Significant Granger Causality Research Articles

Asymmetric spillovers and resilience in physical and financial assets amid climate policy uncertainties: Evidence from China

Climate policy is essential for fostering sustainable development and addressing climate-related issues, while climate policy uncertainty (CPU) introduces complex challenges in providing a consistent framework for action. In this study, we delve into the spillover dynamics among China's domestic CPU, foreign CPU, physical assets, and financial assets across various market scenarios, through a quantile-based connectedness method and evidence from China. The findings reveal: (1) The spillover dynamics are asymmetric across quantiles and time, and the COVID-19 pandemic influences the total connectedness. (2) The complexity of spillover patterns in physical assets is likely contingent upon market conditions, policy sources, asset types, and industrial-chain positions, while the spillovers of financial assets are quantile-dependent and time-varying, with the agricultural equity market showing relative resilience to the pandemic. (3) There is no significant Granger causality in quantiles between domestic CPU and foreign CPU under the entire sample period, and the rankings regarding the spillovers from CPUs to assets vary across market conditions. Implications for policymakers, investors, and firms are provided.

The Relationship between Bitcoin and Nasdaq, U.S. Dollar Index and Commodities

This paper investigates the long-run interaction between Bitcoin and Nasdaq, U.S. Dollar Index and commodities by applying weekly data from 1 January 2017 until 21 May 2023. This study uses FMOLS, DOLS and CCR methods to examine the long-run association between the variables. The results reveal a positive and significant relationship between Bitcoin and Nasdaq, as well as a similar positive association between Bitcoin and Oil prices. Notably, the U.S. Dollar Index exhibits a negative and significant impact on Bitcoin. However, results show that Gold does not have significant impact on Bitcoin. Finally, the results show that there are significant Granger causality from Nasdaq, oil and gold to Bitcoin.

The Empirical Analysis of the Effects of the COVID-19 Pandemic Crisis on the South Korea and Europe Carbon Market

This study examines the impact of the COVID-19 pandemic on carbon credit prices in South Korea and Europe, aiming to analyze the implications for the South Korea and Europe carbon markets. The empirical analysis was based on the data of the Korean carbon credit and European carbon markets, which accounts for more than 80% of the global carbon credit market. The study is based on the daily Korean Allowance Unit (KAU) data from January 2015, when the Emissions Trading System (ETS) was first implemented, to December 2022, when the restrictions eased. For Europe, this study employed the European Union allowance (EUA) market data for the same period, using the EUA futures data, which expires in December every year and exhibit considerably higher liquidity than the EUA spot price. The results indicated that no significant Granger causality between the Volatility Index (VIX), EUA, and KAU during 2015 to 2019, before COVID-19 was identified. However, for 2020 to 2022 (the COVID-19 outbreak period), the null hypothesis of no Granger causality between the VIX and EUA at the 5% confidence level was rejected. These findings suggests that the economic crises and the consequent changes in the demand and supply of carbon emissions can affect the carbon credit price.

Investigating Factors Influencing Inflation in the USA

The determination of inflation has been extensively explored in economic literature and it remains an ongoing and developing field of study. Researchers have examined diverse econometric methodologies and analyzed data from varying time spans to identify the factors influencing inflation. In this research paper, we explore the factors influencing inflation in the USA by utilizing the monthly data from 2002 to 2020. To achieve our aim, we employ error correction model (ECM) and then granger causality method. The findings indicate that certain lagged changes in inflation, consumer price index (CPI) for fuel and utilities, CPI all city average and production price index play a significant role in accounting for the variations in inflation in the USA but the lagged changes in money supply are not a significant determinant in accounting for the fluctuations in inflation in the USA. Moreover, the results of causality analysis suggest that all factors have significant Granger causality on inflation, except money supply.

