Determinants of financial stability and risk transmission in dual financial system: Evidence from the COVID pandemic

Existing literature on spillovers and connectedness between Islamic and conventional financial markets overlooked the fundamental role played by money markets in volatility spillovers and risk transmission across markets. That being so, this paper aims at investigating the dynamic co-movements and volatility spillovers across Islamic and conventional financial markets in a dual financial system over the period from January 3, 2007 to June 30, 2021. To this end, the DECO-GJR-GARCH model and the volatility spillover approach were applied. Furthermore, the ARDL model was utilised to explore the key determinants of co-movements and risk transmission across Islamic and conventional financial markets. This not only allowed us to study the interconnectedness and volatility spillovers between financial sectors under different market conditions but also enabled us to highlight the key role played by the money markets. Empirical results show that markets have significant responses to any new relevant information. While both conventional stock and money market are the main transmitters of shocks to other markets, the Islamic money market is a net recipient. Furthermore, the volatility spillovers across conventional and Islamic financial markets became stronger during the COVID-19 epidemic. The study also finds that global uncertainties have a significant and negative impact on the dynamic co-movements, but not on volatility connectedness among the underlying markets. These findings have important implications for many stakeholders including portfolio managers, investors, and policymakers in terms of diversifying their portfolios and enhancement of financial stability during times of black swan events and negative shocks such as the COVID-19 pandemic.

A Wavelet Analysis of the Dynamic Connectedness among Oil Prices, Green Bonds, and CO2 Emissions

Wavelet power spectrum (WPS) and wavelet coherence analyses (WCA) are used to examine the co-movements among oil prices, green bonds, and CO2 emissions on daily data from January 2014 to October 2022. The WPS results show that oil returns exhibit significant volatility at low and medium frequencies, particularly in 2014, 2019-2020, and 2022. Also, the Green Bond Index presents significant volatility at the end of 2019-2020 and the beginning of 2022 at low, medium, and high frequencies. Additionally, CO2 futures' returns present high volatility at low and medium frequencies, expressly in 2015-2016, 2018, the end of 2019-2020, and 2022. WCA's empirical findings reveal (i) that oil returns have a negative impact on the Green Bond Index in the medium term. (ii) There is a strong interdependence between oil prices and CO2 futures' returns, in short, medium, and long terms, as inferred from the time-frequency analysis. (iii) There also is evidence of strong short, medium, and long terms co-movements between the Green Bond Index and CO2 futures' returns, with the Green Bond Index leading.

Synchronisation among short-term rental markets, co-movements and cycles in 39 European cities

This paper presents new evidence of the short-term rental market's prices and transactions from a daily time -series perspective in 39 European cities from 2015 to 2020. It uses Airbnb micro datasets to build time-series cycles by extracting the original observations containing total bookings (rent transactions), rental units sup-ply, and asking rent, with a daily periodicity. The cycles show the periods in which short-rental activity was more relevant for each city, and the level of rents across Europe. The paper provides empirical evidence of a long-term relationship among the city variables (tested via mean and variance). Causality supporting co-movements across cities was found by estimating a short-term naive market equilibrium model using the vector error correction model approach, supporting the hypothesis that the short-term rental market performs according to housing -market principles. Short-run elasticities among rents and contracts across the 39 cities show causal evidence of co-movements among rents and the supply and demand of properties. The market adjustment on the supply side estimates new units responding to changes in prices within 15 lags (days) and longer (350 lags) from the demand side, equivalent to eight to nine months. Evidence of the pandemic's limited effect on housing supply and prices' positive effect is also provided. A robust negative weekend impact on prices was found, suggesting stronger market relevance on weekdays.

Synchronisation among short-term rental markets, co-movements and cycles in 39 European cities

This paper presents new evidence of the short-term rental market's prices and transactions from a daily time-series perspective in 39 European cities from 2015 to 2020. It uses Airbnb micro datasets to build time-series cycles by extracting the original observations containing total bookings (rent transactions), rental units supply, and asking rent, with a daily periodicity. The cycles show the periods in which short-rental activity was more relevant for each city, and the level of rents across Europe. The paper provides empirical evidence of a long-term relationship among the city variables (tested via mean and variance). Causality supporting co-movements across cities was found by estimating a short-term naïve market equilibrium model using the vector error correction model approach, supporting the hypothesis that the short-term rental market performs according to housing-market principles. Short-run elasticities among rents and contracts across the 39 cities show causal evidence of co-movements among rents and the supply and demand of properties. The market adjustment on the supply side estimates new units responding to changes in prices within 15 lags (days) and longer (350 lags) from the demand side, equivalent to eight to nine months. Evidence of the pandemic's limited effect on housing supply and prices' positive effect is also provided. A robust negative weekend impact on prices was found, suggesting stronger market relevance on weekdays.

