Extreme Spillovers Research Articles

Extreme spillovers across carbon and energy markets: A multiscale higher-order moment analysis

The complexity of carbon market mechanisms and the uncertainty in market conditions raise questions on how carbon and energy markets interact. The majority of existing studies focused on the lower-order moment spillover across carbon and energy markets, thereby posing limitations on carbon risk management and carbon market efficiency. This paper analyzes multiscale skewness and kurtosis spillovers across carbon and energy markets under different market conditions. It is found that carbon market is a short-term net skewness spillover receiver, but becomes a medium-term risk source under all market conditions, capable of transmitting skewness risk to the natural gas market. The carbon market acts as a short- and medium-term kurtosis risk source for the natural gas and electricity markets in the lower probability of extreme returns, but bears the kurtosis risk from the natural gas and coal markets when the extreme risk is high. These results indicate that policymakers should take measures to adjust carbon prices when facing long-term skewness risk and a lower probability of extreme returns in the carbon market, to prevent further spread of risk to the energy markets.

Oil, gold and international stock markets: Extreme spillovers, connectedness and its determinants

This study examines the lower and upper return spillovers and connectedness between important commodity (crude oil and gold) and main international stock markets using the quantile connectedness approach by Ando et al. (2018). The results show stronger return spillovers during bearish and bullish market conditions. Crude oil and gold are net receivers of return spillovers irrespective of quantiles. Furthermore, an intense spillover is observed during high stress periods including COVID-19 in March 2020, and global economic slowdown and the Brexit referendum of 2016, oil price crash in 2014–2016, Tapering Tantrum in 2013, and the subprime crisis of 2008. Moreover, the spillovers in lower quantiles are stronger than in upper quantiles. The volatility uncertainty index (VIX) has a negative impact on the connectedness index at low and median quantiles. Gold and economic policy uncertainty indexes have a positive impact on lower quantile spillovers. In contrast, oil uncertainty index has a negative impact on quantile spillovers. COVID-19 crisis has a positive impact on lower, medium, and upper spillovers.

The extreme spillover from climate policy uncertainty to the Chinese sector stock market: wavelet time-varying approach

The extreme spillover from climate policy uncertainty to the Chinese sector stock market: wavelet time-varying approach

Extreme spillovers between insurance tokens and insurance stocks: Evidence from the quantile connectedness approach

This study examines potential tail spillovers between insurance tokens and conventional stocks using the quantile connectedness approach by Ando et al. (2022). In particular, this study explores static and dynamic spillovers at lower and upper tails of the return distribution. In line with previous studies, tokens and conventional stocks within the insurance market may show positive but low connectedness levels. Furthermore, our findings confirm a higher sensitivity of the insurance system at both tails of the distribution in comparison with the median (Q=0.50). As expected, dynamic connectedness measures change over time, intensifying at the extremes of the distribution. This finding is confirmed by the robustness test that consists of analyzing the RTD (Relative Tail Dependence) measure, as we reject the symmetric response, since its values are clearly different from zero in most of the sample period. These results are of interest to portfolio managers, as the findings will allow them to suggest adjustments to investment portfolios according to the evolution of the dynamic spillovers found.

Demystifying the Impact of COVID-19 on Structural Volatility of Indian Sectoral Market: A Quantile Regression Analysis

The present study explores the impact of COVID-19 on the volatility structure of the sectoral market in India. ARMA(p,q)- GJR-GARCH(1, 1)-std model is used to determine the daily conditional volatility for 13 selected sectors over the period starting from January 2020 to December 2021. The quantile regression model is employed to examine the changes in the structure of volatility in each sector over the pandemic duration. The results of the study show that the volatility of Metal, Oil–Gas and PSU are more sensitive to market volatility, whereas the volume of new COVID-19 cases exceeds the threshold limit, and no extreme spillover is observed from the market volatility. In addition to this, Bankex, Metal, Oil–Gas, Private Banks and Power sector volatility are more responsive to news sentiments during the period of increase in new COVID-19 cases. Furthermore, the results also reveal that news sentiments help to control the significant fluctuation in the sectoral market.