The dynamics of crude oil future prices on China's energy markets: Quantile‐on‐quantile and casualty‐in‐quantiles approaches

AbstractThis study employs the quantile‐on‐quantile method, casualty‐in‐quantiles method, and rolling window regression to investigate the impact of international crude oil future prices on the stock prices of both traditional and new energy sectors in China. The empirical results reveal that the effect of oil future prices on the energy stock market in China varies across quantiles and is easily affected by extreme events. Specifically, the impact of oil future prices on the new energy stock market is significant and volatile, while it is less volatile and displays a negative correlation with the traditional energy market. Furthermore, a concentrated positive correlation is observed in the middle and low quantile stages of the energy stock market. A significant Granger causality exists between oil future prices and the energy stock market in different quantiles. Those findings can provide useful guidance for policymakers, investors, and consumers.

Capital Market Volatility During Crises: Oil Price Insights, VIX Index, and Gold Price Analysis

Abstract The study aims to investigate capital market volatility during crises, exploring the relationships between three key financial indicators: oil prices, the VIX index, and gold prices, using monthly data covering the period from January 2013 to May 2023, based on the Granger causality approach and the impulse response function testing empirically the existence of the long-run relationship using Johansen multivariate approach and the estimation of the VAR/VECM model. By analysing their interdependencies, the research sheds light on how these indicators respond to economic turbulence. The study employs robust econometric methods to investigate causal relationships and predictive patterns, providing valuable insights for investors, policymakers, and analysts navigating uncertain financial landscapes. The findings reveal nuanced dynamics, such as the momentum in oil prices, the inverse relationship between oil prices and the VIX index, and a significant Granger causality relationship running from the VIX index to oil and gold prices. Furthermore, based on the impulse response patterns, the shock in the VIX index caused a notable oil price decrease in the second quarter after the shock, followed by oscillations. Gold prices exhibit a minor initial decline after the VIX shock, with no lasting effects.

Characteristics of Extreme Precipitation and Their Effect on Bean Yield in Rwanda

This study examines the characteristics of extreme precipitation and its effect on bean yield in Rwanda from 1981 to 2017. Over the study area, two rainy seasons, March to May (MAM) and September to December (SOND), account for approximately 80% of total rainfall. The spatial distribution of rainfall over Rwanda revealed that the first three EOF modes of MAM rainfall vary by 72.72 percent over the study period, while the first three EOF modes of SOND rainfall vary by 57.48 percent. The spatial analysis of extreme precipitation reveals that during wet years, the majority of the country's western regions receive significantly more precipitation than the rest. Throughout the study period, the analysis revealed an increasing trend in all extreme precipitation indices. After 2009, a significant increase in the number of wet days (R1mm) and consecutive dry days (CDD) was observed, as was an increase in the number of consecutive wet days (CWD) after 2005. From 1981 to 1989, bean yield increased; however, after 1990, the trend became stationary and began to decline after 2012. Except for R1mm, CDD, and CWD, which showed a negative correlation, the correlation analysis revealed a positive relationship between extreme precipitation indices and bean yield. Despite the fact that there is a weak negative correlation between bean yield and CDD, CDD can be used as a predictor of bean yield due to the significant Granger causality test at the 95% confidence level. Farmers are advised to use alternative methods to increase soil moisture and water holding capacity during the CDD period to avoid yield loss.

Implications of cryptocurrency energy usage on climate change

This study investigates the environmental implications of cryptocurrency energy consumption on climate change. Using a spectrum of approaches, including Granger causality across quantiles, cross-quantilograms, and dynamic connectedness, we provide novel evidence on the nexus between Bitcoin mining and climate change. First, we find a significant Granger causality between carbon dioxide (CO2) emissions and the energy usage of Bitcoin that concentrates on the right-tail quantiles. Second, we show that the directional predictability from hash rate, blockchain size, and Bitcoin returns to Bitcoin electricity consumption is heterogeneous. We also discover significant directional predictability between the energy usage of Bitcoin mining and CO2 emissions. Third, the dynamic connectedness results show that hash rate transmits the most substantial net spillover effects to CO2 emissions and Bitcoin electricity consumption. Accordingly, hash rate exerts a major influence on Bitcoin electricity consumption and climate change. This study highlights the necessity of stimulating technological advances in developing energy-efficient decentralized finance consensus algorithms to transform the cryptocurrency market into a climate-friendly market. The results provide policy implications by emphasizing the importance of cryptocurrency ecosystem decarbonization in addressing environmental concerns.

