Nonlinear Granger Causality Test Research Articles

Are cleaner energy and financial technologies needed? Contagion and causality evidence between global fintech markets, energy consumption, and environmental pollution

AbstractFinancial technology (FinTech) depends on high amounts of energy with an upward trend, possibly affecting emissions due to energy consumption (EC). The study investigates tail dependence, contagion, and nonlinear between FinTech, EC, and carbon dioxide emissions (CO2e) with MS-GARCH-copula and MS-GARCH-copula-causality with a daily sample covering 02 Jan 2012–28 December 2022. The method is a generalized version of single-regime GARCH-copula and causality tests to Markov-switching. Empirical results indicated that FinTech, EC, and CO2e series follow nonlinear processes in addition to unit roots as determined by BDS nonlinearity tests and a set of linear and nonlinear unit root tests. Further, for all series, heteroskedasticity and nonlinear forms of heteroskedasticity cannot be rejected by ARCH–LM and White heteroskedasticity tests, leading to the estimation of the series and their joint dynamics by MS-GARCH-copula and a new MS-GARCH-copula based nonlinear Granger-causality test, the RSGCC test, under two distinct regimes characterized with the low and high volatility for extreme tails of data. Positivity and significance of copula parameters under both regimes indicate a high degree of positive but asymmetric tail dependence and contagion between FinTech & EC, in addition to contagion between FinTech & CO2e and EC & CO2e. RSGCC results determine unidirectional causalities from EC to CO2e and from FinTech to CO2e, coupled with bidirectional causality between FinTech and EC, which enhance the dynamics due to feedback effects. The findings of this paper are of importance for two central Sustainable Development Goals. Results could also be used to bring the FinTech markets and EC to the attention of policymakers, researchers, and eco-friendliness-focused portfolio managers. Graphical Abstract

The impact of investor greed and fear on cryptocurrency returns: a Granger causality analysis of Bitcoin and Ethereum

PurposeThis study probes the psychological interplay between investor sentiment and the returns of cryptocurrencies Bitcoin and Ethereum. Employing the Granger causality test, the authors aim to gauge how extensively the Fear and Greed Index (FGI) can predict cryptocurrency return movements, exploring the intricate bond between investor emotions and market behavior.Design/methodology/approachThe authors used the Granger causality test to achieve research objectives. Going beyond conventional linear analysis, the authors applied Smooth Quantile Regression, scrutinizing weekly data from July 2022 to June 2023 for Bitcoin and Ethereum. The study focus was to determine if the FGI, an indicator of investor sentiment, predicts shifts in cryptocurrency returns.FindingsThe study findings underscore the profound psychological sway within cryptocurrency markets. The FGI notably predicts the returns of Bitcoin and Ethereum, underscoring the lasting connection between investor emotions and market behavior. An intriguing feedback loop between the FGI and cryptocurrency returns was identified, accentuating emotions' persistent role in shaping market dynamics. While associations between sentiment and returns were observed at specific lag periods, the nonlinear Granger causality test didn't statistically support nonlinear causality. This suggests linear interactions predominantly govern variable relationships. Cointegration tests highlighted a stable, enduring link between the returns of Bitcoin, Ethereum and the FGI over the long term.Practical implicationsDespite valuable insights, it's crucial to acknowledge our nonlinear analysis's sensitivity to methodological choices. Specifics of time series data and the chosen time frame may have influenced outcomes. Additionally, direct exploration of macroeconomic and geopolitical factors was absent, signaling opportunities for future research.Originality/valueThis study enriches theoretical understanding by illuminating causal dynamics between investor sentiment and cryptocurrency returns. Its significance lies in spotlighting the pivotal role of investor sentiment in shaping cryptocurrency market behavior. It emphasizes the importance of considering this factor when navigating investment decisions in a highly volatile, dynamic market environment.

Private and Public Investment: Are they Substitute or Complementary in Driving Economic Growth in Nigeria?

