ABSTRACT
We use the time-varying parameter structural vector autoregression stochastic volatility (TVP-SVAR-SV) and causality-in-quantiles methods to explore the linkage between market liquidity and efficiency in the European Union Emissions Trading Scheme (EU ETS) during Phase III. Our results show that two-way causality existed under normal and lower market conditions. Additionally, the linkage between liquidity and efficiency exhibits time-varying characteristics. Except in cases of extremely high market liquidity, the pass-through effect of liquidity on efficiency is mostly positive in the long run. The linkage is stronger in the medium and long term, but the response of liquidity to efficiency shocks is more complicated. Market efficiency has an overall inhibitory effect on liquidity in the short term and a promoting effect in the medium and long term. Furthermore, we investigate the impulse response during the COVID-19 period and the war between Russia and Ukraine and find that improvements in efficiency will permanently damage liquidity. Overall, the abilities of market makers and arbitrage traders, impacted by multiple factors, play an important role in the process by which liquidity affects market efficiency. By revealing and explaining the dynamic relationship between liquidity and efficiency, this research provides valuable information for policymakers and various market participants.
ABSTRACT
We examine the daily dependence and directional predictability between the returns of crude oil and the Crude Oil Volatility Index (OVX). Unlike previous studies, we apply a battery of quantile-based techniques, namely the quantile unit root test, the causality-in-quantiles test, and the cross-quantilogram approach. Our main results show evidence of significant bi-directional predictability that is quantile-dependent and asymmetric. A significant positive Granger causality runs from oil (OVX) returns to OVX (oil) returns when both series are in similar lower (upper) quantiles, as well as in opposite quantiles. The Granger causality from OVX returns to oil returns is only significant during periods of high volatility, although it is not always positive. The findings imply that the forward-looking estimate of oil volatility, reflecting the sentiment of oil market participants, should be considered when studying price variations in the oil market, and that crude oil returns can be used to predict oil implied volatility during bearish market conditions. Therefore, the findings have implications regarding predictability under various conditions for oil market participants.
ABSTRACT
COVID-19 unexpectedly ensnared the entire world and wreaked havoc on global economic and financial systems. The stock market is sensitive to black swan events, and the COVID-19 disaster was no exception. Against this backdrop, this study explores the impact of COVID-19 and economic policy uncertainty (EPU) on Chinese stock markets' returns for the period spanning January 23, 2020 to August 04, 2021. The outcomes of the novel quantile-on-quantile regression analysis revealed that both COVID-19 and EPU had a significant negative impact on both Shanghai and Shenzhen stock market returns, while COVID-19 aggravated the level of economic uncertainty in both financial markets. The quantile causality approach of Troster et al. (2018) validates our main estimations. We conclude that COVID-19 and a high level of EPU enervated the returns of China's leading stock markets. Our study provides key insights to policymakers and market participants to determine the behavior of China's stock market returns vis-à-vis COVID-19 during the peak of the pandemic and beyond. Specifically, our findings apprise portfolio investors to augment their portfolio diversification fronts.
ABSTRACT
As part of the artificial intelligence (AI) industry there are many companies engaged in providing hardware that enhances the use of artificial intelligence technology for big data analysis, along with companies that are involved in data analytics, software, system software, and artificial intelligence software. This paper examines the quantiles-based connectedness and non-linear causality-in-quantiles nexus of AI enterprises with basic materials and oil & gas companies, and their Islamic markets. Formally, we consider two perspectives, including before and after the pandemic of COVID-19 (for period May 18, 2018–June 01, 2022). It is observed that in the network of AI-based investments and companies related to basic materials and oil & gas industries, AI is a net recipient of shocks before and during the COVID-19 era, with a higher intensity of shock-receiving in the normal market and during COVID-19-affected period than in the upper and lower tails and prior to COVID-19 period. However, AI could serve as the cause-in-quantiles of oil & gas-related companies in the Islamic markets (in both pre-COVID-19 and COVID-19 timeframes) and conventional oil & gas firms (only within COVID-19). On the other hand, both the Islamic and the conventional basic materials and oil & gas businesses appear to be a non-linear cause-in-variance of the AI technology in the middle quantiles of the COVID-19 situation. Aside from this, the only causal factors from resources-based markets to AI are Islamic and conventional basic materials companies, as observed only during COVID-19. Based on our analysis, COVID-19 presented an excellent opportunity for improving the involvement of AI innovations with basic materials and oil & gas companies. As a consequence, the basic materials market may be able to provide hardware and software infrastructures to support the technology of artificial intelligence. Also, the inventions that enter the oil & gas industry due to the use of artificial intelligence could have a significant impact on their average performance. In this light, AI could be recognized as a strategic link in the supply chain of basic materials and oil & gas companies. There are many implications arising from these new insights for the developers of AI applications, resource policy-makers and managers, as well as investors who are interested in investing in new technologies.
