Network connectedness of the term structure of yield curve and global Sukuks

This paper explores the connectedness between the returns and volatilities of the conventional and Islamic bond markets. We use the level, slope, and curvature of the US yield curve and estimate the connectedness of these factors with the Dow Jones Islamic indices (of 3 to 10 years of maturity) as well as the minimum connectedness portfolio. The static analysis shows that level and slope of the conventional yield curve are the net transmitters of shocks while the Islamic indices have been mostly at the receiving end. The dynamic connectedness analysis shows a varying degree of the connectedness over the full sample period characterized by distinctive trajectories of booms and busts. The pairwise connectedness analysis also confirms that level and slope are the net transmitters in the system with an exception in most recent times of Covid-19 pandemic. The findings have implications for the researchers, policy makers, regulators, shariah boards, investors, and fund managers.

Decoupling Between the Energy and Semiconductor Sectors During the Pandemic: New Evidence from Wavelet Analysis

We study the impact of COVID-19 on the pairwise dependence between three indices, the COVID-19 Media Coverage Index, MSCI World Semiconductor Index, and the MSCI World Energy Index, as well as investigate the respective volatility spillovers. We find intervals of weak, moderate, and strong coherence between the Media Coverage Index and returns and volatility of semiconductor and energy sector companies. Low coherence intervals indicate a diversification potential of investments in these sectors and in their volatility-based products during periods of systemic crises such as the financial turmoil induced by COVID-19. Our results provide evidence that after the escalation of the pandemic in early 2020, the energy sector cedes its leading role in terms of volatility to the semiconductor industry. We report on appealing hedging attributes related to the decoupling between the trends in the global semiconductor industry and the global energy sector accelerated by the COVID-19 triggered crisis.

COVID-19 and the quantile connectedness between energy and metal markets.

This study analyzes the relationship between clean and dirty energy sources and energy metals during the COVID-19 pandemic. We document a sharp increase in connectedness after the COVID-19 pandemic, that is asymmetric at the lower and upper quantiles, with stronger dependence among the variables at the upper quantiles. Among the energy metals, cobalt is the least connected to the energy markets. Finally, our empirical results show a switch in the net connectedness indexes of energy metals and clean energy after January 2021. Our results have implication for investors and policy makers for energy and metal under various market conditions.

Decoupling Between the Energy and Semiconductor Sectors During the Pandemic: New Evidence from Wavelet Analysis

We study the impact of COVID-19 on the pairwise dependence between three indices, the COVID-19 Media Coverage Index, MSCI World Semiconductor Index, and the MSCI World Energy Index, as well as investigate the respective volatility spillovers. We find intervals of weak, moderate, and strong coherence between the Media Coverage Index and returns and volatility of semiconductor and energy sector companies. Low coherence intervals indicate a diversification potential of investments in these sectors and in their volatility-based products during periods of systemic crises such as the financial turmoil induced by COVID-19. Our results provide evidence that after the escalation of the pandemic in early 2020, the energy sector cedes its leading role in terms of volatility to the semiconductor industry. We report on appealing hedging attributes related to the decoupling between the trends in the global semiconductor industry and the global energy sector accelerated by the COVID-19 triggered crisis.

Impacts of COVID-19 on dynamic return and volatility spillovers between rare earth metals and renewable energy stock markets

We examine the time-frequency co-movements and return and volatility spillovers between the rare earths and six major renewable energy stocks. We employ the wavelet analysis and the spillover index methodology from January 1, 2018 to May 15, 2020. We report that the COVID-19-triggered significant increase in co-movements and spillovers in returns and volatility between the rare earths and renewable energy returns and volatility. The rare earths act as net recipient of both return and volatility spillovers, while the clean energy stocks are net transmitters of return and volatility spillovers before and during the COVID-19 crisis. The solar and wind stocks are net transmitters/receivers of spillovers before/during the pandemic. The remaining markets shift from net spillover receivers to transmitters or vice versa;evidencing the effects of the pandemic. Our results show that cross-market hedge strategies may have their efficiency impaired during the periods of crises implying a necessity of portfolio rebalancing.

Dynamic connectedness between non-fungible tokens, decentralized finance, and conventional financial assets in a time-frequency framework

This study examines how the COVID-19 pandemic has affected the connectedness between non-fungible tokens, decentralized finance coins, traditional financial assets, and cryptocurrencies. We employed a time-varying parameter vector autoregressive based frequency-dependent network connectedness approach to investigate return and volatility spillover effects between assets in time and frequency domains. The findings show that both the returns and volatility spillovers have been significantly affected by the COVID-19 pandemic, and long- and short-term connectedness vary over the course of the pandemic. These findings have implications for investors, portfolio managers, and policymakers regarding their investment strategies, portfolio allocation, and risk monitoring.