The paper investigates the relationship between government and private investment with a view to ascertaining their complementarity or substitutability in driving economic growth in Nigeria for the period of 1981-2021. To achieve the objective, nonlinear autoregressive distributed lags (ARDL) models and nonlinear Granger causality tests were employed to analyze annual secondary data which were sourced from the Central Bank of Nigeria Statistical Bulletin. Findings show that government investment expenditure displayed a positive and long-term influence on private investment, suggesting a “crowding-in” effect where increased public investment stimulates private investment thereby fostering overall economic growth. Economic adversities or reduced public investment exerted a substantial negative impact on private investment, underscoring the complementarity of government investment expenditure in driving economic growth in Nigeria. Also, the nonlinear Granger causality tests reveal unidirectional relationship as government investment expenditure causes private investment across various embedding dimensions, emphasizing the positive influence of government investment on private investment. The findings generally suggest the need for balanced fiscal policies, prioritizing productive government investments, encouraging private investment, controlling inflation, and making time-sensitive policy decisions to promote economic growth in Nigeria.

Examining the Dependence Structure Between Carry Trade and Equity Market Returns in BRICS Economies

This paper contributes to the literature on carry trade by investigating the dynamic correlation and the dependence structure between the US-dollar carry trade and equity markets in the (Brazil, Russia, India, China and South Africa (BRICS)) economies during sample observations that include regular and crisis periods. Furthermore, the nonlinear Granger causality test based on the feed-forward neural networks (FFNN) model assesses how global volatility predicts the dynamic correlation between the US-dollar carry trade and equity markets in BRICS. The paper finds the dynamic correlations between carry trade and equity markets in BRICS are more pronounced during most global crises. Moreover, the results of the symmetrised Joe Clayton (SJC) copula model showed that the lower tail dependence between the two series is higher during the various crises. Furthermore, the results of the empirical analysis show that global volatility predicts the dynamic correlations between carry trade and equity markets in BRICS only during crises. Asset managers and investors can benefit from this paper's findings regarding portfolio diversification, risk management, asset allocation, and hedging when dealing with equity assets and carry trades.

Foreign Exchange Reserves and Economic Growth of Brazil: A Nonlinear Approach

Foreign Exchange Reserves and Economic Growth of Brazil: A Nonlinear Approach

Dynamic network relationship between transportation and urban economy: A case study of China's high-speed rail as a new transportation technology

High-speed rail (HSR) stands as one of the most significant transportation technological advancements in the global railway industry over the past half-century, attracting considerable scholarly attention regarding its impact on urban economies. However, researches remain rare as to the dynamic evolution of the HSR netwok and the economic network, as well as their relationship. Our research tackles this issue by employing social network analysis (SNA) and a complex gravity model to construct China's HSR network and economic network. Furthermore, we apply the quadratic assignment procedure (QAP) and nonlinear Granger causality test to analyze the relationship between the two networks. Results reveal a significant correlation between China's HSR and economic network from 2008 to 2020, exhibiting heterogeneity in nonlinear causal relationships across different spatial scales. Notably, the strength of the relationship between the HSR netwok and the economic network follows an inverted “U” shape. This correlation is more pronounced within smaller administrative boundaries than inter-administrative regions. Additionally, the HSR netwok and the economic network in China's central and western regions tend to foster mutually reinforcing patterns of evolution. These findings shed light on the interactive processes between HSR development and economic growth in China, providing a foundation for policy formulation in emerging economies seeking to develop a new transportation technology and promote synergistic economic growth.

Detection of the nonlinear response of vegetation to terrestrial water storage changes in central Asian endorheic basins