An analysis of the time-varying causality and dynamic correlation between green bonds and US gas prices

Using the GO-GARCH, ADCC, and DCC models, this study investigates the volatilities and conditional correlations between green bonds and US gas prices. Furthermore, we investigate the causality between the markets from 11th January 2013 to 8th September 2020 using Shi et al. (2018) time-varying causality and Breitung and Candelon (2006) frequency domain Granger-causality approaches. Hedge ratio results show that GO-GARCH has the highest hedging effectiveness for hedging green bonds with shale gas and natural gas. Natural gas appears to be the best hedge for green bond prices, according to estimates of hedging effectiveness. We find no significant Granger causality between green bonds and natural gas in the short- and long-term dynamics of the spectral Granger causality test, but we do find long-term Granger causality between green bonds and shale gas. We find strong significant bidirectional causal effects between green bonds and gas prices during bearish market conditions using the time-varying causality test. The evidence of a strong correlation between green bonds and shale gas and natural gas suggests that policymakers should consider both markets as interconnected in their policy formulation strategy. Furthermore, increasing the share of green bonds will undeniably support the transition to a low-carbon economy, fulfilling the promise of the Paris climate change agreement. Realizing the role of green bonds as diversifiers against the fluctuation of gas markets encourages portfolio investors who want to achieve higher investment performance to include green bonds in their portfolios.

The COVID-19 effects on agricultural commodity markets

ABSTRACT This study examines the effect of the COVID-19 pandemic on major agricultural commodity prices (cattle, cocoa, coffee, corn, cotton, hog, rice, soya oil, soybeans, soybean meal, sugar and wheat) using daily data from 1 January 2016 to 25 February 2022. We measured COVID-19 effect using a news-based sentiment index. A robust nonparametric Granger causality-in-quantiles test is used to test the effect of the COVID-19 sentiment on agricultural commodity prices and price volatility. We find significant Granger causality from the news-based COVID-19 sentiment to mean of the agricultural commodity prices in the lower and upper ranges of the quantiles. Moreover, findings show that the COVID-19 sentiment is also causal for variance of agricultural commodity prices, but only above the quantile ranges above the first quarter. Thus, COVID-19 is causal for large volatility changes in agricultural commodity prices. Accordingly, the extremely negative sentiment associated with COVID-19 has not only caused a price crash in agricultural markets, but also significantly increased market risk. Policymakers should be wary of the risks and vulnerabilities of agricultural commodities to extreme events, as well as the ramifications for producers and consumers throughout the economy.

Tourism demand forecasting from the perspective of mobility: a brand-new predictive variable generated from intercity population mobility big data

ABSTRACT Few studies have investigated tourism demand forecasting from a mobility perspective. We explore whether population mobility data improve the ability to forecast tourist volumes in scenic spots. We selected Jiuzhaigou and Mount Siguniang in China as study cases and ensured the robustness of the results using single- and multistep rolling evaluation techniques. The findings reveal a significant correlation, long-term cointegration, and two-way Granger causality between intercity population flow and tourist flow. The comparison model with population flow as a variable outperforms the benchmark model. We prove the feasibility of leveraging population flow in tourism demand forecasting and suggest further research.

The Granger Causality Between Corruption and Economic Growth in ASEAN Countries

This paper studies the Granger causality between corruption and economic growth in ASEAN countries over the period of 1995-2019. Ten ASEAN countries are Brunei Darussalam, Cambodia, Indonesia, Lao People's Democratic Republic, Malaysia, Myanmar, Philippines, Thailand, Vietnam, and Singapore. The corruption is measured by the Corruption Perception Index published annually by Transparency International. The economic growth is measured by a country’s annual gross domestic product growth rate. There is a significant bidirectional Granger causality found between lower level of corruption and higher economic growth in Indonesia. It is shown that higher economic growth Granger causes lower level of corruption in Brunei Darussalam, and lower level of corruption Granger causes higher economic growth in Cambodia. No Granger causality between corruption and economic growth is found in the remaining seven ASEAN countries.

