Nonparametric Granger Causality Research Articles

COVID-19 pandemic-related news and Chinese commodities futures: Time-frequency connectedness and causality-in-quantiles approaches

The COVID-19 pandemic has had a significant impact on the global economy and commodity markets, making it essential to study its effects. We aim to learn the dynamic spillovers between COVID-19 pandemic-related news and Chinese commodities futures (Energy, Petrochemical, Non-ferrous metals, Oil & Fats, and Softs) using time-frequency connectedness and causality-in-quantiles approaches. To capture the diverse impacts of different types of COVID-19 news, we utilize a range of COVID-19 news-related indices based on media news data from January 2, 2020, to October 28, 2022. Based on static connectedness data, it is evident that Petrochemical and Energy futures commodities are the primary contributors and recipients in our research samples. The short- and long-term data sets consistently demonstrate that COVID-19 uncertainty is the main recipient, and they have heterogeneous effects on the commodities. Interestingly, the Coronavirus Panic Index and Coronavirus Media Hype Index show the greatest influence on commodities. Dynamic data reveals that the level of connectedness among the markets fluctuates significantly over time, and short-term connectedness is higher than that in the long term. The non-parametric Granger causality test shows that COVID-19 uncertainty can significantly impact commodities futures in China. Specifically, the results remain robust even when utilizing various uncertainty measures and distinct quantiles.

MODULATION OF CARBON EMISSIONS AND CLEAN ENERGY SOURCES: A NONPARAMETRIC GRANGER CAUSALITY-BASED EVIDENCE FROM ASEAN ECONOMIES

This study uses a dataset from 1990 to 2020 to investigate the impact of renewable energy sources in preventing further environmental degradation in the Association of Southeast Asian Nations (ASEAN) economies. This research aims to investigate using renewable energy sources to lower CO2 emissions in the area. In this investigation, a unique method known as nonparametric Granger causality was employed by using the quantiles approach. The findings showed a strong causal influence of green energy on the returns of CO2 emission at high quantiles of conditional distribution in six ASEAN countries. In addition, the results demonstrated a substantial causal connection between the use of green energy and the erratic levels of CO2 emission across the board, except in Indonesia. Because of this, this study suggested that the administration and officials introduce private and public investment toward green energy to lessen the environmental collapse occurring in ASEAN countries.

Vine copula Granger causality in quantiles

ABSTRACT Causal relationships between time series are typically examined by testing for Granger causality. Although many studies have tested Granger causality in mean, non-causality in mean does not need to carry over to other distribution characteristics or different parts of the distribution. This scenario has motivated many researchers to investigate causal relations from the perspective of conditional quantiles. Several methods have been proposed for both parametric and nonparametric modelling frameworks. Parametric methods have limitations in detecting nonlinear causality, whereas nonparametric methods have difficulty selecting smoothing parameters that significantly affect detection performance. To overcome the difficulties of both parametric and nonparametric Granger causality tests in quantiles, we propose a vine copula Granger causality test in quantiles using the semiparametric time-series modelling technique. The proposed test overcomes shortcomings in parametric modelling and has a computational advantage over nonparametric tests. Our test shows good performance in terms of size and power when using various simulated data. Finally, we illustrate our test using recent cryptocurrency data.

How Do Financial Development and Renewable Energy Affect Consumption-Based Carbon Emissions?

This paper bridges the gap in the literature by employing the novel quantile-on-quantile (QQ) approach, the quantile regression approach, and the nonparametric Granger causality test in quantiles to assess the effect of international trade on consumption-based carbon emissions (CCO2e) in Uruguay. Our study incorporates other drivers of CCO2 emissions, such as financial development and renewable energy, into the model. We find that, in the majority of the quantiles, exports, financial development, and renewable energy exert a negative impact on CCO2e, and the influence of imports on CCO2e is positive in all quantiles. Moreover, the quantile regression approach is used as a robustness test for the quantile-on-quantile approach. The causal interaction from the regressors to CCO2e is evaluated using the nonparametric Granger causality test in quantiles. The outcome of the nonparametric Granger causality test in quantiles suggests that imports, exports, renewable energy, and financial development can predict CCO2e at different quantiles. Based on these outcomes, we recommend that the financial sector must strengthen its focus on giving funding to enterprises that embrace environmentally friendly technologies and incentivize them to employ other energy-efficient technologies for manufacturing reasons, thereby preventing environmental deterioration.

