Volatility spillovers during normal and high volatility states and their driving factors: A cross‐country and cross‐asset analysis

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.

Do geopolitical risks and global market factors influence the dynamic dependence among regional sustainable investments and major commodities?

“…Besides, the plots of timevarying total spillover at both the conditional mean and median suggest that over the sample period, the level of total spillover appear to have intensified during the first wave of the COVID-19 pandemic in the first two quarters of 2020, especially when total spillover is measured at the conditional mean using the standard Diebold and Yilmaz (2012) approach. Significant increase in the levels of risk spillovers among sustainable and conventional assets during the peak of the COVID-19 pandemic has been documented in previous studies (see e.g., Iqbal, Bouri et al, 2022;Iqbal, Naeem et al, 2022;Zhao et al, 2023).…”

“…Second, we contribute to the literature by being the first to use the QVAR method recently developed by Ando et al (2022) to analyse the interdependence between major natural resource commodities and sustainable investment. This methodology follows past leading studies on spillover in the quantiles (see e.g., Bouri & Harb, 2022;Bouri et al, 2020;Iqbal, Bouri et al, 2022;Iqbal, Naeem et al, 2022). As noted earlier, the method avails us the opportunity to characterize crossmarket linkages and the propagation of shocks across different market conditions for the studied investment indices.…”

WTI, Brent or implied volatility index: Perspective of volatility spillover from oil market to Chinese stock market

“…The energy-stock relationship may also differ in different market states. For instance, conditional on quantile-based method, [ 5 ] aim to describe volatility transmission among global equities, strategic commodities and the US Treasury bond market during normal and high volatility states. Note that there exist not only return and volatility spillover between energy market and stock market, higher moments namely skewness and kurtosis spillover may also contain valuable information.…”

The nexus between herding behavior and spillover: evidence from G7 and BRICS

“…The results showed that the negative impact of total volatility outweighs the positive impact, indicating system-wide pessimism. Najaf et al (2022) examined the volatility spillover in various equity markets and asset classes using a quantum-based approach. They found that in a normal state of volatility, the US stock market is at the center of the volatility effect.…”