Price bubbles and Co-bubbles in the green economy market.

In light of growing concerns about climate change and environmental issues, investor interest has surged in the new green economy market. However, the existing literature is limited regarding potential price bubbles and co-bubbles within this new domain. This study examines price bubbles and co-bubbles in the new green economy market, covering 31 indexes classified into three groups: the green economy market and its components, geographical regions, and sectors. Using daily data from August 31, 2005, to May 31, 2024, a test procedure is first applied to detect periods of price bubble in the various indexes, then logistic regressions are employed to examine price co-bubble behaviours. The results show evidence of price bubbles in the green economy market, particularly in solar and wind indexes, with peaks during the COVID-19 pandemic and Russia-Ukraine conflict, whereas the water index is the least prone to price bubbles. Regarding geographical region, the USA market exhibits a higher tendency for price bubbles than the Asian or European markets. Several sectors are resistant to price bubbles. The co-bubble analysis reveals a strong reliance of wind index on price bubbles in the solar and water indexes. Price bubbles in Asia significantly influence price bubbles in Europe and the USA. These findings have implications for investment portfolio management and risk management strategies in the new green economy market.

Do green finance and green innovation affect corporate credit rating performance? Evidence from machine learning approach.

This study investigates the impact of green finance (GF) and green innovation (GI) on corporate credit rating (CR) performance in Chinese A-share listed firms from 2018 to 2021. The least absolute shrinkage and selection operators (LASSOs) machine learning algorithms are first used to select the critical drivers of corporate credit performance. Then, we applied partialing-out LASSO linear regression (POLR) and double selection LASSO linear regression (DSLR) machine learning techniques to check the impact of GF and GI on CR. The main results reveal that a 1% increase in GF diminishes CR by 0.26%, whereas GI promotes CR performance by 0.15%. Moreover, the heterogeneity analysis reveals a more significant negative effect of GF on the CR performance of heavily polluting firms, non-state-owned enterprises, and firms in the Western region. The findings raise policies for managing green finance and encouraging green innovation formation, as well as addressing company heterogeneity to support sustainability.

The heterogeneous reaction of green and conventional bonds to exogenous shocks and the hedging implications.

Unlike most previous studies considering the yields on green bonds versus conventional bonds or the hedging ability of green bonds against downside market risk, the main purpose of this paper is to paper examine the short-term response of green and conventional bonds to the Russia-Ukraine conflict shock and the US Federal monetary policy tightening. Using daily data from August 3, 2021 to March 29, 2022, this paper conducts an event-based study (Cumulative Abnormal Returns, CAR) and then applies a hedging analysis in the context of increasing geopolitical risk and financial stress. The analysis reveals that green bonds exhibit a stronger reaction to the Russia-Ukraine conflict and the US Federal rate hike than conventional, municipal, and treasury bonds in different time frames. Compared to conventional, municipal, and treasury bonds, green bonds offer lower negative CAR responses during the event window and the [-5, +5] period, suggesting a rigidity feature. The dynamic correlation and hedging analysis indicate that green bonds, unlike the other bonds indices, have a negative dynamic correlation with both geopolitical risks and financial stress, implying a hedging ability around the conflict shock and the Federal tightening cycle. These findings enrich the existing literature on green bonds, offering a wide range of applications for investment managers and policymakers.

Hedging the Risks of MENA Stock Markets with Gold: Evidence from the Spectral Approach.

In this paper, we contribute to the old debate on the dynamic correlation between gold and stock markets by considering a spectral approach within the framework of portfolio hedging. Specifically, we consider eight MENA stock markets (Tunisia, Egypt, Morocco, Jordan, UAE, Saudi Arabia, Qatar, and Oman) and examine the optimal composition between gold and the stock market index, with a minimum portfolio risk and a high expected return. Based on the spectral approach, we propose seven portfolio structures and evaluate them through a comparison with the conventional DCC-GARCH method and the most best 10 portfolios constructed by using wavelet approach. The main results show that the spectral-based approach outperforms the DCC-GARCH and the wavelet methods. In fact, the optimal gold-stock composition depends on the spectral density of each stock market index, where a stock market index with a stable spectral density requires more investments in gold than a stock market index with an unstable spectral density.