Precious metals as safe-haven for clean energy stock investment: Evidence from nonparametric Granger causality in distribution test

The study aims to examine the connectedness between clean energy stocks and precious metals prices under the different market episodes. We employ the Granger causality-in-the distribution test proposed by Candelon and Tokpavi (2016) to investigate the presence of a causality relationship between the variables for the whole distribution because the test has superior power even if the sample size is small. WilderHill Clean Energy Index is considered a benchmark for the clean energy stock market and gold, silver, platinum, and palladium prices are used for the precious metals. By using daily data from January 1, 2001, to December 12, 2021, we find that there is a unidirectional causal link running from the clean energy stock returns to the precious metal prices in the center and the left tail of the distribution. On the other hand, there is strong feedback between the variables in the right tail of the distribution. These results show that clean energy stock prices have an edge in affecting precious metal prices and precious metals cannot be used to hedge the downside risk of clean energy stock investments.

Grasp-squeeze adaptation to changes in object compliance leads to dynamic beta-band communication between primary somatosensory and motor cortices

In asking the question of how the brain adapts to changes in the softness of manipulated objects, we studied dynamic communication between the primary sensory and motor cortical areas when nonhuman primates grasp and squeeze an elastically deformable manipulandum to attain an instructed force level. We focused on local field potentials recorded from S1 and M1 via intracortical microelectrode arrays. We computed nonparametric spectral Granger Causality to assess directed cortico-cortical interactions between these two areas. We demonstrate that the time-causal relationship between M1 and S1 is bidirectional in the beta-band (15–30 Hz) and that this interareal communication develops dynamically as the subjects adjust the force of hand squeeze to reach the target level. In particular, the directed interaction is strongest when subjects are focused on maintaining the instructed force of hand squeeze in a steady state for several seconds. When the manipulandum’s compliance is abruptly changed, beta-band interareal communication is interrupted for a short period (~ 1 s) and then is re-established once the subject has reached a new steady state. These results suggest that transient beta oscillations can provide a communication subspace for dynamic cortico-cortical S1–M1 interactions during maintenance of steady sensorimotor states.

Vine copula Granger causality in mean

Ever since the Granger causality test was proposed in 1969, financial market researchers have used it heavily to determine whether the past of a one-time series facilitates the future prediction of another time series. However, as many researchers have noted, the traditional Granger causality test based on the vector autoregression model has limitations in detecting nonlinear causality. To relax the parametric model assumptions of the Granger causality test, nonparametric versions have been proposed to use the advantage of detecting nonlinear Granger causality but have shown difficulty in selecting smoothing parameters that significantly affect detection performance. To overcome the difficulties of both parametric and nonparametric Granger causality tests, we propose the vine copula Granger causality test in mean based on the semiparametric time-series modeling technique. The proposed test overcomes the shortcomings of parametric modeling and has a computational advantage over the nonparametric tests. Our test shows good size and power performance with various simulated data and a real data.

Drivers of environmental degradation in Turkey: Designing an SDG framework through advanced quantile approaches

Turkey is a laggard in terms of the achievement of its Sustainable Development Goals (SDGs), and one of the primary issues it faces is environmental deterioration. Therefore, a policy-level reorientation may be needed to address this relevant issue. From this standpoint, this research assesses the impact of renewable energy (RE) use and financial development on the emissions of CO2 as well as the role of urbanization and agriculture, utilizing a dataset stretching between 1985 and 2019. By applying the innovative quantile-on-quantile regression (QQR) and non-parametric Granger causality in quantiles techniques, the study assesses the ways in which the quantiles of the independent variables affect the quantiles of CO2 emissions. The outcomes from the QQR show that in all quantiles, financial development, economic growth, urbanization, and agriculture impact CO2 emissions positively, while in the middle quantiles, the influence of renewable energy use on CO2 is negative. Furthermore, the outcomes from the non-parametric Granger causality test disclosed that in mean and variance, all the variables could predict CO2 emissions at different quantiles. A complete SDG-oriented policy framework has been proposed based on the research’s findings so that Turkey may move toward reaching its SDG 13 and SDG 7 targets.

