Dynamic connectedness among regional FinTech indices in times of turbulences

Alshater, Muneer M.; Polat, Onur; Khoury, Rim El; Yoon, Seong‐Min

doi:10.1080/13504851.2022.2141443

Cited by 9 publications

(6 citation statements)

References 13 publications

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“…Zhou et al [64] used a TVP-VAR DY model, constructed a network in Chinese sectoral stock markets, and analyzed sectoral risk spillovers based on high-frequency data. Alshater et al [65] explored the connectedness among several regional FinTech indices and analyzed how the Russia-Ukraine war affected the dynamic spillover using the TVP-VAR DY and time-frequency connectedness network approaches.…”

Section: Financial Market Risk Network Using a Multivariate System Ap...mentioning

confidence: 99%

Risk Connectedness among International Stock Markets: Fresh Findings from a Network Approach

Choi¹,

Yoon²

2023

Preprint

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We analyze the upside and downside risk connectedness among international stock markets. We characterize the connectedness among international stock returns using the Diebold and Yilmaz spillover index approach and compute the upside and downside value-at-risk. We document that the connectedness level of the downside risk is higher than that of the upside risk and that stock markets are more sensitive when the stock market declines. We also find that specific periods (e.g., the global financial crisis, the European debt crisis, and the COVID-19 turmoil) intensify the spillover effects across international stock markets. Our results demonstrate that the EU, Ger-many, and the US acted as net transmitters of dynamic connectedness; however, Japan (JP), China (CH), and India (IN) acted as net receivers of dynamic connectedness during the sample period. These findings provide significant new information to policymakers and market participants.

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Section: Financial Market Risk Network Using a Multivariate System Ap...mentioning

confidence: 99%

Risk Connectedness among International Stock Markets: Fresh Findings from a Network Approach

Choi¹,

Yoon²

2023

Preprint

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“…Alshater et al (2022) [49] investigated the connectedness among four regional FinTech indices in a cross-region view. The empirical results revealed the role of FinTech in Asia-Pacific as a spillover receiver.…”

Section: The Interconnectedness Of Fintechmentioning

confidence: 99%

“…Interestingly, the interconnectedness of FinTech during extreme periods has been paid special attention. Researchers either conducted a robustness check [18] or a subsample analysis [49,51] of major events, such as the COVID-19 pandemic and the Russia-Ukraine conflict. This is reasonable since high uncertainty may boost the volatility of financial assets and, thus, fuels intense connectedness across assets [18].…”

Section: The Interconnectedness Of Fintechmentioning

confidence: 99%

“…GARCH-based methods are a family of parameterized models that explain the connectedness between assets via the dynamic structure of the conditional covariance. The straightforward interpretation of the results explains why GARCH-based methods have been extensively applied in measuring the interconnectedness of FinTech [49,52]. However, one of the main disadvantages of GARCH-based methods is similar to WCA: it cannot reflect the directional spillover from one asset to another.…”

Section: The Measurement Approaches Of Connectednessmentioning

confidence: 99%

“…Moreover, the VAR-based approaches can also provide directional spillovers via the sliding window estimation. The superiority of VAR-based approaches attracts researchers to adopt them in measuring the interconnectedness of FinTech [18,49,51]. We will elaborate on the technical details of DY and BK approaches in the next section.…”

Section: The Measurement Approaches Of Connectednessmentioning

confidence: 99%

See 2 more Smart Citations

Volatility Spillover Dynamics and Determinants between FinTech and Traditional Financial Industry: Evidence from China

Wang,

Xia,

et al. 2023

Mathematics

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We explore the dynamics and determinants of volatility spillover between financial technology (FinTech) and the traditional financial industry (TFI). The total, directional, and net spillover indices are measured, and we further decompose volatility spillovers in a time–frequency framework. Economic fundamentals, risk contagion, and market attention are examined as potential determinants of volatility spillovers. We reveal that the total spillover index is time-varying and exhibits an inverse U-shape between 2017 and 2021. FinTech is a net volatility receiver in most cases, and exported volatility to the TFI during the COVID-19 pandemic. The long-term components are the main driving force of volatility spillovers based on the frequency domain decomposition. Meanwhile, the economic fundamental determinants are the main contributing factors of volatility spillovers, explaining over 60% of total spillover between FinTech and the TFI. The findings remain robust under different settings of forecasting horizons, lag lengths, and proxy of the risk contagion determinant.

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Dynamic Interlinkages between the Twitter Uncertainty Index and the Green Bond Market: Evidence from the Covid-19 Pandemic and the Russian-Ukrainian Conflict

Polat,

Başar,

Ekşi

2024

Comput Econ

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This study examines the time-varying connectedness between green bonds, Twitter-based uncertainty indices, and the S&P 500 Composite Index. We implement the time- and frequency-based connectedness methodologies and employ data between April 1, 2014 and April 21, 2023. Our findings suggest that (i) connectedness indices robustly capture prominent incidents during the episode; (ii) Twitter-based uncertainty indices are the highest transmitters of return shocks; (iii) net return spillovers transmitted by the S&P 500 Index sharply increased in 2020:1–2020:3, stemmed by the stock market crash in February 2020; and (iv) Twitter-based uncertainty indices showed significant net spillovers in July and November 2021.

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Dynamic connectedness among regional FinTech indices in times of turbulences

Cited by 9 publications

References 13 publications

Risk Connectedness among International Stock Markets: Fresh Findings from a Network Approach

Risk Connectedness among International Stock Markets: Fresh Findings from a Network Approach

Volatility Spillover Dynamics and Determinants between FinTech and Traditional Financial Industry: Evidence from China

Dynamic Interlinkages between the Twitter Uncertainty Index and the Green Bond Market: Evidence from the Covid-19 Pandemic and the Russian-Ukrainian Conflict

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