The resilience of cryptocurrency market efficiency to COVID-19 shock.

We examine the price disorder and informational efficiency of five cryptocurrencies (Bitcoin, BNB, Cardano, Ethereum, and XRP) before and during the COVID-19 pandemic. In this sense, we estimate the permutation entropy and Fisher information measure (FIM). We use these complexity measures to construct the Shannon-Fisher causality plane (SFCP) to map these cryptocurrencies and their respective locations in a two-dimensional plane and then apply the sliding time window approach to study the temporal evolution of informational efficiency. All cryptocurrencies exhibit high but slightly varying informational efficiency during both periods. Cardano was the most efficient cryptocurrency. These results might point to the increasing maturity and lower potential for price predictability, which matter to cryptocurrencies' usage for liquidity risk diversification strategy.

Is Bitcoin's Carbon Footprint Persistent? Multifractal Evidence and Policy Implications.

The Bitcoin mining process is energy intensive, which can hamper the much-desired ecological balance. Given that the persistence of high levels of energy consumption of Bitcoin could have permanent policy implications, we examine the presence of long memory in the daily data of the Bitcoin Energy Consumption Index (BECI) (BECI upper bound, BECI lower bound, and BECI average) covering the period 25 February 2017 to 25 January 2022. Employing fractionally integrated GARCH (FIGARCH) and multifractal detrended fluctuation analysis (MFDFA) models to estimate the order of fractional integrating parameter and compute the Hurst exponent, which measures long memory, this study shows that distant series observations are strongly autocorrelated and long memory exists in most cases, although mean-reversion is observed at the first difference of the data series. Such evidence for the profound presence of long memory suggests the suitability of applying permanent policies regarding the use of alternate energy for mining; otherwise, transitory policy would quickly become obsolete. We also suggest the replacement of 'proof-of-work' with 'proof-of-space' or 'proof-of-stake', although with a trade-off (possible security breach) to reduce the carbon footprint, the implementation of direct tax on mining volume, or the mandatory use of carbon credits to restrict the environmental damage.

Impact of institutional quality on sustainable development: Evidence from developing countries.

Efficient environmental resource management is a serious concern for sustainable development in developing countries. This study determines the impact of institutional quality on sustainable development, based on total factor productivity improvements through the environmental regulatory process by way of abatement policies using an augmented endogenous sustainable growth model. Based on panel data covering 66 developing countries from 1984 to 2019, the employed methods involve the fixed effects and the system generalized method of moments (GMM). The main results indicate that institutional quality has a positive impact on sustainable development. Institutional quality has a more positive role in sustainable development in lower middle-income countries than low-income countries. The overall results indicate that the disaggregated performance of institutional quality variables is higher in lower middle-income countries than low-income countries. Two main policy implications are implied by our analyses: legislative backing in the form of institutional enforcement is mandatory to design efficient and productive policy relevant to environmental resource management; and various institutional forms should be considered when designing environmental resource protection policy from an environmental governance point of view.

Asymmetric efficiency of cryptocurrencies during COVID19.

In this study, we examine the asymmetric efficiency of cryptocurrencies using 1-hour data of Bitcoin, Ethereum, Litecoin, and Ripple. In doing so, we utilize the asymmetric multifractal detrended fluctuation analysis (MF-DFA). We find significant asymmetric multifractality in the price of cryptocurrencies and that upward trends exhibit stronger multifractality than downward trends. Using the time-varying deficiency measure, we show that the COVID-19 outbreak adversely affected the efficiency of the four cryptocurrencies, given a substantial increase in the levels of inefficiency during the COVID-19 period. Bitcoin and Ethereum are the hardest hit, and at the same time, these two largest cryptocurrencies recovered faster at the end of March 2020 from their sharp dip towards inefficiency. The findings confirm previous evidence that market efficiency is time varying; also, unprecedented catastrophic events, such as the COVID-19 outbreak, have adverse effects of on the efficiency of leading cryptocurrencies.

On the Dynamics of International Real-Estate-Investment Trust-Propagation Mechanisms: Evidence from Time-Varying Return and Volatility Connectedness Measures.