Lack of redundancy between electrophysiological measures of long-range neuronal communication

BackgroundCommunication between brain areas has been implicated in a wide range of cognitive and emotive functions and is impaired in numerous mental disorders. In rodent models, various metrics have been used to quantify inter-regional neuronal communication. However, in individual studies, typically, only very few measures of coupling are reported and, hence, redundancy across such indicators is implicitly assumed.ResultsIn order to test this assumption, we here comparatively assessed a broad range of directional and non-directional metrics like coherence, Weighted Phase Lag Index (wPLI), phase-locking value (PLV), pairwise phase consistency (PPC), parametric and non-parametric Granger causality (GC), partial directed coherence (PDC), directed transfer function (DTF), spike-phase coupling (SPC), cross-regional phase-amplitude coupling, amplitude cross-correlations and others. We applied these analyses to simultaneous field recordings from the prefrontal cortex and the ventral and dorsal hippocampus in the schizophrenia-related Gria1-knockout mouse model which displays a robust novelty-induced hyperconnectivity phenotype. Using the detectability of coupling deficits in Gria1−/− mice and bivariate correlations within animals as criteria, we found that across such measures, there is a considerable lack of functional redundancy. Except for three pairwise correlations—PLV with PPC, PDC with DTF and parametric with non-parametric Granger causality—almost none of the analysed metrics consistently co-varied with any of the other measures across the three connections and two genotypes analysed. Notable exceptions to this were the correlation of coherence with PPC and PLV that was found in most cases, and partial correspondence between these three measures and Granger causality. Perhaps most surprisingly, partial directed coherence and Granger causality—sometimes regarded as equivalent measures of directed influence—diverged profoundly. Also, amplitude cross-correlation, spike-phase coupling and theta-gamma phase-amplitude coupling each yielded distinct results compared to all other metrics.ConclusionsOur analysis highlights the difficulty of quantifying real correlates of inter-regional information transfer, underscores the need to assess multiple coupling measures and provides some guidelines which metrics to choose for a comprehensive, yet non-redundant characterization of functional connectivity.

Incidence Network, Risk Spillover and Systemic Risk of Financial Institutions

Systemic risk is closely related to the risk contagion of financial institutions. The risk of a single financial institution will spread among financial systems through the complex incidence network among financial institutions, and lead to the outbreak of crisis. However, the risk contagion paths of different financial institutions are different, and the degree of impact is significantly different. The research of using the reduced-form approach mostly focuses on measuring the risk level of a single institution in the system, but there is relatively little literature on the measurement of risk spillover level among financial institutions and different types of financial sectors. The existing research for complex network model analysis is still focused on the construction of multiple symmetric correlation matrix, and the analysis of financial institutions through the construction of asymmetric correlation matrix is still a small number. Based on the classification of commercial banks, securities companies, insurance institutions and other types of financial institutions, this paper measures the impact of specific financial institutions on systemic risk from the perspective of risk spillover degree of financial institutions. Then, according to the nonparametric Granger causality test, the incidence matrix of financial institutions is constructed, and the financial institutions are used as nodes to depict and describe the incidence network indicators and network center properties of financial institutions. At the same time, the risk area is divided by combining the risk spillover index and the input/output value of financial institutions, so as to clearly identify the vulnerability of risk tolerance and the infectivity of risk transmission of financial institutions, in order to judge the risk prevention core and the key of targeted measures. The empirical results show that banking financial institutions are the core of China’s financial incidence network, and they have high stability. Securities financial institutions are at a high level in the level of systemic risk spillover and the degree of financial network connection, and they are the key sectors for China’s systemic risk prevention. The vulnerability and infectivity of different financial institutions are different, and the vulnerability and infectivity of the same financial institution are also different. The increase of capital scale does not mean the increase of risk contagion and vulnerability. Therefore, the risk identification of financial institutions should be comprehensive, not limited to a single standard. On the basis of focusing on the scale of financial institutions, combined with the direction, path and degree of risk impact of specific financial institutions, the risk prevention core is determined. At the same time, according to the different characteristics of risk vulnerability and risk contagion of financial institutions, we should implement targeted regulatory measures.

