Volatility communicator or receiver? Investigating volatility spillover mechanisms among Bitcoin and other financial markets

Jiang, Shangrong; Li, Yuze; Lu, Quanying; Wang, Shouyang; Wei, Yunjie

doi:10.1016/j.ribaf.2021.101543

Cited by 55 publications

(15 citation statements)

References 45 publications

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“…This finding may be due to the multicollinearity caused by the high correlation between WAV and DSV as shown in Table 2. The incorporation of DSV slightly improves the explanatory power (approximately 1.8% in terms of adjusted R2), which is consistent with some recent studies, such as Jiang, et al (2022) [98].…”

Section: Determinants Of Total Spillover Indexsupporting

confidence: 91%

“…Risk contagion determinant is valid when increments and decrements in the same direction are observed in markets volatility and volatility spillovers. Inspired by Jiang, et al (2022)[98], we employ weighted average volatility (WAV) of FinTech, Banking, Security, Diversified, and Insurance indices as a proxy of risk contagion. (III) Market attention determinant.…”

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confidence: 99%

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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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Section: Determinants Of Total Spillover Indexsupporting

confidence: 91%

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confidence: 99%

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

Wang,

Xia,

et al. 2023

Mathematics

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“…For example, some previous studies show a weak relationship and thus hedging and safe haven implications for asset allocation and risk management (Bouri et al 2017a ; Baur et al 2018 ; Shahzad et al 2020 ), whereas others find a stronger relationship after the pandemic jeopardizes Bitcoin’s hedging ability (see, Conlon and McGee 2020 ; Kristoufek 2020 ; Kumar et al 2022 ). Elsayed et al ( 2022 ), Jiang et al ( 2022 ), Maghyereh and Abdoh ( 2022 ), and Akyildirim et al ( 2021 ) have all documented significant volatility links between aggregate stock market returns and the Bitcoin market. Furthermore, the majority of previous studies have attempted to explain Bitcoin volatility (see, e.g., Walther et al 2019 ; D’Amato et al 2022 ; Sapkota 2022 ; Wang et al 2022 ) or focused on the predictability of major cryptocurrencies and the profitability of trading strategies using machine learning techniques (Sebastião and Godinho 2021 ), 6 whereas our paper has a different scope by focusing on Bitcoin price ability to predict stock volatility.…”

Section: Introductionmentioning

confidence: 99%

The predictive power of Bitcoin prices for the realized volatility of US stock sector returns

Bouri

Salisu²,

Gupta³

2023

Financ Innov

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This paper is motivated by Bitcoin’s rapid ascension into mainstream finance and recent evidence of a strong relationship between Bitcoin and US stock markets. It is also motivated by a lack of empirical studies on whether Bitcoin prices contain useful information for the volatility of US stock returns, particularly at the sectoral level of data. We specifically assess Bitcoin prices’ ability to predict the volatility of US composite and sectoral stock indices using both in-sample and out-of-sample analyses over multiple forecast horizons, based on daily data from November 22, 2017, to December, 30, 2021. The findings show that Bitcoin prices have significant predictive power for US stock volatility, with an inverse relationship between Bitcoin prices and stock sector volatility. Regardless of the stock sectors or number of forecast horizons, the model that includes Bitcoin prices consistently outperforms the benchmark historical average model. These findings are independent of the volatility measure used. Using Bitcoin prices as a predictor yields higher economic gains. These findings emphasize the importance and utility of tracking Bitcoin prices when forecasting the volatility of US stock sectors, which is important for practitioners and policymakers.

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“…First, a few studies have looked into the roles of cryptocurrency marketplaces in the global financial market system. Jiang et al (2022) confirmed that natural gas, bitcoin, gold and currency markets all function as volatility communicators, whereas global oil prices and the stock market act as volatility reveivers. According to Rao et al (2022), Bitcoin can no longer be used as a good hedging mechanism, confirming the fact that 21st-century technology assets are becoming increasingly valuable.…”

Section: Related Literaturementioning

confidence: 85%

An empirical analysis of the dynamic impact of DeFi on GCC foreign exchange forward markets: portfolio implication

Tien,

Dang,

Hung

2023

IMEFM

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Purpose This paper aims to investigate the conditional equicorrelation and cross-quantile dependence between the DeFi, European and GCC currency markets (Oman, Qatar, Bahrain, Kuwait, Saudi Arabia and the United Arab Emirates). Design/methodology/approach This study applies the GARCH-DECO model and cross-quantilogram framework. Findings The findings reveal evidence of weak and negative average equicorrelations between the examined markets through time, excluding the COVID-19 outbreak and Russia–Ukraine conflict, which is consistent with the literature examining relationships in different markets. From the cross-quantilogram model, the authors note that the dependence between DeFi, EURO and GCC foreign exchange rate markets is greatest in the short run and diminishes over the medium- and long-term horizons, indicating rapid information processing between the markets under consideration, as most innovations are transmitted in the short term. Practical implications For the pairs of DeFi and currency markets, the static and dynamic optimal weights and hedging ratios are also estimated, providing new empirical data for portfolio managers and investors. Originality/value To the best of the authors’ knowledge, this is one of the most important research looking into the conditional correlation and predictability between the DeFi, EURO and GCC foreign exchange markets. More importantly, this study provides the first empirical proof of the safe-haven, hedging and diversification qualities of DeFi, EURO and GCC currencies, and this work also covers the COVID-19 pandemic and the Russia–Ukraine war with the use of a single dynamic measure produced by the GARCH-DECO model. In addition, the directional predictability between variables under consideration using the cross-quantilogram model is examined, which can be capable of capturing the asymmetry in the quantile dependent structure. The findings are helpful for both policymakers and investors in improving their trading selections and strategies for risk management in different market conditions.

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Volatility communicator or receiver? Investigating volatility spillover mechanisms among Bitcoin and other financial markets

Cited by 55 publications

References 45 publications

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

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

The predictive power of Bitcoin prices for the realized volatility of US stock sector returns

An empirical analysis of the dynamic impact of DeFi on GCC foreign exchange forward markets: portfolio implication

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