A global network topology of stock markets: Transmitters and receivers of spillover effects

Shahzad, Syed Jawad Hussain; Hernandez, Jose Areola; Rehman, Mobeen Ur; Al‐Yahyaee, Khamis Hamed; Zakaria, Muhammad

doi:10.1016/j.physa.2017.11.132

Cited by 53 publications

(17 citation statements)

References 49 publications

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“…Though our study is based on alternative energy markets from different regions, the results are not totally different from the studies employing conventional developed and emerging stock markets. For example, there are many studies that report diversification benefits based on low integration level between emerging and developed markets (see Rehman 2017, 2019;Shahzad et al 2018;Mensi et al 2017;Gupta and Guidi 2012). Therefore, despite of sampling various alternative energy markets, our study remains silent on the diversification benefits associated with traditional developed and emerging stock markets and hence, presents a future avenue of research.…”

Section: Resultsmentioning

confidence: 98%

Dynamic correlation pattern amongst alternative energy market for diversification opportunities

Rehman

2020

Economic Structures

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Last couple of decades witnessed recognition of energy markets as investment commodities which led interest of the international investment community. We investigate the potential of globally diverse alternative energy markets for optimal returns by analysing their correlation pattern. Our study employs daily data spanning from January 2006 to December 2017. To estimate pair-wise return co-movement, we employ rolling window multiple wavelet correlation based on decomposed returns using maximal overlap discrete wavelet transformation to infer implications for both short- and long-run investors. We witness maximum diversification between developed (World, Developed, EU, G7) and emerging (BRIC, Emerging) markets. Most of these combinations exhibit no traces of contagion during the financially and economically turbulent periods. Finally, we use non-linear causality test to highlight increased integration between our sampled alternative energy indices after financial and economic crises periods. Our results carry implications for short- and long-run investors as well as for policy makers.

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Section: Resultsmentioning

confidence: 98%

Dynamic correlation pattern amongst alternative energy market for diversification opportunities

Rehman

2020

Economic Structures

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“…Generally, building network connectedness among price returns and volatility is hardly new in conventional assets such as equities (e.g., Fowowe and Shuaibu, 2016;Shahzad et al, 2018) and bonds (Louzis, 2015;Ahmad et al, 2018). Interestingly, it helps in understanding stress periods (i.e.…”

Section: Introductionmentioning

confidence: 99%

“…Prior studies have uncovered the network of connectedness among and within different assets/markets that include equities (Fowowe and Shuaibu, 2016;Shahzad et al, 2018;, bonds (Louzis, 2015;Ahmad et al, 2018), currencies (Baruní k, et al, 2017;Singh et al, 2018), commodities (Ji et al, 2018a & b;Zhang and Broadstock, 2018), and interest rates (Louzis, 2015). Generally, empirical evidence suggests that connectedness in both return and volatility is significant, time-varying, and is shaped by crisis periods (Shahzad et al, 2018;Zhang and Broadstock, 2018). Importantly, the related literature often finds that the largest stock market such as the US is the largest transmitter of shocks to the stock markets of developed and emerging markets (e.g., Candelon et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Dynamic connectedness and integration in cryptocurrency markets

Bouri

Lau

et al. 2019

International Review of Financial Analysis

450

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This study applies a set of measures developed by Diebold and Yilmaz (2012, 2016) to examine connectedness via return and volatility spillovers across six large cryptocurrencies from August 7, 2015 to February 22, 2018. Regardless of the sign of returns, the results show that Litecoin is at the centre of the connected network of returns, followed by the largest cryptocurrency, Bitcoin. This finding implies that return shocks arising from these two cryptocurrencies have the most effect on other cryptocurrencies. Further analysis shows that connectedness via negative returns is largely stronger than via positive ones. Ripple and Ethereum are the top recipients of negative-return shocks, whereas Ethereum and Dash exhibit very weak connectedness via positive returns. Regarding volatility spillovers, Bitcoin is the most influential, followed by Litecoin; Dash exhibits a very weak connectedness, suggesting its utility for hedging and diversification opportunities in the cryptocurrency market. Taken together, results imply that the importance of each cryptocurrency in return and volatility connectedness is not necessarily related to its market size. Further analyses reveal that trading volume and global financial and uncertainty effects as well as the investment-substitution effect are determinants of net directional spillovers. Interestingly, higher gold prices and US uncertainty increase the net directional negative-return spillovers, whereas they do the opposite for net directional positive-return spillovers. Furthermore, gold prices exhibit a negative sign for net directional-volatility spillovers, whereas US uncertainty shows a positive sign. Economic actors interested in the cryptocurrency market can build on our findings when weighing their decisions.

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“…Specifically, leptokurtic and skewed distributions 2 of stock market returns show that the underlying dependence structure varies across the distribution, making traditional approaches restrictive and less precise. Shahzad et al (2018) argues that the traditional approaches may not accurately measure the interdependence in the bearish and bullish market states, because the normality assumption in the joint distribution is not met. It is also important to note that connectedness between international stocks markets may vary across frequencies due to the heterogeneity of multiple agents interacting in these markets.…”

Section: Introductionmentioning

confidence: 99%

Quantile coherency networks of international stock markets

Baumöhl

Shahzad

2019

Finance Research Letters

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This paper uses the novel quantile coherency approach to examine the tail dependence network of 49 international stock markets in the frequency domain. We find that geographical proximity and state of market development are important factors in stock markets networks. Both the short-and long-run connectedness significantly increased after the global financial crisis and spillover is higher during bearish market states, highlighting the possibility of contagion effect mainly among developed markets. Frontier and emerging markets are relatively less connected. These findings have implications for international equity market diversification and risk management.

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A global network topology of stock markets: Transmitters and receivers of spillover effects

Cited by 53 publications

References 49 publications

Dynamic correlation pattern amongst alternative energy market for diversification opportunities

Dynamic correlation pattern amongst alternative energy market for diversification opportunities

Dynamic connectedness and integration in cryptocurrency markets

Quantile coherency networks of international stock markets

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