ABSTRACT
Little attention has been paid to the effects of climate oscillations on the performance of renewable energy stock markets, although many studies have examined the instability of these markets caused by various external shocks. This paper aims to investigate the heterogenous impacts of El Niño-Southern Oscillation (ENSO) on renewable energy stock markets under different market conditions using a quantile framework. Our results show that, firstly, ENSO has significant shocks on the EU renewable energy stock market in most market conditions, whereas it has no significant influence on the US market. Secondly, there is an obvious asymmetry in the responses of the EU renewable energy stock markets under bullish and bearish markets to ENSO, respectively. Thirdly, strong La Niña events appear to have larger impacts than those of strong El Niño on the EU renewable energy stock markets. Finally, the good performance of the EU renewable energy stock markets can be deteriorated by strong La Niña events during the COVID-19 crisis. These findings can not only help us to understand the heterogenous shocks of ENSO on different renewable energy markets, but also provide deeper insights on efficient managements of extreme climate risks to renewable energy stock markets.
ABSTRACT
We aim to document the impact of cryptocurrencies on China's carbon price variation using some quantile techniques during COVID-19 with the daily data spanning from August 7, 2015 to April 30, 2021. In this paper, we show that cryptocurrencies have a very strong explanation power for carbon market with the non-parametric causality-in-quantiles method. In addition, cryptocurrencies can work as a good hedging candidate for carbon market at different investment horizons with the quantile coherency approach. Using hedging effectiveness measure, we further show that COVID-19 can reverse the optimal hedging ratios in our portfolio specification in cryptocurrencies‑carbon emission trading pairs while this pandemic does not have effects on the trading effectiveness. Finally, the heterogeneity and asymmetry features in the dynamic quantile-on-quantile effects are detected and the effects on carbon efficient index show relatively strong fluctuation while on carbon emission trading market are relatively strong in magnitude. Our empirical results conclude with many potential applications for policymakers and investors.
ABSTRACT
Vigorously developing the clean energy industry, improving the carbon allowance trading scheme, and issuing green bonds can effectively reduce emissions. To this end, this study aims to investigate the time-varying connections among clean energy, carbon, and green bonds through the DCC-MIDAS model, thus providing a bird's-eye view of their dynamic nexus. A non-parametric causality-in-quantile method is also employed to adequately capture the asymmetric causation of economic policy uncertainty (EPU) and the oil volatility index (OVX) on cross-asset correlations under different market conditions. The primary results imply complicated links among these three assets, with alternating positive and negative trends throughout the sample period. Notably, turbulence in financial markets can exacerbate network connectivity, particularly during the COVID-19 pandemic. Moreover, EPU and OVX can serve as strong predictors across various distributions of cross-market connections, which indicates that co-movement between assets is vulnerable to exogenous risks, especially under normal market conditions. Our findings have broader implications for market participants and policymakers.
ABSTRACT
Purpose: This article aims to uncover the impact of Google Trends on cryptocurrency markets beyond Bitcoin during the time of increased attention to altcoins, especially during the COVID-19 pandemic. Design/methodology/approach: This paper analyses the nexus among the Google Trends and six cryptocurrencies, namely Bitcoin, New Economy Movement (NEM), Dash, Ethereum, Ripple and Litecoin by utilizing the causality-in-quantiles technique on data comprised of the years January 2016–March 2021. Findings: The findings show that Google Trends cause the Litecoin, Bitcoin, Ripple, Ethereum and NEM prices at majority of the quantiles except for Dash. Originality/value: The findings will help investors to develop more in-depth understanding of impact of Google Trends on cryptocurrency prices and build successful trading strategies in a more matured digital assets ecosystem. © 2022, Emerald Publishing Limited.