The terrestrial water storage anomaly (TWSA) is an important parameter for assessing the land water budget, and it interacts well with terrestrial ecosystems via complex hydrological processes. Recently, the decline in central Asian terrestrial water storage (TWS) has threatened the health of local ecosystems. Therefore, it is of great significance to adopt an efficient approach to explore and identify the nonlinear relationship between two important indicators, i.e., the TWSA and normalized difference vegetation index (NDVI) in the arid central Asian endorheic basins. In this study, we analysed the long-term trends of the TWSA and NDVI, and identified the lag month (1 month) as the optimal moving window of the nonlinear Granger causality test embedded in random forest to detect the nonlinear NDVI change response of NDVI changes in vegetation to the TWSA from 2003 to 2015. There are decreasing trends in TWSAs over approximately 81.7% of the study area and the NDVI generally decreased resulting in approximately 36% vegetation browning in the study area. The nonlinear Granger unidirectional causes of the TWSA were responsible for 97.9% of the NDVI variation in the study area considering the optimal response time for moving windows. The causes of vegetation browning in the central Asian Aral Sea basin and vegetation greening in the basins of Northwest China could be mostly explained by the changes in TWS. Our findings contribute to understanding the nonlinear causal linkages between vegetation and the TWSA in endorheic basins, and these findings provide insights for obtaining terrestrial water consumption patterns and water resource management under the joint influence of climate change and human activities.

Revisiting the main driving factors influencing the dynamics of gross primary productivity in the Mongolian Plateau

By contributing to what is considered as the world's largest carbon flux, global climate change has been significantly altering terrestrial vegetation gross primary production (GPP) at a global level. Thus, a robust understanding of the main driving factors influencing GPP change is neccessary for increasing terrestrial carbon sequestration potential of a region. In this study, we conduct an analysis of the main driving factors that have influenced GPP change in the Mongolian Plateau in the 1982–2018 period. The results of our analysis indicate that the GPP of the plateau's total vegetated area has witnessed both growth and decline (of 51.16% and 48.48%, respectively) over the past 37 years, with the latter occurring mainly across northern Mongolia and northeastern Inner Mongolia. Water availability, temperature, and solar radiation are deemed the most crucial climatic factors behind GPP change in 51.21%, 15.51%, and 18.63%, respectively, of the plateau's total vegetated area, according to a nonlinear Granger causality test. These three factors also explain up to 31%, 25.20%, and 17.21% of GPP change in 82.78%, 64.23%, and 69.96%, respectively, of the plateau's total vegetated area, as 34.52%, 31.57%, and 48.62% of the total vegetated area affected by the abovementioned driving factors concomitantly exhibit time-lag effects. The average intensity and extent of the effects of extreme climate indices on GPP change are lesser than the effect of general climate change; up to 21.31% and 22.35% of the GPP change can be explained by the extreme precipitation and temperature in 47.41% and 34.33%, respectively, of the plateau's total vegetated area. Moreover, the standardized total effects of this study's structural equation model (SEM) show that precipitation has the greatest impact on the GPP change (0.42), followed by snow water equivalent (SWE) (–0.11), solar radiation (0.08), and soil moisture or near surface temperature (0.07). Overall, our study provides new insights into the effects of the aforementioned driving factors on GPP change in the Mongolian Plateau, which will be helpful in mitigating the adverse effects of future climate change in the region.

The extreme risk connectedness of the global financial system: G7 and BRICS evidence

Using daily money, stock, bond, foreign exchange, and credit markets data in the G7 and BRICS between 2006 and 2022, this paper investigates the extreme risk interconnectedness across countries and markets. Specifically, we propose a multilayer nonlinear extreme risk spillover network based on the CAViaR model and nonlinear Granger causality test to capture extreme risk spillovers across and within layers from static and dynamic perspectives, respectively. We find that the extreme risks of the G7 countries are higher than those of the BRICS countries. Simultaneously, extreme risks in the stock and foreign exchange markets are significantly higher than those in other markets. The stock market tends to be the net emitter of extreme risks, and the bond and credit markets tend to be the net recipients. During special event periods, BRICS countries (except Russia) tend to be net recipients of extreme risks. Our study provides new evidence on the interconnectedness of extreme risk across markets and countries, which has several practical implications for managing financial risks and maintaining the financial system’s stability.