Do Local and World COVID-19 Media Coverage Drive Stock Markets? Time-Frequency Analysis of BRICS

The role of media coverage as a proxy for investor sentiments has led to the assessments of the impact of COVID-19 media coverage on financial markets. To determine how both local and global media coverage affect financial markets differently, we investigate this issue from the perspective of top emerging markets, BRICS (i.e., Brazil, Russia, India, China, and South Africa). With datasets covering January 2020 to March 2022, we employ the wavelet coherence technique on two major subsamples, viz. initial outbreak year sample and the “new normal” era sample. Our findings demonstrate the leading role of BRICS equities in the initial outbreak period, particularly across medium and low frequencies. In the “new normal” era, we find a significant effect of world media coverage on BRICS equities. We discuss the implications of our findings, which are of importance to investors, policymakers, and practitioners.

The Return and Volatility Connectedness of NFT Segments and Media Coverage: Fresh Evidence Based on News About the COVID-19 Pandemic.

We study the relationship between return and volatility of non-fungible tokens (NFT) segments and media coverage during the outbreak of the COVID-19 pandemic in a connectedness framework. We document media coverage as a net transmitter of spillover for both the return and volatility of NFT segments. We find that NFTs representing the Utilities segment is a major transmitter of spillover. Our findings have important implications for portfolio managers, regulators, and policymakers.

Oil price shocks and the term structure of the US yield curve: a time-frequency analysis of spillovers and risk transmission.

This paper investigates the influence of oil demand, oil supply, and risk-driven shocks on the yield curve in the US between 1995 and 2020. The US term-structure shape is modeled by three structural factors, the level, slope, and curvature. Their empirical analysis is performed according to the Diebold-Li modified variant of the widely used Nelson-Siegel model. The technique of wavelet analysis allows investigating the interrelation of shocks in oil prices and the US yield curve along time and frequency domains, simultaneously. We report on low, medium, and high coherence zones, relative to the oil price movements and the changes in the three yield-curve factors. The low coherence intervals indicate the potential for the three latent factors to be used for creating diversification strategies capable of hedging adverse dynamics in the oil market, potentially workable through global crises. We document the variability of dynamic patterns observable for the US sovereign yield factors on per-type-of-shock basis, evidencing the potential role of the US sovereign debt investments for designing cross-asset hedge strategies for commodity and fixed-income markets.

Modelling the asymmetric effect of COVID-19 on REIT returns: A quantile-on-quantile regression analysis.

The COVID-19 pandemic has affected all sectors of the economy resulting in unprecedented challenges for market participants, policymakers, and practitioners. This study envisages this issue from the perspective of real estate investment trusts (REITs), which is a relatively less analysed segment. We examine the impact of the COVID-19 pandemic on REIT returns for 12 top REIT regimes spread across America, Asia, and Europe under the bullish, bearish, and normal market conditions over the COVID-19 period (specifically from February 02, 2020, to January 24, 2022). We employ the quantile-on-quantile regression and causality-in-quantiles approach. We document a strong (weak) predictive power of COVID-19 cases on REIT returns within the lower (upper) conditioned quantiles. Our findings are of importance to market participants, practitioners, and regulators across REIT regimes.

Covid-19 impact on NFTs and major asset classes interrelations: insights from the wavelet coherence analysis

Non-fungible tokens (NFTs) revolutionize crypto-landscape, becoming popular among investors and general public. This first-ever study of coherence between returns of NFTs and major assets employs the wavelet approach. The pairwise returns coherence between the considered markets grows throughout the Covid-19. Before the pandemic, NFTs lag behind stocks (2017) and bitcoin (2018), while lead gold (2018). We reveal that the returns coherence between NFTs and other assets is high/low for the two-week-plus/below-to-weeks investment horizons. We refine Aharon and Demir´s (2021) findings stating that NFTs absorbed risk during Covid-19 by demonstrating that this conclusion holds only in the short-run for below-two-weeks horizons.

Impact of the Covid-19 induced panic on the Environmental, Social and Governance leaders equity volatility: A time-frequency analysis.

We apply wavelet analyses to study how the Covid-19 fueled panic influenced the volatility of ESG (environmental, social and governance) leaders' indices encompassing the World, the USA, Europe, China, and the Emerging Markets. We document intervals of the low, medium, and high coherence between the Coronavirus Panic Index and the price moves of the ESG Leaders indices. The low coherence intervals signify the diversification potential of ESG investments during a systemic pandemic such as Covid-19. We document differences in the pattern exhibited by various geographical indices highlighting their potential role for designing cross-geography hedge strategies, both now and in the future.

The impact of Covid-19 on commodity markets volatility: Analyzing time-frequency relations between commodity prices and coronavirus panic levels.

We apply wavelet analyses to study how the Covid pandemic influenced the volatility of commodity prices, covering various classes of commodities. We document the intervals of low, medium, and high coherence between the coronavirus panic index and the moves of the commodity prices. The low coherence intervals indicate the diversification potential of commodity investments during a systemic pandemic such as Covid-19. We document differences in the observed patterns per commodity category and evidence their potential role for designing cross-assets hedge strategies based on investments in commodities.