Cryptocurrency Interdependencies and COVID-19: The Diebold-Yilmaz and the Frequency Connectedness Approaches

It is well-known that financial connectedness tends to surge during financial/geopolitical turmoils. To this end, this study examines the impact of the COVID-19 pandemic on cryptocurrency connectedness by employing the Diebold-Yilmaz and the frequency connectedness approaches. Total spillover indexes estimated by both methodologies create proper signs to the 2017/2018 cryptocurrency bubble and gradually escalate around March 2020, which coincides with the WHO's official announcement of the COVID-19. The study contributes to the literature by gauging the COVID-19 connectedness among eight major cryptocurrencies on different frequency bands and 200-day moving windows by employing two novel methodologies.

Dynamic relationships among green bonds, CO2 emissions, and oil prices

Green bonds play a pivotal role in the financing of sustainable infrastructure systems. Likewise, CO2 emissions and oil prices can cause an impact on the green bonds market. In order to better understand this issue, this study analyzes the relationship among green bonds, CO2 futures' prices, and oil prices using a daily data set that includes 2,206 observations corresponding to daily information from 1 January 2014 to 15 June 2022. The Granger Causality Test and the Dynamic Conditional Correlation (DCC-Garch) Model were employed to conduct this analysis. Furthermore, a sensitivity analysis was performed to identify crisis periods concerning the sample period and provide an analysis of DCC-Garch results during extreme market conditions like the COVID-19 pandemic and the Russian invasion of Ukraine. The Granger Causality Test results present a unidirectional causality running from the Green Bond Index to the oil price returns. Also, there is a unidirectional causality running from the Green Bond Index to the CO2 futures' returns. Additionally, a unidirectional causality runs from the oil price returns to the CO2 futures' returns. The results for the DCC-Garch indicate a positive dynamic correlation between the Brent oil price return and the CO2 futures' returns. Finally, the Green Bond Index shows a negative dynamic correlation to the oil return and the CO2 futures' returns presenting a strong correlation in uncertainty periods.

The impact of COVID-19 pandemic on the volatility of conventional and Islamic stock indexes: a comparative study on ASEAN and GCC countries

Purpose The purpose of the study is to adopt Morlet's wavelet method to examine the differences in the level of volatility (i.e. riskiness) between the conventional and Shari'ah indexes during the COVID-19 pandemic (February 4 to June 19, 2020) on selected Association of South East Asian Nation (ASEAN) and Gulf Cooperation Council (GCC) countries. As a comparison, the equivalent time period of relative tranquillity is used;February 4 to June 19, 2019. Design/methodology/approach Morlet's wavelet method is used in analyzing the volatility levels for both the conventional and Shari'ah indexes before and during the COVID-19 pandemic for the selected ASEAN and GCC countries. Findings This study has several findings;first, the markets in the ASEAN region appear to be more volatile during the pandemic than in the GCC region. Second, most of the Shari'ah indexes were more volatile during the COVID-19 pandemic than their conventional counterparts. Nevertheless, the GCC index pairs appear to show more similarities between both the Shari'ah and conventional index. Practical implications The findings from this study indicate that investors, government, regulators and all other stakeholders should stay vigilant during a pandemic or health threat period as it has become a pertinent source of volatility spillovers. As such, investors should devise optimal asset allocation strategies, portfolio diversification and portfolio rebalancing measures, taking into consideration not only financial adversity but also public health gravity as a potential source of turbulent markets. Originality/value This study uses the wavelet method to examine the volatility level of both the Shari'ah and conventional indexes during the COVID-19 pandemic and its equivalent time frame in 2019. It has further added to the Islamic literature by comparing the volatility between selected ASEAN and GCC countries. The wavelet method is most appropriate for short-duration studies as it captures both the time and frequency domains of the time-series behavior.

High-frequency connectedness between Bitcoin and other top-traded crypto assets during the COVID-19 crisis

In this paper, we analyse co-movements and correlations between Bitcoin and thirty-one of the most-tradable crypto assets using high-frequency data for the period from January 2019 to December 2020. We apply the Diagonal-BEKK model to data from the pre-COVID and COVID-19 periods, and identify significant changes in patterns of co-movements and correlations during the pandemic period. We also employ the Minimum Spanning Tree (MST) and Planar Maximally Filtered Graph (PMFG) methods to study the changes of the crypto asset network structure after the COVID-19 outbreak. While the influential role of Bitcoin in the digital asset ecosystem has been confirmed, our novel findings reveal that due to recent developments in the blockchain ecosystem, crypto assets that can be categorised as dApps and protocols have become more attractive to investors than pure cryptocurrencies.

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.

Co-movements in sector price indexes during the COVID-19 crisis: Evidence from the US.

This paper is an examination of co-movements between sector indexes in the United States prior to and during the COVID-19 period. Using daily data between January 2013 and July 2020, this study is the first to examine sectoral cointegration, as well as how contagion occurs from one healthcare sector to others. We find that only five sectors reacted to the shock to the healthcare sector. Our findings can assist policymakers in appropriately responding to the current crisis and tackling potential pandemics in the future. Our findings are also valuable for stockholders in terms of predicting price changes and improving portfolio diversification.

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.