Extreme spillover effect of COVID-19 pandemic-related news and cryptocurrencies on green bond markets: A quantile connectedness analysis.

We provide the first empirical study on the role of panic and stress related to the COVID-19 pandemic, including six uncertainties and the four most traded cryptocurrencies, on three green bond market volatilities. Based on daily data covering the period from January 1, 2020 to January 31, 2022, we combine Diebold and Yilmaz's (2012, 2014) time domain spillover approach and Ando et al.'s (2022) quantile regression framework to investigate the time-frequency spillover connectedness among markets and measure the direction and intensity of the net transmission effect under extreme negative and positive event conditions, and normal states. We further provide novel insights into the green finance literature by examining sensitivity to quantile analysis of the net transfer mechanism between green bonds, cryptocurrencies, and pandemic uncertainty. Regarding the network connectedness analysis, the results reveal strong net information spillover transmission among markets under the bearish market. In extremely negative event circumstances, the MSCI Euro green bond acts as the leading net shock receiver in the system, whereas COVID-19 fake news appears as the largest net shock contributor, followed by BTC. According to sensitivity to quantile analysis, the net dynamic shock transfer mechanism is time-varying and quantile-dependent. Overall, our work uncovers crucial implications for investors and policymakers.

Predictive power of investor sentiment for Bitcoin returns: Evidence from COVID-19 pandemic

In this paper, we examine the impact of investor sentiment on Bitcoin returns. Using a large dataset of messages discussed on social media and several financial indicators, we create a sentiment indicator based on computational text analysis and driven by the principal component analysis (PCA) method. We utilize a vector autoregressive analysis and other analytical methods to examine the sentiment index–bitcoin return nexus. Our findings reveal that the sentiment index is a strong predictor of cryptocurrency market returns in the short term. Furthermore, we confirm that during the COVID-19 pandemic, investors' sentiments significantly impacted Bitcoin returns. Our results show that the proposed sentiment index can generate excess returns for investors who utilize it as a return predictor. Our empirical findings suggest important policy implications.

Tail-risk spillovers from China to G7 stock market returns during the COVID-19 outbreak: A market and sectoral analysis.

This study uses a combination of copulas and CoVaR to investigate risk spillovers from China to G7 countries before and during the COVID-19 pandemic. Using daily data on stock and equity sectors for the period from January 1, 2013 to June 9, 2021, the main empirical results show that, before the COVID-19 pandemic, stock markets were positively related and systemic risk was comparable for all countries. However, during the COVID-19 outbreak, the level of dependence increased for all G7 countries and the upside-downside risk spillovers become on average higher for all stock markets, with the exception of Japan. Our results also provide evidence of higher market risk exposure to information from China for the technology and energy sectors. Moreover, we find an asymmetric risk spillover from China to the G7 stock markets, with higher intensity in downside risk spillovers before and during COVID-19 spread.

Multiresolutional statistical machine learning for testing interdependence of power markets: A Variational Mode Decomposition-based approach

In the increasingly interconnected and digitized world, the field of electricity price forecasting has benefited from growing research especially due to the market liberalization and the connectedness between electrical systems. This study defines a novel multiscaled forecasting model based upon the Variational Mode Decomposition (VMD) to quantify multiscaled cross-correlation between two important European markets during COVID-19 pandemic. The VMD is known to be a strong information processing tool which is localized in both frequency and time, and is especially used for capturing nonstationary and nonlinear behaviors of time series. The set of new VMD techniques is applied on hourly electricity spot prices from the Nord Pool and MIBEL energy exchanges for the period ranging from January 2019 to March 2020. The sampled time series include a period of specific recession in the financial system, coinciding with the Brexit and COVID-19 event, which was accompanied by a significant collapse in the world’s economic sphere. The empirical results reveal a significant dependence between electricity markets across long- and medium-run investment time horizons, with evidence for dynamic lead–lag relationships at some frequency sub-bands. However, over the short-term (daily and intra-daily intervals), we notice a kind of independence between markets, especially in times of crisis, which offers investors different investment diversification opportunities. On the other hand, the accuracy of generated forecasts prove the interest of a conjoint modeling and the reliability of this new tool, in particular when the approach is adequately coupled with feedforward neural networks.

Impact of COVID-19 pandemic on crude oil prices: Evidence from Econophysics approach.

This paper provides an analysis of crude oil, diesel, and gasoline prices for the period from November 1, 2019 to December 31, 2020. We apply Log Periodic Power-Law Singularity (LPPLS) and Discrete Scale LPPLS bubble indicators to explore the dynamic bubbles of oil prices and predict their crash times. The results indicate that West Texas Light crude oil and North Sea Brent crude oil experienced a statistically significant negative financial bubble during the COVID-19 outbreak. In addition, gasoline and diesel prices are mainly driven by fundamentals. Our findings are expected to be useful to oil market investors, policymakers, and energy experts.

The safe-haven property of precious metal commodities in the COVID-19 era

This paper examines the presence of short- and long-run asymmetric relationships between precious metal commodities and S&P 500 stock market index and explores the safe-haven role of five precious metals (Aluminum, Platinum, Palladium, Copper, Gold) against the S&P 500 market index. We apply the nonlinear ARDL framework on daily data covering the COVID-19 pandemic period from 12/31/2019 to 06/25/2021. The main results highlight that precious metal commodities still impact the S&P 500 stock market index in an asymmetric manner but have lost their safe-haven role against the S&P 500 index. In the short-run, only aluminum has an asymmetric effect on S&P 500. However, Platinum, palladium, copper and gold have a symmetric effect on S&P 500. In the short-term, palladium, copper and gold are safe-haven assets against the S&P 500 stock market index. Furthermore, none of the considered precious metals acts as a safe-haven asset in the long-run. Our results have significant implications for portfolio investment strategies.

The bubble contagion effect of COVID-19 outbreak: Evidence from crude oil and gold markets.

This paper examines the causal relationship between crude oil and gold spot prices to assess how the economic impact of COVID-19 has affected them. We analyze West Texas Light crude oil (WTI) and gold prices from January 4, 2010, to May 4, 2020. We detect common periods of mild explosivity in WTI and gold markets. More importantly, we find a bilateral contagion effect of bubbles in oil and gold markets during the recent COVID-19 outbreak.