From Bitcoin to carbon allowances: An asymmetric extreme risk spillover.

The Paris Agreement (COP21) sets out a global framework to limit global warming below 2C. Therefore, the target of carbon neutrality has a key role. In this context, countries have implemented cap-and-trade markets of carbon emissions allowances to manage the impact of CO2 released by companies. Over recent years, cryptocurrencies have given a new drive to pollution because of the massive energy consumption of mining activity. This paper investigates the tail relationship between the carbon credit market and the price of Bitcoin. For this purpose, we use two novel econometric models: the multivariate-quantile conditional autoregressive (MVMQ-CAViaR) model and Granger causality across quantiles. The results suggest that there is a downside risk spillover, i.e., tail co-dependence. We find that Bitcoin spillovers have a stronger impact on the carbon market. On the other hand, we show that the carbon market does not Granger-cause Bitcoin. The results of the Granger analysis confirm the multivariate quantile model's findings, i.e., Bitcoin influences the carbon market in the lower quantiles. We deem our results useful for policymakers to improve the framework of carbon emissions allowances.

Feverish sentiment and global equity markets during the COVID-19 pandemic.

This paper proposes a new approach to estimating investor sentiments and their implications for the global financial markets. Contextualising the COVID-19 pandemic, we draw on the six behavioural indicators (media coverage, fake news, panic, sentiment, media hype and infodemic) of the 17 largest economies and data from 1 st January 2020 to 3 rd February 2021. Our key findings, obtained using a time-varying parameter-vector auto-regression (TVP-VAR) model, indicate the total and net connectedness for the new index, entitled 'feverish sentiment'. This index provides us insight into economies that send or receive the sentiment shocks. The construction of the network structures indicates that the United Kingdom, China, the United States and Germany became the epicentres of the sentimental shocks that were transmitted to other economies. Furthermore, we also explore the predictive power of the newly constructed index on stock returns and volatility. It turns out that investor sentiment positively (negatively) predicts the stock volatility (return) at the onset of COVID-19. This is the first study of its kind to assess international feverish sentiments by proposing a novel approach and its impacts on the equity market. Based on empirical findings, the study also offers some policy directions to mitigate the fear and panic during the pandemic.

Tail risk connectedness in clean energy and oil financial market.

This research investigates the connectedness and the tail risk spillover between clean energy and oil firms, from January 2011 to October 2021. To this, we use the Tail-Event driven NETworks (TENET) risk model. This approach allows for a measurement of the dynamics of tail-risk spillover for each sector and firm. Hence, we can provide a detailed picture of the existing extreme relationships within these markets. We find that the total connection between the markets varies during the period analysed, showing how the uncertainty in oil price plays a critical role in the risk dynamics for oil companies. Also, we find that relationships between energy firms tend to be intrasectoral; that is, each sector receives (emits) risk from (to) itself. These results can have important practical implications for risk management and policymakers.