New insights into an old issue: exploring the nexus between economic growth and CO2 emissions in China.

It is globally acceptable that carbon dioxide (CO2) emissions are one of the greenhouse gases are considered the main factor influencing global warming and environmental degradation. The present study focuses on China, the world's largest carbon emitter. The study aims to capture the time-frequency dependency of economic growth and CO2 emissions in China for the time period 1950-2016 using a wavelet coherence approach, which allows us to investigate both the long-run and short-run causal links of the estimated variables. In order to capture the long-run and causal linkage between economic growth and CO2 emissions, the study employs Maki cointegration, wavelet coherence, Toda-Yamamoto causality, Fourier Toda-Yamamoto causality, and nonparametric Granger causality tests. The findings of this study reveal that (i) there is a significant vulnerability between economic growth and CO2 emissions throughout the 2000s both the short-term and medium-term; (ii) there is long-run cointegration linkage between economic growth and CO2 emissions in China; (iii) economic growth in China has an important power for predicting CO2 emissions over the selected study period, especially in the short-term and medium-term; and (iv) it was observed that there is positive correlation between economic growth during the 1980s and 1990s in the short-term only. The outcome of the Toda-Yamamoto causality, Fourier Toda-Yamamoto causality, and nonparametric Granger causality tests underlines that economic growth is a robust policy variable for predicting CO2 emissions in China.

Measuring directed functional connectivity using non-parametric directionality analysis: Validation and comparison with non-parametric Granger Causality

Background‘Non-parametric directionality’ (NPD) is a novel method for estimation of directed functional connectivity (dFC) in neural data. The method has previously been verified in its ability to recover causal interactions in simulated spiking networks in Halliday et al. (2015). MethodsThis work presents a validation of NPD in continuous neural recordings (e.g. local field potentials). Specifically, we use autoregressive models to simulate time delayed correlations between neural signals. We then test for the accurate recovery of networks in the face of several confounds typically encountered in empirical data. We examine the effects of NPD under varying: a) signal-to-noise ratios, b) asymmetries in signal strength, c) instantaneous mixing, d) common drive, e) data length, and f) parallel/convergent signal routing. We also apply NPD to data from a patient who underwent simultaneous magnetoencephalography and deep brain recording. ResultsWe demonstrate that NPD can accurately recover directed functional connectivity from simulations with known patterns of connectivity. The performance of the NPD measure is compared with non-parametric estimators of Granger causality (NPG), a well-established methodology for model-free estimation of dFC. A series of simulations investigating synthetically imposed confounds demonstrate that NPD provides estimates of connectivity that are equivalent to NPG, albeit with an increased sensitivity to data length. However, we provide evidence that: i) NPD is less sensitive than NPG to degradation by noise; ii) NPD is more robust to the generation of false positive identification of connectivity resulting from SNR asymmetries; iii) NPD is more robust to corruption via moderate amounts of instantaneous signal mixing. ConclusionsThe results in this paper highlight that to be practically applied to neural data, connectivity metrics should not only be accurate in their recovery of causal networks but also resistant to the confounding effects often encountered in experimental recordings of multimodal data. Taken together, these findings position NPD at the state-of-the-art with respect to the estimation of directed functional connectivity in neuroimaging.

Symbolic transfer entropy test for causality in longitudinal data

In this study, we use multiple-unit symbolic dynamics and transfer entropy to develop a non-parametric Granger causality test procedure for longitudinal data. Monte Carlo simulations show that our test exhibits the correct size and a high power in situations where linear panel data causality tests fail, such as (1) when the linearity assumption does not hold, (2) when the data generating process is heterogeneous across the cross-section units or presents structural breaks, (3) when there are extreme observations in some of the cross-section units, (4) when the process exhibits causal dependence on the conditional variance, or (5) when the analysis involves qualitative data. We illustrate the usefulness of our proposed procedure by analyzing the dynamic causal relationships between public expenditure and GDP, between firm productivity and firm size in US manufacturing sectors, and among sovereign credit ratings, growth, and interest rates.