Tail dependence, dynamic linkages, and extreme spillover between the stock and China's commodity markets

Cross-market linkage and spillover effects under extreme risk scenarios have recently attracted widespread attention from scholars. However, few studies have focused on tail dependence and extreme spillovers between the stock and the Chinese commodity markets. For the first time, this paper investigates the tail dependence, dynamic linkages, and extreme return spillovers between the stock market and China's commodity markets, employing the novel quantile coherency, DCC-FIGARCH model, and quantile connectedness approach. The empirical results demonstrate that chemical commodities and non-ferrous metals exhibit relatively stronger linkages with the US and Chinese stock markets. Lower return quantiles of the stock markets exhibit higher coherency with the lower return quantiles of Chinese commodity markets in the long-term time horizon. The quantile coherency in the long-term (yearly) is higher than that in the middle (monthly) and short-term (weekly) time horizons. The dynamic linkages and return spillovers change over time and are vulnerable to major crises, particularly during the COVID-19 pandemic. The return spillovers at the extreme lower quantile are stronger than the spillovers at the extreme upper and median quantiles. Chemical commodity and non-ferrous metal sectors (grain commodity and noble metal sectors) are the two key net transmitters (recipients) of the return spillovers. The Chinese and US stock markets mainly act as the net recipients of the extreme spillovers.

Extreme risk spillovers across financial markets under different crises

We examine how extreme risk spillovers caused by different crises transmit through financial markets in the US, Europe and Asia-Pacific regions. By modifying the bivariate peak-over-threshold and DCC-GARCH models and using daily negative log-returns from November 1991 to August 2020, we find significant bidirectional extreme risk spillovers with persistent effects between US and other markets. Contributions to extreme risk spillovers vary across crises. The recent COVID-19 shock (non-financial channel) and the 2007–2008 global crisis (financial channel) contribute more to extreme risk spillovers. Moreover, the extreme spillovers in the COVID-19 shock depend more on the non-financial channel compared to the global crisis and cause larger contributions to extreme losses, showing the impact of the non-financial shocks on financial markets. • We investigate how extreme risk spillovers propagate between financial markets in different crisis. • An approach to measure extreme risk spillovers is proposed. • Extreme risk spillovers are persistent and bidirectional. • Extreme risk spillovers transmit through the financial and non-financial channels. • Compared with different crisis, the recent COVID-19 crisis contributes more to extreme risk spillovers.

Oil, Gold and International Stock Markets: Extreme Spillovers, Connectedness and its Determinants

Oil, Gold and International Stock Markets: Extreme Spillovers, Connectedness and its Determinants

Distributional Predictability and Extreme Spillovers of Clean Energy, Steam Coal and Technology: Evidence from a Quantile-Based Analysis

Distributional Predictability and Extreme Spillovers of Clean Energy, Steam Coal and Technology: Evidence from a Quantile-Based Analysis

Extreme spillovers across Asian-Pacific currencies: A quantile-based analysis

The Asia-Pacific region remains understudied regarding return connectedness among exchange rate markets. Furthermore, previous studies mostly use average-based estimators to measure return connectedness, even though they are not necessarily suitable to quantify connectedness measures during extreme events, i.e. in the tails of the conditional distribution. The aim of this paper is to study the dynamic connectedness among 11 Asia-Pacific exchange rates from September 1994 to August 2019, not only on average but also in the left and right tails. Using mean-based measures of connectedness, the level of return connectedness is on average 32%, indicating a moderate level of connectedness across the currencies under study. However, using a novel quantile-based connectedness framework, the level of connectedness is found to be much higher, reaching around 85% at both the left and right tails of the conditional distribution. This result shows that return connectedness across the 11 exchange rates is much stronger during extreme negative and extreme positive events, suggesting that the application of mean-based models of connectedness in the Asia-Pacific exchange rate markets is too restrictive and inadequate. Furthermore, return connectedness measures are time-varying in all cases, but are less volatile in the tails. A detailed analysis involving the relative tail dependence shows that an asymmetric behaviour is present, indicating that return spillovers differ between periods of extreme market depreciations and periods of extreme market appreciations.

Extreme Spillover Effects of Volatility Indices

In this study, we analyse contagion effects and extreme comovements of equity and volatility indices in major international markets. The tail dependence coecients (TDCs) increase during a financial crisis, especially for the lower TDCs of stock index returns and upper TDCs of volatility index returns. This indicates that crashes and uctuations are easier to transmit between markets during turmoils, implying the existence of contagion. In particular, we find that the crash events transmit from the Japanese market to other markets, whereas booms are more likely to transmit from the US and Europe to Japan.

Strategic Information Revelation in an R&D Race with Spillovers

The paper analyzes how spillovers in the product market affect the incentives of firms to reveal information about their innovative productivity. Such spillovers create a free-rider effect in the patent race that countervails the familiar business-stealing effect. The equilibrium disclosure strategy of firms depends on the size of the spillover. For extreme spillovers firms fully disclose, but for intermediate spillovers firms either partially disclose or conceal all information. Strategic disclosure may change the effect of spillovers on expected equilibrium profits. Whereas the expected profit is single-peaked for any fixed disclosure rule, it need not be single-peaked for equilibrium disclosure.