A Study on the Spatial Correlation Effects of Digital Economy Development in China from a Non-Linear Perspective

Accurate analysis of the spatial correlation effects, spatial aggregation patterns, and critical factors in the development of China’s digital economy is of great significance to the high-quality development of China’s economy. Based on the monthly data of “The Tencent Internet Plus” digital economy index for 31 provinces in China from 2018 to 2020, the non-linear Granger causality test and social network analysis were applied to reveal the spatial correlation effects of China’s digital economy. The quadratic assignment procedure (QAP) was used to empirically examine the factors influencing the formation of non-linear spatial association networks. The results show that the spatially linked relationships of the digital economy in 31 Chinese provinces exhibit a significant non-linear spatially correlated network structure. Block model analysis reveals that the development of the digital economy between the four major sectors is closely linked, and the national linkage effect is significant. The results of the secondary assignment procedure indicate that capital stock, information infrastructure, and geographical proximity have a significant positive impact on the formation of spatial linkages in the digital economy. In contrast, technological innovation has a significant negative impact.

Which return regime induces overconfidence behavior? Artificial intelligence and a nonlinear approach

Overconfidence behavior, one form of positive illusion, has drawn considerable attention throughout history because it is viewed as the main reason for many crises. Investors’ overconfidence, which can be observed as overtrading following positive returns, may lead to inefficiencies in stock markets. To the best of our knowledge, this is the first study to examine the presence of investor overconfidence by employing an artificial intelligence technique and a nonlinear approach to impulse responses to analyze the impact of different return regimes on the overconfidence attitude. We examine whether investors in an emerging stock market (Borsa Istanbul) exhibit overconfidence behavior using a feed-forward, neural network, nonlinear Granger causality test and nonlinear impulse-response functions based on local projections. These are the first applications in the relevant literature due to the novelty of these models in forecasting high-dimensional, multivariate time series. The results obtained from distinguishing between the different market regimes to analyze the responses of trading volume to return shocks contradict those in the literature, which is the key contribution of the study. The empirical findings imply that overconfidence behavior exhibits asymmetries in different return regimes and is persistent during the 20-day forecasting horizon. Overconfidence is more persistent in the low- than in the high-return regime. In the negative interest-rate period, a high-return regime induces overconfidence behavior, whereas in the positive interest-rate period, a low-return regime induces overconfidence behavior. Based on the empirical findings, investors should be aware that portfolio gains may result in losses depending on aggressive and excessive trading strategies, particularly in low-return regimes.

Revisiting the finance-growth nexus in Hong Kong: fresh insights from nonparametric analysis

PurposeSince financial sector plays a critical economic role in Hong Kong, the current research aims to comprehensively analyze the association between financial development and economic growth in the country to draw correct conclusions about the impact that financial sector's development has on the growth of the economy. This requires both using of more comprehensive data that includes all or nearly all elements of the country's financial sector and utilizing advanced econometrics techniques to provide more reliable evidence based on the findings. In the study, both issues have been addressed more academically to aid the relevant authorities better.Design/methodology/approachThis study empirically examines the financial development-economic growth nexus in Hong Kong employing data covering 1980–2019. The quantile-on-quantile (QQ) approach of Sim and Zhou (2015) is utilized to investigate certain subtle aspects of the association linking financial development and economic growth. In addition, the authors benefit from applying the nonlinear Granger causality test of Diks and Panchenko (2006) to assess the variables' nexus in a nonlinear manner.FindingsIn contrast to the evidence of a unidirectional linkage documented in many related studies, the empirical findings suggest that a bi-directional relationship exists between financial development and economic growth for Hong Kong. This is a helpful input for the relevant policymakers and implies that they can set appropriate policies and regulations to balance financial development and economic growth in this country.Originality/valueThe originality of this study can be divided into two parts. Methodologically, unlike past studies that utilized mostly linear and parametric methods, the paper contributes to the literature by applying the more robust nonparametric and nonlinear methodologies. Theoretically, most researchers have used various financial development indicators, which led to very different conclusions. Therefore, this study attempts to resolve this deficiency in the literature by using a more comprehensive index for financial development.