The Causality between Financial Instability and Monetary Policy Uncertainty: Who Predicts Whom?

By employing the bootstrap full-sample Granger causality test and sub-sample rolling window causality test, this paper attempts to disentangle the causal nexus between financial instability and monetary policy uncertainty in the US, Japan, and Greece. The bootstrap full sample causality test reveals that there is unidirectional causality from monetary policy uncertainty to financial instability in the US, while there exists the reverse channel in Japan and Greece. Nonparametric Granger causality test further demonstrates that there is no causal relationship in each country, indicating the potential time-varying relationship between two variables. To allow the dynamic relationship between them, we use the bootstrap sub-sample rolling window Granger causality test and conclude that there are bidirectional causal relationships between financial instability and monetary policy uncertainty in specific subperiods for all three countries, such as during regional and global crisis. Overall, this paper helps us better understand the intricate mechanisms between financial instability and monetary policy uncertainty from the predictive perspective.

The short- and long-run efficiency of energy, precious metals, and base metals markets: Evidence from the exponential smooth transition autoregressive models

The aim of this paper is to investigate the long and short-run relationship between spot and futures prices of the energy, precious metals, and base metals markets. We analyze daily data from January 1985 to February 2019. The empirical findings based on the cointegration test, which follows a nonlinear process, suggest that the spot prices of energy and metals assets have long-run relationships with their futures prices. Nonparametric Granger causality test results also indicate bi-directional causality among futures and spot prices. These findings indicate that the energy and metals markets are informationally efficient in the sense of Fama (1970).

The linear and nonlinear lead–lag relationship among three SSE 50 Index markets: The index futures, 50ETF spot and options markets

The lead–lag relationship in finance markets has always been the focus point of many researches. The linear Granger causality, a classic method for the research of lead–lag relationship, has been extensively analyzed for markets of spot, future and option, but the nonlinear causality between spot and option markets has attracted little attention. This article first analyzes the linear and nonlinear Granger Causality among Shanghai Stock Exchange 50 (SSE 50) index futures, SSE 50ETF spot and options markets, and compares the effects of different time frequencies on test results. From the results of Granger causality test, we demonstrate some significant lead–lag relationships via 1-hour data, whereas the 5-minutes data may be detrimental to nonparametric Granger causality test. Furthermore, from the 1-minute data, we find that within one day, some different short-term lead–lag relationships appear, and they often contradict to the directions obtained from entire sample spanning 1 year. In search of the more detailed volatile causalities, we employ the nonparametric thermal optimal path (TOP) method on 5-minutes data, finding it potentially an expansion to the traditional Granger method, due to its ability to process high frequency data.

Is a more financially open world riskier?

The paper studies co-movement and causality between global financial openness and nearly all known types of financial crises during 1970–2014. It builds on a composite measure of global financial openness, which synthesizes three well-established indicators (de-jure, de-facto capital account openness and the KOF economic globalization index). I use linear and nonparametric Granger (no) causality tests as well as wavelet-based techniques to characterize the dynamic dependence, which appears rather weak. Only in case of systemic and triple crises, this dependence is found more robust. I offer two explanations for the elusive openness-crisis nexus. First, the impact of global financial openness on the frequency of crises can be indirect, transmitted through global macro-fundamentals (world real GDP growth rates, global inflation and commodity prices). Second, capital account liberalization can be self-reinforcing. Despite an increased risk of crises, countries, which have partly implemented such reforms, are likely to proceed. In doing so, they are often guided by their peers in a regional integration agreement. Overall, the findings of the paper question the widespread assertion that increasing global financial openness undermines financial stability.