Economic complexity and entrepreneurship density: A non-linear effect study

This study examines the link between economic complexity and entrepreneurship density. Testing 53 economies over the period 2006-2016, we find a significant Granger causality from economic complexity to entrepreneurship density, but reverse causality is not supported. Also, we suggest that entrepreneurship density is an inverted-U shaped function of economic complexity. That is, an increase in economic complexity initially facilitates entrepreneurship density as more business opportunities are created. However, beyond a specific threshold, an increase in economic complexity induces higher risks and uncertainties, discouraging entrepreneurial activities. The results remain robust by different estimators and in sub-samples of High-Income Economies and Low-and-Middle-Income Economies.

Emotions, moods and hyperreality: social media and the stock market during the first phase of COVID-19 pandemic

PurposeThis paper aims to expand the emerging literature on COVID-19 and the financial markets by searching for a relationship between the uncertainty of the first phase of the COVID-19 pandemic experienced through social media and the extreme volatility of the Italian stock market.Design/methodology/approachThe authors analyze the relationship between social media and stock market trends during the first phase of the COVID-19 pandemic through the lens of social theory and Baudrillard's simulacra and hyperreality theory. The authors conducted the data analysis in two phases: the emotional and Granger correlation analysis by using the KPI6 software to analyze 3,275,588 tweets for the predominant emotion on each day and observe its relationship with the stock market.FindingsThe research results show a significant Granger causality relation between tweets on a particular day and the closing price of the FTSE MIB during the first phase of the COVID-19 epidemic. The results highlight a strong relationship between social media hyperreality and the real world. The study confirms the role of social media in predicting stock market volatility.Research limitations/implicationsThe findings have theoretical and practical implications as they reveal the relevance of social media in our society and its relationship with businesses and economies. In an emergency, social media, as an expression of users' feelings and emotions, can generate a state of hyperreality that is strong correlated with reality. Since social media allows users to publish and share messages without any filter and mediation, the hyperreality generated is affected by highly subjective elements.Originality/valueThis research is different from the previous ones on the same topic because unlike previous studies, conducted under normal or simulated scenarios, this study is focused on the first phase of an unpredictable and unforeseen emergency event: the COVID-19 pandemic. This research adopts a multidisciplinary approach and integrates previous studies on the economic and financial effects generated by social media by applying well-known theories to a new and unexplored context. The study reveals the significant impact generated by social media on stock markets during a global pandemic.

Need of care in interpreting Google Trends-based COVID-19 infodemiological study results: potential risk of false-positivity

BackgroundGoogle Trends (GT) is being used as an epidemiological tool to study coronavirus disease (COVID-19) by identifying keywords in search trends that are predictive for the COVID-19 epidemiological burden. However, many of the earlier GT-based studies include potential statistical fallacies by measuring the correlation between non-stationary time sequences without adjusting for multiple comparisons or the confounding of media coverage, leading to concerns about the increased risk of obtaining false-positive results. In this study, we aimed to apply statistically more favorable methods to validate the earlier GT-based COVID-19 study results.MethodsWe extracted the relative GT search volume for keywords associated with COVID-19 symptoms, and evaluated their Granger-causality to weekly COVID-19 positivity in eight English-speaking countries and Japan. In addition, the impact of media coverage on keywords with significant Granger-causality was further evaluated using Japanese regional data.ResultsOur Granger causality-based approach largely decreased (by up to approximately one-third) the number of keywords identified as having a significant temporal relationship with the COVID-19 trend when compared to those identified by Pearson or Spearman’s rank correlation-based approach. “Sense of smell” and “loss of smell” were the most reliable GT keywords across all the evaluated countries; however, when adjusted with their media coverage, these keyword trends did not Granger-cause the COVID-19 positivity trends (in Japan).ConclusionsOur results suggest that some of the search keywords reported as candidate predictive measures in earlier GT-based COVID-19 studies may potentially be unreliable; therefore, caution is necessary when interpreting published GT-based study results.