ABSTRACT
Combining advanced quantile-on-quantile (QQ) regression and causality-in-quantiles (QC) methods, we examine the asymmetric effects of non-ferrous metal price shocks on clean energy stocks at aggregate and sub-sector levels. From the aggregate perspective, the impact of non-ferrous metal price shocks is strongly negative for bull clean energy stock markets but is positive under bear circumstances. According to QC analysis, non-ferrous metal price shocks can effectively predict returns on clean energy stocks in some quantiles. Sub-sectors of clean energy stocks react differently, proving the heterogeneity of different industries. Synergistic movements between non-ferrous metal price shocks and some clean energy sectors in bear markets are detected, indicating that non-ferrous metals are not safe havens for clean energy stock markets under extreme market conditions. Furthermore, non-ferrous metals have a significantly stronger negative impact on clean energy stocks during the epidemic, demonstrating the structural changes effect of COVID-19.
ABSTRACT
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.
ABSTRACT
Using a rare disaster risk database from almost the last one hundred years, we examine the differences in the reaction of asset prices to rare disaster risk between commodity and financial assets. We first employ time-varying parameter VAR (TVP-VAR) models to investigate the role of rare disaster risk in the price dynamics of major asset markets. The results indicate that disaster risk generally has a more intense and persistent impact on crude oil and stock markets when compared to gold and bond markets. However, the role of rare disaster risk differs substantially between commodity and financial assets, as well as between the short and long term. Moreover, when using a nonparametric causality-in-quantiles method to detect causal relationships, we provide evidence of the nonlinear causality effect of rare disaster risks on asset volatilities, and not their returns, except for crude oil. In addition, we demonstrate that augmenting a diversified portfolio of stock or bonds with gold can significantly increase its risk-adjusted performance. The findings have important implications for investors as well as policymakers.
ABSTRACT
This research examines the impact of the COVID-19 on cryptocurrencies' connectedness by employing two techniques: TVP-VAR-based connectedness and causality in the quantiles. First, the TVP-VAR-based connectedness unveils that cryptocurrencies act as a net receiver and transmitter of shocks, with Bitcoin, Ethereum are the highest transmitters among others. Moreover, the causality-in-quantile test shows that COVID-19 significantly causes spillover connectedness among cryptocurrencies, mainly at the quantiles ranging from 0.1 to 0.8, while an insignificant causal relationship is found in few cases. The study has implications for investors and policymakers.
ABSTRACT
With many commodity and financial markets reportedly experiencing poor performances during this COVID-19 pandemic, this study intends to examine the effect of the pandemic on the connectedness among the markets. There are several reasons that suggest that apart from the pandemic affecting the performances of the markets, it can also be a driver of their connectedness, coming from the perspective of the global financial cycle channel. Therefore, we first employ the recently developed time-varying parameter vector autoregressions (TVP-VAR) technique to examine the volatility spillover among the commodity and financial assets. We find evidence of strong volatility across the markets, with gold and USD being net receivers of shocks, and others, net transmitters. With this evidence, we proceed to the evaluation of the influence of the COVID-19 pandemic on the connectedness across the markets using both the linear and non-linear (causality-in-quantiles) causality tests. The causality-in-quantiles test outperforms the linear Granger-causality test, and the results show significant causal impacts of the two measures of COVID-19 pandemic (infectious diseases-based equity market volatility and the growth rate of the U.S. COVID-19 reported cases) on the connectedness across the markets, especially at the lower and middle-level quantiles. Overall, these findings prove that the pandemic has been largely responsible for risks transmission across various commodity and financial markets. This is because it has significantly raised investors' and policy uncertainties and immensely altered global financial cycle which in turn results in global flows of capital, and movements in the prices of assets across different financial markets.