Adaptive efficiency of the Mexican Stock Exchange

PropósitoEste documento analiza la eficiencia adaptativa del mercado para la relación precio-volumen de las empresas que cotizan en la Bolsa Mexicana de Valores. El periodo bajo estudio es de 1982 a 2015. Para detectar causalidad y determinar la eficiencia adaptativa del mercado, se aplicó una prueba lineal y dos no-lineales. Existen pocos documentos en la literatura que estudien la relación precio-volumen en mercados latinoamericanos. Como tal, este documento puede ser de interés e importancia tanto para académicos como para profesionales de las finanzas.MetodologíaSe aplicó la prueba de causalidad no-paramétrica de Diks y Panchenko y la prueba de bicorrelación cruzada de Brooks y Hinich.HallazgosDerivado de la prueba DP, los hallazgos muestran que existe causalidad no-lineal bidireccional en 25.71% de las empresas bajo estudio, comparado a un 8% cuando se aplica la prueba de causalidad lineal de Granger. Por lo tanto, existe evidencia de eficiencia en forma débil del mercado. De la pruba BH, se muestra evidencia de eficiencia adaptativa del mercado, dado que el 71.42% de las empresas exhibieron alguna forma de dependencia no-lineal en ciertos periodos de tiempo. Con estos resultados, el proceso de información debe ser mejor estudiado para una mayor comprensión de las políticas regulatorias del mercado y mejores herramientas para la toma de decisiones por los inversionistas.OriginalidadEste documento complementa los estudios sobre la relación precio-volumen y la eficiencia en un mercado latinoamericano.

Reciprocal Interactions between Medial Septum and Hippocampus in Theta Generation: Granger Causality Decomposition of Mixed Spike-Field Recordings.

The medial septum (MS) plays an essential role in rhythmogenesis in the hippocampus (HIPP); theta-rhythmic bursts of MS neurons are believed to drive theta oscillations in rats’ HIPP. The MS theta pacemaker hypothesis has solid foundation but the MS-hippocampal interactions during different behavioral states are poorly understood. The MS and the HIPP have reciprocal connections and it is not clear in particular what role, if any, the strong HIPP to MS projection plays in theta generation. To study the functional interactions between MS and HIPP during different behavioral states, this study investigated the relationship between MS single-unit activity and HIPP field potential oscillations during theta states of active waking and REM sleep and non-theta states of slow wave sleep (SWS) and quiet waking (QW), i.e., sleep-wake states that comprise the full behavioral repertoire of undisturbed, freely moving rats. We used non-parametric Granger causality (GC) to decompose the MS-HIPP synchrony into its directional components, MS→HIPP and HIPP→MS, and to examine the causal interactions between them within the theta frequency band. We found a significant unidirectional MS→HIPP influence in non-theta states which switches to bidirectional theta drive during theta states with MS→HIPP and HIPP→MS GC being of equal magnitude. In non-theta states, unidirectional MS→HIPP influence was accompanied by significant MS-HIPP coherence, but no signs of theta oscillations in the HIPP. In theta states of active waking and REM sleep, sharp theta coherence and strong theta power in both structures was associated with a rise in HIPP→MS to the level of the MS→HIPP drive. Thus, striking differences between waking and REM sleep theta states and non-theta states of SWS and QW were primarily observed in activation of theta influence carried by the descending HIPP→MS pathway associated with more regular rhythmic bursts in the MS and sharper MS→HIPP GC spectra without a significant increase in MS→HIPP GC magnitude. The results of this study suggest an essential role of descending HIPP to MS projections in theta generation.

Systemic risk in Europe: deciphering leading measures, common patterns and real effects

The paper studies salient features of systemic risk in a sample of 22 European (EU and non-EU) countries during January 2010–March 2016. Building on a novel dataset and conducting an empirical horse race, we determine pivotal systemic risk measures for the sample countries. SRISK and volatility indicator tend to lead other metrics, followed by leverage. In contrast to the conventional wisdom, composite systemic risk measures aggregated with the aid of principal and independent component analysis perform worse. The leading systemic risk measures exhibit a high degree of connectedness. The VIX index, TED spread, the Composite Index of Systemic Stress (CISS) and long-term interest rates underlie their dynamics. Two clusters within the sample are identified, with CISS and long-term interest rates being crucial to distinguish between them. There is only scarce evidence for causal linkages between systemic risk and industrial production in the sample countries, based on the concurring results of standard and nonparametric Granger causality tests.