In this paper, we investigate the time-varying interconnectedness of international Real Estate Investment Trusts (REITs) markets using daily REIT prices in twelve major REIT countries since the Global Financial Crisis. We construct dynamic total, net total and net pairwise return and volatility connectedness measures to better understand systemic risk and the transmission of shocks across REIT markets. Our findings show that that REIT market interdependence is dynamic and increases significantly during times of heightened uncertainty, including the COVID-19 pandemic. We also find that the US REIT market along with major European REITs are generally sources of shocks to Asian-Pacific REIT markets. Furthermore, US REITs appear to dominate European REITs. These findings highlight that portfolio diversification opportunities decline during times of market uncertainty.

The predictive power of Bitcoin prices for the realized volatility of US stock sector returns.

This paper is motivated by Bitcoin's rapid ascension into mainstream finance and recent evidence of a strong relationship between Bitcoin and US stock markets. It is also motivated by a lack of empirical studies on whether Bitcoin prices contain useful information for the volatility of US stock returns, particularly at the sectoral level of data. We specifically assess Bitcoin prices' ability to predict the volatility of US composite and sectoral stock indices using both in-sample and out-of-sample analyses over multiple forecast horizons, based on daily data from November 22, 2017, to December, 30, 2021. The findings show that Bitcoin prices have significant predictive power for US stock volatility, with an inverse relationship between Bitcoin prices and stock sector volatility. Regardless of the stock sectors or number of forecast horizons, the model that includes Bitcoin prices consistently outperforms the benchmark historical average model. These findings are independent of the volatility measure used. Using Bitcoin prices as a predictor yields higher economic gains. These findings emphasize the importance and utility of tracking Bitcoin prices when forecasting the volatility of US stock sectors, which is important for practitioners and policymakers.

Asymmetric effects of climate policy uncertainty, geopolitical risk, and crude oil prices on clean energy prices.

We investigate the asymmetric effects of climate policy uncertainty (CPU), geopolitical risk (GPR), and crude oil prices (WTI) on the realized volatility of the returns of clean energy prices (CEP) in the USA. Using the non-linear autoregressive distributed lags (NARDL) model on data from January 2001 to December 2021, we provide evidence that the effects of CPU, GPR, and WTI on CEP's returns and realized volatility differ in the short and long run and are asymmetric. An increase and decrease in CPU affect CEP's realized volatility more than returns in the long run. Notably, an increase in CPU positively affects the CEP's returns, and a decrease negatively affects CEP's returns in the short run. Moreover, an increase in GPR exerts higher effects on returns in the short run, while both an increase and a decrease in GPR have significant long-run effects. An increase or decrease in WTI shows higher effects on CEP's returns and realized volatility in the long run, while an increase in WTI shows short-run effects. Our findings provide valuable information for making investment decisions while considering the asymmetric effects of climate policy uncertainty, geopolitical risks, and crude oil prices.

Spillovers from global economic policy uncertainty and oil price volatility to the volatility of stock markets of oil importers and exporters.

Economic policy uncertainty generally tends to induce a pessimistic view of future market behaviour. Furthermore, instabilities in global oil prices have serious implications for the economies of oil exporters and importers, due to their over-dependence on crude oil for revenue and production activities, respectively, and thereby on stock market indices. Against limited empirical evidence, this study examines the spillover effects from global economic policy uncertainty (GEPU) and oil price volatility to the volatility of the stock market indices of oil exporters and importers in both developed and emerging economies. The results show that the spillover effect from GEPU to oil exporters is relatively smaller than to oil importers, for both developed and emerging countries. Conversely, the volatility spillovers from oil prices to oil exporters are relatively larger than to oil importers, for both developed and emerging countries. Specifically, the volatility spillovers from oil prices to oil exporters (importers) in emerging countries are relatively stronger compared to oil exporters (importers) in developed countries. The findings indicate that the volatility of the stock markets of emerging countries is more sensitive to global factors such as GEPU and oil price volatility, and that oil exporters and importers in emerging economies are more sensitive to oil price volatility than oil exporters and importers in developed economies, which is in line with previous studies.

