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1.
International Journal of Finance and Economics ; 2022.
Article in English | Scopus | ID: covidwho-2263988

ABSTRACT

Understanding the transmission of volatility across markets is essential for managing risk and financial stability, especially under crisis periods during which an extreme event occurring in one market is easily transmitted to another market. To gain such an understanding and enrich the related literature, we examine in this article the system of volatility spillovers across various equity markets and asset classes using a quantile-based approach, allowing us to capture spillovers under normal and high volatility states. The sample period is 16 March 2011–10 November 2020 and the employed dataset comprises 12 implied volatility indices representing a forward-looking measure of uncertainty of global equities, strategic commodities and the US Treasury bond market. The results show that the identity of transmitters and receivers of volatility shocks differ between normal and high volatility states. The US stock market is at the centre of volatility spillovers in the normal volatility state. European and Chinese stock markets and strategic commodities (e.g. crude oil and gold) become major volatility transmitters in the high volatility state, after acting as volatility receivers during normal periods. Furthermore, we study the drivers of implied volatility spillovers using regression models and find that US Default spread contributes to the total volatility spillover index in both volatility states, whereas TED spread plays a significant role in the normal volatility state. As for the role of short rate and risk aversion, it is significant in the high volatility state. These findings matter to the decision-making process of risk managers and policymakers. © 2022 John Wiley & Sons Ltd.

2.
International Journal of Finance & Economics ; 2022.
Article in English | Web of Science | ID: covidwho-2121345

ABSTRACT

Understanding the transmission of volatility across markets is essential for managing risk and financial stability, especially under crisis periods during which an extreme event occurring in one market is easily transmitted to another market. To gain such an understanding and enrich the related literature, we examine in this article the system of volatility spillovers across various equity markets and asset classes using a quantile-based approach, allowing us to capture spillovers under normal and high volatility states. The sample period is 16 March 2011-10 November 2020 and the employed dataset comprises 12 implied volatility indices representing a forward-looking measure of uncertainty of global equities, strategic commodities and the US Treasury bond market. The results show that the identity of transmitters and receivers of volatility shocks differ between normal and high volatility states. The US stock market is at the centre of volatility spillovers in the normal volatility state. European and Chinese stock markets and strategic commodities (e.g. crude oil and gold) become major volatility transmitters in the high volatility state, after acting as volatility receivers during normal periods. Furthermore, we study the drivers of implied volatility spillovers using regression models and find that US Default spread contributes to the total volatility spillover index in both volatility states, whereas TED spread plays a significant role in the normal volatility state. As for the role of short rate and risk aversion, it is significant in the high volatility state. These findings matter to the decision-making process of risk managers and policymakers.

3.
Energy ; : 124580, 2022.
Article in English | ScienceDirect | ID: covidwho-1906985

ABSTRACT

Financial events in global energy markets could trigger extreme volatility spillovers and even become financial crises without effective risk management. To analyze extreme volatility spillovers in energy markets and demonstrate how risks are spread under extreme market conditions, this paper first measures the extreme volatility spillovers in the main energy markets from 2011 to 2019 using the vine Copula model then constructs the extreme volatility spillover networks based on their tail dependence correlations. Furthermore, the extreme risk spread is investigated by applying clique analysis. In consideration of the recent shock namely the COVID-19 pandemic, we further analyze the energy markets' extreme volatility spillovers in 2020. We find that extreme volatility spillover effects are stronger in renewable energy markets, especially during market booms and it also reflects an asymmetry feature of the energy markets’ extreme volatility spillovers. The water energy market is influential in the extreme volatility spillover networks. More significantly, there are strong volatility spillover effects among renewable energy markets, while risks are less likely to spread among non-renewable energy markets under extreme circumstances. We make contributions to reveal the volatility spillover under extreme conditions, then comprehensively analyze the risk spread in the extreme volatility spillover networks. By comparing how risks spread before and during the COVID-19, and between renewable and non-renewable energy markets, our findings could be of great help for policymakers and other stakeholders to cope with the uncertainties, especially in the extreme environment.

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