Are environmental regulations holding back industrial growth? Evidence from China

To achieve sustainable economic development, many developing countries have continuously tightened their environmental regulations; for example, the Chinese government revised some policies to protect the environment, such as the Environmental Protection Law, and levied environmental taxes. Will these regulatory tools affect the growth of the industrial economy? What are the differences in the influence of various types of environmental regulations? Based on city-level panel data from China, this paper applied a difference-in-difference (DID) model and a bootstrap panel Granger causality test to investigate the relationship between two types of environmental regulations (command-and-control environmental regulation and market-Based incentive environmental regulation) and industrial growth. The results show that (1) Command-and-control environmental regulation (CAC) has a significant inhibitory effect on industrial growth. (2) In most regions, there is no significant Granger causality between market-based incentive environmental regulation (MBI) and industrial growth. (3) Compared with command-and-control environmental regulation, market-based incentive environmental regulation allows more flexibility for enterprises, which is more beneficial for technological innovation. Meanwhile, market-based incentive environmental regulation does little to suppress industrial growth.

Dynamic Cross-Market Volatility Spillover Based on MSV Model: Evidence from Bitcoin, Gold, Crude Oil, and Stock Markets

This paper examines the spillover effect between bitcoin, gold, crude oil, and major stock markets by using the MSV model with dynamic correlation and Granger causality. The empirical results of the DC-GC-MSV model are logically correct and convergent. The DIC test result has proved that the DC-GC-MSV model is better and more accurate. Bitcoin has no significant Granger causality spillover effect than other assets. As a safe haven product for stock assets, gold price has one-way spillover effect from stock market volatility. Moreover, crude oil has the highest correlation with the stock market. In the recent COVID-19 epidemic and the sluggish economic environment, investors need to consider a balanced asset allocation among low-correlation assets, medium-correlation assets, and high-correlation assets to reduce risks.

Bond yield and crude oil prices predictability

Using long-term government bond yield (LTY), corporate bond yields spread (DFY) and Treasury bill rate (TBL) as the proxies, we find bond yield can effectively predict WTI and Brent spot prices. In-sample analysis indicates that bond yield variables have substantial explanatory power on oil returns, and there are significant Granger causality relationships from LTY and DFY to oil returns. In out-of-sample forecast, bond yield variables defeat historical average benchmark as well as the competing predictors from both statistical and economic perspectives. Moreover, the predictive abilities of bond yield variables can be tremendously enhanced with multivariate prediction methods. We prove that the prediction power of bond yield variables partially stems from their abilities on capturing oil market sentiment. Our findings survive a series of robustness checks.

Impact of the COVID-19 Pandemic on Systemic Risk of Gulf Cooperation Council Banking

Motivated by a recognition of the increased vulnerability of the banking sector to the COVID-19 pandemic, we examine market-based systemic risk and connectedness in the banking sector of Gulf Cooperation Council member countries, which include Bahrain, Kuwait, the Kingdom of Saudi Arabia (KSA), Oman, Qatar, and the United Arab Emirates (UAE). First, we apply conditional value-at-risk and marginal expected shortfall to measure tail risk between banks. These measures increased the most for the UAE, whereas Oman was almost unaffected. Second, to analyze the spillover effects of financial systemic risk, we apply the Granger-causality network method. The results reveal a remarkable rise in the percentage and number of significant Granger-causality links between banks for Kuwait and KSA during the pandemic. Oman and Qatar experienced an unnoticeable increase in bank return connectedness. Furthermore, the study identifies the bank characteristics that provide a shelter from the systemic shocks of the pandemic. The study findings indicate that income diversification is the most important variable for enhancing bank stability amid the pandemic. Our findings provide policy-related implications for understanding and mitigating risk shock transmission and the containment of systemic financial risk, in addition to multiple future lines of research.

Exploring the linkages of Foreign Direct Investment in Spain since 1970’s: an ARDL approach

This paper analyzes the relationship between foreign direct investment (FDI), exports and economic growth in Spain using annual time series data for the period 1970 to 2016. To examine these linkages the autoregressive distributed lag (ARDL) bounds testing approach to cointegration for the long-run is applied. The results confirm a long-run relationship among the examined variables. The Granger causality test indicates a strong unidirectional causality between FDI and exports with direction from FDI to exports. Besides, the results for the relationship between FDI and economic growth are interesting and indicate that there is no significant Granger causality from FDI to economic growth and vice-versa.