Bitcoin and S&P500: Co-movements of high-order moments in the time-frequency domain.

Interactions between stock and cryptocurrency markets have experienced shifts and changes in their dynamics. In this paper, we study the connection between S&P500 and Bitcoin in higher-order moments, specifically up to the fourth conditional moment, utilizing the time-scale perspective of the wavelet coherence analysis. Using data from 19 August 2011 to 14 January 2022, the results show that the co-movement between Bitcoin and S&P500 is moment-dependent and varies across time and frequency. There is very weak or even non-existent connection between the two markets before 2018. Starting 2018, but mostly 2019 onwards, the interconnections emerge. The co-movements between the volatility of Bitcoin and S&P500 intensified around the COVID-19 outbreak, especially at mid-term scales. For skewness and kurtosis, the co-movement is stronger and more significant at mid- and long-term scales. A partial-wavelet coherence analysis underlines the intermediating role of economic policy uncertainty (EPU) in provoking the Bitcoin-S&P500 nexus. These results reflect the co-movement between US stock and Bitcoin markets beyond the second moment of return distribution and across time scales, suggesting the relevance and importance of considering fat tails and return asymmetry when jointly considering US equity-Bitcoin trading or investments and the policy formulation for the sake of US market stability.

Rare earth and financial markets: Dynamics of return and volatility connectedness around the COVID-19 outbreak.

This study examines the return and volatility connectedness between the rare earth stock market and clean energy markets, world equity, base metals, gold, and crude oil. Using daily data from September 21, 2010 to August 28, 2020, a time-varying parameter vector autoregression (TVP-VAR) approach to connectedness is applied to uncover the dynamics of connectedness during the entire period and the COVID-19 pandemic period. Volatility connectedness is generally stronger than return connectedness. However, the return and volatility connectedness pattern varies over the full sample period, exhibiting a significant spike following the abrupt COVID-19 outbreak in February-March 2020. The rare earth index shows a close interdependence with the clean energy, world equity, and oil indexes during the outbreak of the pandemic, though it mostly remains a return and volatility receiver over the entire period. During the COVID-19 outbreak, the rare earth stock index becomes more central to the network of connectedness for both return and volatility, showing strong interdependence with clean energy and world equity. The volatility of the rare earth stock index exhibits a strong interdependence with that of crude oil prices. Our findings help investors understand diversification benefits and investment protection. They support policymakers in developing strategies for lessening import dependence on rare earth metals.

Climate bond, stock, gold, and oil markets: Dynamic correlations and hedging analyses during the COVID-19 outbreak.

Adverse ecological effects have recently generated several eco-friendly investment opportunities including green and climate bonds. Although climate bonds have emerged as an appealing investment, little is known about their dynamic correlations and market linkages with US equities, crude oil, and gold markets, especially during stress times such as the COVID-19 outbreak, which are essential for asset allocation and hedging effectiveness. In this paper, we report time-varying correlations between climate bonds and each of the markets considered, which intensify during the COVID-19 pandemic. On average, climate bonds are negatively associated with US equities and have a near zero correlation with crude oil, whereas they are positively associated with gold. There is a bidirectional volatility linkage between climate bonds and the three indexes under study, whereas return linkages are marginal. The hedge ratio is positive for bond-gold, whereas it switches between positive and negative states for bond-stock and bond-oil, especially it switches more extremely during the COVID-19 outbreak. Although climate bonds provide the highest risk reduction in a portfolio containing US equities or gold as a part of a hedging strategy, their hedging effectiveness is considerably reduced during the pandemic. The findings have implications for markets participants aiming to green their portfolios and make them robust during stress times, enabling a smooth and speedy transition to a low-carbon economy.

Asymmetric volatility spillover among Chinese sectors during COVID-19.