Effect of Futures Trading Restrictions on Market Efficiency: A Multifractal Analysis

Based on the China Securities Index 300 (CSI 300 index) futures trading restrictions in 2015, this paper uses the multifractal detrending moving-average cross-correlation analysis method (MF-X-DMA) to investigate the effect of introducing futures trading restrictions on the market efficiency of CSI 300 index spot and futures markets. We begin by using multifractal detrending moving-average analysis (MF-DMA) and find that the futures trading restrictions improve spot market efficiency but decrease futures market efficiency. Moreover, we examine the cross-correlation between spot and futures markets and the information transmission process. MF-X-DMA analysis shows an increase in the level of persistent cross-correlation between spot and futures markets, and a decrease in the multifractality degree of cross-correlation, suggesting that the relationship between spot and futures markets becomes stronger and less complicated after the futures trading restrictions. Moreover, the nonlinear Granger causality test shows that futures returns do not Granger cause spot returns after the restrictions. Therefore, the futures trading restrictions may mitigate the harmful effect of speculative trading in the futures market and thus improve spot market efficiency.

Analysis and Research on Causality between Carbon Emission and Structural Optimization Based on Linear and Nonlinear Granger Causality Test

Although the tourism industry is booming, its scale imbalance, structural imbalance and inefficient allocation of resources have hindered the realization of the high-quality development goals of the regional tourism industry. Therefore, it is very important to optimize the tourism industry structure. This paper uses linear and nonlinear Granger causality tests to study the relationship between ’ tourism industrial structure optimization ’ and ’ carbon dioxide emissions ’ in 31 provinces in China. The results show that : ( 1 ) There is a nonlinear Granger two-way causal relationship between ’ tourism industrial structure optimization ’ and ’ carbon dioxide emissions ’. ( 2 ) With the acceleration of the upgrading and rationalization of the tourism industry structure, structural optimization and adjustment will help reduce carbon dioxide emissions. In addition, reducing carbon emissions can also promote the upgrading and rationalization of the tourism industry structure.

The relationship between geopolitical risk and crude oil prices: evidence from nonlinear and frequency domain causality tests

ABSTRACT This paper aims to investigate the possible explanatory effect of geopolitical risks on the oil price changes from February 1986 to December 2019 by employing the nonlinear bivariate Granger causality test and frequency domain Granger causality test based on the geopolitical risk index. The results suggest that there exists a nonlinear causality from geopolitical risks to crude oil prices. Moreover, geopolitical risks have a short-term impact on oil prices, less than 12 months. Actual geopolitical events have a smaller and less lasting impact on oil prices than pure geopolitical risks.

Currency internationalization and openness: A paradigm from renminbi

AbstractThis article intended to empirically test the interactive effects among renminbi (RMB) internationalization, trade and financial openness. Based on the monthly data from the first month of 2011 to the sixth month of 2018, we first build a vector error correction model includes RMB internationalization index, financial openness, trade openness, inflation rate, exchange rate and economic growth rate. Second, both linear and nonlinear Granger causality tests are conducted to analyze the causality of those variables. The results reveal that financial and trade openness have the greatest effects on the degree to which RMB is internationalized. In the short run, the trade openness will promote the development of RMB internationalization, while the financial openness will restrain such an effect. Long‐term conclusions are the opposite. Meanwhile, financial openness can influence RMB internationalization nonlinearly while RMB internationalization cannot influence financial openness either linearly or nonlinearly. In addition, a strong two‐way nonlinear Granger causality exists between RMB internationalization and exchange rate, and RMB internationalization and trade openness, respectively. Moreover, we find more nonlinear Granger relationships compared to linear Granger relationships, which implied that the relationships between RMB internationalization and other essential elements need to be assessed in the context of a complex nonlinear mechanism. The findings have important policy implication for RMB internationalization and provide a reference to jointly promote currency internationalization and openness.

