Relationship between financial inclusion and carbon emissions: International evidence.

The nexus between financial inclusion and carbon emissions is becoming an increasingly important topic, given the augmented awareness of the negative impacts of climate change and carbon emissions on the environment and human health. In this study, we examine the impact of financial inclusion on carbon emissions using the STIRPAT framework for 102 countries from 2004 to 2020. We measure financial inclusion as a composite index, using principal component analysis (PCA) from five financial inclusion proxies. Our robust panel regression estimations suggest an N-Shaped relationship between financial inclusion and carbon emissions. The N-shaped Environmental Kuznets Curve (EKC) implies that the impact of financial inclusion on carbon emission is nonlinear and changes from an inverted U-shaped to a U-shaped. This finding is strong in developing countries and weak in advanced countries. It is also robust across our two normalized measures of financial inclusion as well as across different estimation techniques. These findings suggest adapting a universal environmental strategy that enhances financial inclusion through strong and accessible financial systems, particularly for low-income countries. Our results further suggest that government authorities and policymakers need to develop well-directed and inclusive financial policies that consider the varying levels of governance, regulations, and income across countries.

Tail event-based sovereign credit risk transmission network during COVID-19 pandemic.

This paper investigates the interconnectedness between sovereign credit risk based on the tail event and network dynamics technique. Specifically, we examine the interdependence in upper tails of sovereign credit default swap in the case of fifteen most COVID-19 affected countries. Empirical findings indicate that connectedness among SCDS spreads changed over time and is higher during the COVID19 outbreak. Russia, Brazil, and China are the most credit risk emitter and receiver during the COVID-19 pandemic.

Liquidity connectedness in cryptocurrency market.

We examine the dynamics of liquidity connectedness in the cryptocurrency market. We use the connectedness models of Diebold and Yilmaz (Int J Forecast 28(1):57-66, 2012) and Baruník and Krehlík (J Financ Econom 16(2):271-296, 2018) on a sample of six major cryptocurrencies, namely, Bitcoin (BTC), Litecoin (LTC), Ethereum (ETH), Ripple (XRP), Monero (XMR), and Dash. Our static analysis reveals a moderate liquidity connectedness among our sample cryptocurrencies, whereas BTC and LTC play a significant role in connectedness magnitude. A distinct liquidity cluster is observed for BTC, LTC, and XRP, and ETH, XMR, and Dash also form another distinct liquidity cluster. The frequency domain analysis reveals that liquidity connectedness is more pronounced in the short-run time horizon than the medium- and long-run time horizons. In the short run, BTC, LTC, and XRP are the leading contributor to liquidity shocks, whereas, in the long run, ETH assumes this role. Compared with the medium term, a tight liquidity clustering is found in the short and long terms. The time-varying analysis indicates that liquidity connectedness in the cryptocurrency market increases over time, pointing to the possible effect of rising demand and higher acceptability for this unique asset. Furthermore, more pronounced liquidity connectedness patterns are observed over the short and long run, reinforcing that liquidity connectedness in the cryptocurrency market is a phenomenon dependent on the time-frequency connectedness.

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.

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.

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.

Air pollutants, economic growth and public health: implications for sustainable development in OECD countries.

The rapid economic growth over recent years and the resulting environmental pollution in OECD countries are a serious concern for the health of the general public. A comprehensive analysis of environmental pollutants, economic growth, and public health is done using data from 28 OECD economies from 2002 to 2018. Panel fully modified least squares and the panel vector error correction model are used. The results show that there is long-run causality from renewable energy and carbon dioxide (CO2) emissions to healthcare spending. Renewable energy and healthcare spending are positively and significantly related. It is concluded that investment in renewable energy leads to a reduction in air pollution, improvements in healthcare, and the promotion of economic growth.

The predictive power of oil price shocks on realized volatility of oil: A note.

This paper examines the predictive power of oil supply, demand and risk shocks over the realized volatility of intraday oil returns. Utilizing the heterogeneous autoregressive realized volatility (HAR-RV) framework, we show that all shock terms on their own, and particularly financial market driven risk shocks, significantly improve the forecasting performance of the benchmark HAR-RV model, both in- and out-of-sample. Incorporating all three shocks simultaneously in the HAR-RV model yields the largest forecasting gains compared to all other variants of the HAR-RV model, consistently at short-, medium-, and long forecasting horizons. The findings highlight the predictive information captured by disentangled oil price shocks in accurately forecasting oil market volatility, offering a valuable opening for investors and corporations to monitor oil market volatility using information on traded assets at high frequency.