Inter-sectoral volatility linkages in the Chinese stock market are understudied, especially asymmetries in realized volatility connectedness, accounting for the catastrophic event associated with the COVID-19 outbreak. In this paper, we examine the asymmetric volatility spillover among Chinese stock market sectors during the COVID-19 pandemic using 1-min data from January 2, 2019 to September 30, 2020. In doing so, we build networks of generalized forecast error variances by decomposition of a vector autoregressive model, controlling for overall market movements. Our results show evidence of the asymmetric impact of good and bad volatilities, which are found to be time-varying and substantially intense during the COVID-19 period. Notably, bad volatility spillover shocks dominate good volatility spillover shocks. The findings are useful for Chinese investors and portfolio managers constructing risk hedging portfolios across sectors and for Chinese policymakers monitoring and crafting stimulating policies for the stock market at the sectoral level.

Regime specific spillover across cryptocurrencies and the role of COVID-19.

The aim of this study is to examine the daily return spillover among 18 cryptocurrencies under low and high volatility regimes, while considering three pricing factors and the effect of the COVID-19 outbreak. To do so, we apply a Markov regime-switching (MS) vector autoregressive with exogenous variables (VARX) model to a daily dataset from 25-July-2016 to 1-April-2020. The results indicate various patterns of spillover in high and low volatility regimes, especially during the COVID-19 outbreak. The total spillover index varies with time and abruptly intensifies following the outbreak of COVID-19, especially in the high volatility regime. Notably, the network analysis reveals further evidence of much higher spillovers in the high volatility regime during the COVID-19 outbreak, which is consistent with the notion of contagion during stress periods.

Impact of the COVID-19 outbreak on the US equity sectors: Evidence from quantile return spillovers.

The aim of this study is to examine the extreme return spillovers among the US stock market sectors in the light of the COVID-19 outbreak. To this end, we extend the now-traditional Diebold-Yilmaz spillover index to the quantiles domain by building networks of generalized forecast error variance decomposition of a quantile vector autoregressive model specifically for extreme returns. Notably, we control for common movements by using the overall stock market index as a common factor for all sectors and uncover the effect of the COVID-19 outbreak on the dynamics of the network. The results show that the network structure and spillovers differ considerably with respect to the market state. During stable times, the network shows a nice sectoral clustering structure which, however, changes dramatically for both adverse and beneficial market conditions constituting a highly connected network structure. The pandemic period itself shows an interesting restructuring of the network as the dominant clusters become more tightly connected while the rest of the network remains well separated. The sectoral topology thus has not collapsed into a unified market during the pandemic.

Predictability of GCC stock returns: The role of geopolitical risk and crude oil returns.

Stock return predictability has always been one of the central themes of finance literature, given its crucial implications for investment decisions, risk management, and financial and monetary policymaking. This paper evaluates the in-sample and out-of-sample stock return predictive power of the global and Saudi geopolitical risk indices and crude oil returns in the context of six Gulf Cooperation Council (GCC) countries. Monthly data from February 2007 to December 2019 and the feasible generalized least square (FGLS) estimator for predictive modelling by Westerlund and Narayan (2012, 2015) are used. Global and Saudi GPR indices show weak evidence of in-sample predictability of excess stock returns. However, the out-of-sample forecasts show that only the global geopolitical risk index provides superior prediction in the context of Kuwaiti and Omani stock markets, compared to the historical average benchmark model. Crude oil prices are shown to be a better predictor in most cases, in both in-sample and out-of-sample forecast models The results imply that crude oil returns can be used for active prediction of GCC stock market returns, once econometric issues are accounted for. The findings remain mostly unaffected when excess risk adjusted returns are used.

Intraday return predictability: Evidence from commodity ETFs and their related volatility indices.

Using high-frequency data of crude oil, gold, and silver exchange-traded funds (ETFs) and their related volatility indices, we analyse patterns of intraday return predictability, also called intraday momentum, in each market. We find that intraday return predictability exists in all the markets, but the patterns of predictability differ for each market, with different half-hour returns, not necessarily the first half-hour returns of the trading day, exhibiting significant predictability for their last half-hour counterparts, depending on the specific market. The intraday return predictability is stronger on days of higher volatility and larger jumps. Substantial economic value can be generated by a market timing strategy which is constructed upon the intraday momentum, in all the markets under study. Possible theoretical explanations for the intraday return predictability are infrequent portfolio rebalancing investors and late-informed investors.