Can renewable energy drive industrial growth in developing economies? Evidence from India

PurposeThis paper aims to focus on the contribution of segregated renewable energy (RE) sources such as solar, wind, bagasse, biomass, small hydropower (SHP) and waste to heat in driving sustainable industrial production in India.Design/methodology/approachThis study uses non-linear modelling techniques such as quantile regression and the non-linear Granger causality test to explore the interplay between segregated RE generation and industrial production in India.FindingsThe study findings support the role of segregated RE sources generation, especially SHP and bagasse, on industrial production in India. This paper finds unidirectional non-linear Granger causality running from segregated RE sources to industrial production. Bidirectional non-linear Granger causality has been established from biomass, waste-heat to index of industrial production and vice versa, supporting an asymmetric feedback hypothesis.Research limitations/implicationsThe study findings will aid the energy policymaker in framing policies for RE sources, especially bagasse-based and SHP generation for the sustainable industrial growth of India.Originality/valueTo the best of the authors’ knowledge, this is one of the first studies to explore the role of segregated RE sources generation to drive sustainable industrial growth in India using non-linear techniques.

Co-movement and Granger causality between Bitcoin and M2, inflation and economic policy uncertainty: Evidence from the U.K. and Japan

This study aims to investigate the co-movement and Granger causality between Bitcoin prices (BTC) and M2 (cash, demand, and time deposits), inflation, and economic policy uncertainty (EPU) in the U.K. and Japan. It uses monthly data from 31 July 2010 to 30 August 2020 and employs the wavelet coherence method, Toda-Yamamoto, and nonlinear Granger-causality tests. The empirical results show that (i) Bitcoin prices influence M2 and interact with inflation and EPU. In the short term, inflation affects Bitcoin price positively, supporting Bitcoin as an inflation hedged instrument in Japan. Both in Japan and the U.K., the short-term effects of M2 on Bitcoin prices are negative, while EPU's effects on Bitcoin prices are positive, (ii) a bidirectional Toda-Yamamoto Granger causality exists between Bitcoin prices, inflation, and EPU and confirms that M2 affects Bitcoin prices, (iii) a nonlinear bidirectional causality exists between Bitcoin prices and inflation. While Bitcoin prices Granger cause M2 in the U.K. and Japan, inflation shows a nonlinear Granger causality with EPU in Japan. These findings help investors make investment decisions while considering the effects of M2, inflation, and EPU, and monetary authorities and policymakers make policies involving Bitcoin.

The differential influence of social media sentiment on cryptocurrency returns and volatility during COVID-19

This research investigates the effects of several measures of Twitter-based sentiment on cryptocurrencies during the COVID-19 pandemic. Innovative economic, as well as market uncertainty measures based on Tweets, along the lines of Baker et al. (2021), are employed in an attempt to measure how investor sentiment influences the returns and volatility of major cryptocurrencies, developing on non-linear Granger causality tests. Evidence suggests that Twitter-derived sentiment mainly influences Litecoin, Ethereum, Cardano and Ethereum Classic when considering mean estimates. Moreover, uncertainty measures non-linearly influence each cryptocurrency examined, at all quantiles except for Cardano at lower quantiles, and both Ripple and Stellar at both lower and higher quantiles. Cryptocurrencies with lower values are found to be unaffected by investor sentiment at extreme values, however, prove to be profitable due to more aligned investor behaviour.

Does geopolitical risk matter in crude oil and stock markets? Evidence from disaggregated data

This paper investigates the nonlinear Granger causality and spillover effects among geopolitical risk, crude oil and Chinese disaggregated sectoral stock markets by using a multivariate nonlinear Granger causality test and connectedness network analysis. The nonlinear Granger causality and spillover effects are shown to be heterogeneous across sectors. The nonlinear Granger causality indicates Brent oil is likely to play a more important role in geopolitical risk and Chinese stock markets, especially for the Energy and Financial sectors. But the connectedness network analysis suggests that geopolitical risk is the net receiver of spillover effects from WTI oil and the net transmitter to Brent oil. Chinese sectoral stock markets are the main provider of spillover effects in crude oil and geopolitical risk, and its spillover effects are stronger for the Consumer Discretionary, Industrials, Materials, Health Care and Information Technology sectors. In addition, crude oil may play an essential function as an intermediary receiver of Chinese sectoral stock market shocks to geopolitical risk.