements)

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“…This represents a crucial shortcoming in the related literature as the sole focus on mean-based connectedness masks potential differences in the patterns and strength of information transmission across the lower, middle, and upper quantiles of return distributions, representing stress, tranquil, and bullish periods, respectively. As indicated in related studies, market linkages are stronger during bearish states than normal or bullish states (e.g., Aloui et al 2020 ; Shahzad et al 2018 ; Baumöhl and Shahzad 2019 ; Saeed et al 2020 ). Accordingly, a tail-based connectedness analysis emerges as a suitable alternative approach given its ability to provide more comprehensive information on how return connectedness occurs among the US equity sector indices in extreme market conditions such as the COVID-19 outbreak period.…”

Section: Introductionmentioning

confidence: 82%

Impact of the COVID-19 outbreak on the US equity sectors: Evidence from quantile return spillovers

et al. 2021

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The aim of this study is to examine the extreme return spillovers among the US stock market sectors in the light of the COVID-19 outbreak. To this end, we extend the now-traditional Diebold-Yilmaz spillover index to the quantiles domain by building networks of generalized forecast error variance decomposition of a quantile vector autoregressive model specifically for extreme returns. Notably, we control for common movements by using the overall stock market index as a common factor for all sectors and uncover the effect of the COVID-19 outbreak on the dynamics of the network. The results show that the network structure and spillovers differ considerably with respect to the market state. During stable times, the network shows a nice sectoral clustering structure which, however, changes dramatically for both adverse and beneficial market conditions constituting a highly connected network structure. The pandemic period itself shows an interesting restructuring of the network as the dominant clusters become more tightly connected while the rest of the network remains well separated. The sectoral topology thus has not collapsed into a unified market during the pandemic.

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Section: Introductionmentioning

confidence: 82%

Impact of the COVID-19 outbreak on the US equity sectors: Evidence from quantile return spillovers

et al. 2021

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“… 5 Investors exhibit different horizons due to the varying levels of their risk tolerance levels, investment objectives, different assimilation and absorption of information, and different institutional constraints ( Chakrabarty et al, 2015 ; Baumöhl and Shahzad, 2019 ; Maghyereh et al, 2018 , 2020 ). …”

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confidence: 99%

“… 9 The discussion and notation contained in this section followed Baruník and Kley (2019) ; Baumöhl and Shahzad (2019) ; and Balcilar et al (2016) . …”

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confidence: 99%

The tail dependence structure between investor sentiment and commodity markets

Maghyereh

Abdoh

2020

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A growing body of literature considers investor sentiment as the partial driver of change in commodity prices. In contrast with previous studies that have almost exclusively focused on linear relationship, this empirical paper investigates the entire dynamic dependence of the quantile of investor sentiment and that of ten important commodities. To do so, we use the novel quantile cross-spectral dependence approach of Baruník and Kley (2019) and the nonparametric causality-in-quantiles test proposed by Balcilar et al. (2017a) over the period 1998–2018. Overall, the results show that the inter-dependence between sentiment and commodity differs according to return quantile and time frequency.

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“…Studies based on this methodology lead to the conclusion that a statistically significant increase in the correlation between markets is an indicator of contagion (see, e.g., [1,10,31,32]). The GARCH model approach ( [33,34]), the DCC-GARCH model [35,36], the DCC-MGARCH model [11], the spillover index approach proposed by [37][38][39], the TVP-VAR approach [18], and the quantile crossspectral analysis proposed by [40,41] have also been applied to study the transmission of contagion from one market to another.…”

Section: Literature Reviewmentioning

confidence: 99%

Non-Linear Interdependencies between International Stock Markets: The Polish and Spanish Case

2020

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This research analyzes non-linear interdependencies between the Polish (WIG20) and the Spanish (IBEX 35) stock market returns with some other relevant international stock market returns, such as the German (DAX-30), the British (FTSE-100), the American (S&P 500) and the Chinese (SSE Composite) stock markets. In addition, this research focuses on the impact of the stage of the economy on these interdependencies, in concrete, on the influence of the 2008 Global Financial Crisis. To that end, we use a nonlinear autoregressive distributed lag (NARDL) approach in the sample period between January 1998 to December 2018. Our results show positive interdependencies between the Polish and the Spanish stock markets with the international reference stock markets analyzed in this research, as well as significant long-run relations between most of the stock markets. Furthermore, the Polish and the Spanish stock market returns may similarly react to positive and negative changes in international stock market returns, evidencing strong short-run asymmetry. In addition, both countries show great persistence in response to both positive and negative changes in stock market returns in the other mayor international markets. Finally, the NARDL model proposed in this research would show good explanatory power, mainly to changes in the international stock market returns, except for the Chinese market.

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Quantile coherency networks of international stock markets

Cited by 53 publications

References 17 publications

Impact of the COVID-19 outbreak on the US equity sectors: Evidence from quantile return spillovers

Impact of the COVID-19 outbreak on the US equity sectors: Evidence from quantile return spillovers

The tail dependence structure between investor sentiment and commodity markets

Non-Linear Interdependencies between International Stock Markets: The Polish and Spanish Case

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