Narrow Results

Taking the unique advantage of the cryptocurrency market setting, this paper examines the relationships between blockchain participation and returns, trading volume and realized volatility of main cryptocurrencies (i.e., Bitcoin, Ethereum and Litecoin). Dissimilar to previous theoretical studies that model the influencing factors on participation, we employ the number of unique from addresses¹ as the proxy for cryptocurrency investors’ blockchain participation and further explore the impact of such participation. By using vector autoregressive (VAR) model, we find that the blockchain participation has a significant and positive impact on the next day’s trading volume and realized volatility for the main cryptocurrencies. Our results are robust to the Granger causality test and alternative measure for blockchain participation.

Korea is not only a top paprika exporter to Japan, but also Japan is the largest Korean paprika importer. In this situation, investigating who has price leadership contributes to fill a gap in previous literature. This study examines paprika price relationships between wholesale prices in Korea, import prices in Japan and wholesale prices in Japan using monthly data from 2007 to 2017. A threshold vector autoregressive model (TVAR) and Granger causality test are used to test the price leadership between Korean exporters and Japanese importers. Moreover, forecasted prices based on TVAR show that Korean exporter’s paprika prices will be lower than Japanese importer’s price in the future. The results show that Japanese importers provide price leadership to wholesalers in Korea and Japan. Our findings suggest that paprika farmers could benefit from policies designed to address the trade situation.

This study investigates the price relationship between domestic fresh milk, imported sterilized milk and soybean milk in Korea to analyze the possible substitutable relationship among them. We divided the periods into 2012–2017, 2018–2021 and 2012–2021 (total sample) and utilized the Granger causality test and Geweke’s (1982) method to capture the recent increase in milk import trends in Korea. Our results show that the price causal relationship has changed by comparing 2012–2017 and 2018–2021 periods. While we did not find any Granger causal relationship in 2012–2017, our results show that fresh milk prices are directly and indirectly affected by soybean milk and imported sterilized milk prices, respectively. Our results also present bidirectional Granger causation between imported and soybean milk; however, causal dominance exists in the direction of soybean to imported milk prices based on the Geweke (1982) approach. Our findings imply that the consumption substitutability of fresh milk has changed, and the government of Korea should consider this changed substitutability to reconstruct the volume of the milk production quota to support Korean milk farmers.

Hallucinations are elusive phenomena that have been associated with psychotic behavior, but that have a high prevalence in healthy population. Some generative mechanisms of Auditory Hallucinations (AH) have been proposed in the literature, but so far empirical evidence is scarce. The most widely accepted generative mechanism hypothesis nowadays consists in the faulty workings of a network of brain areas including the emotional control, the audio and language processing, and the inhibition and self-attribution of the signals in the auditive cortex. In this paper, we consider two methods to analyze resting state fMRI (rs-fMRI) data, in order to measure effective connections between the brain regions involved in the AH generation process. These measures are the Dynamic Causal Modeling (DCM) cross-covariance function (CCF) coefficients, and the partially directed coherence (PDC) coefficients derived from Granger Causality (GC) analysis. Effective connectivity measures are treated as input classifier features to assess their significance by means of cross-validation classification accuracy results in a wrapper feature selection approach. Experimental results using Support Vector Machine (SVM) classifiers on an rs-fMRI dataset of schizophrenia patients with and without a history of AH confirm that the main regions identified in the AH generative mechanism hypothesis have significant effective connection values, under both DCM and PDC evaluation.

Objective: In patients with Genetic Generalized Epilepsy (GGE), transcranial magnetic stimulation (TMS) can induce epileptiform discharges (EDs) of varying duration. We hypothesized that (a) the ED duration is determined by the dynamic states of critical network nodes (brain areas) at the early post-TMS period, and (b) brain connectivity changes before, during and after the ED, as well as within the ED. Methods: EEG recordings from two GGE patients were analyzed. For hypothesis (a), the characteristics of the brain dynamics at the early ED stage are measured with univariate and multivariate EEG measures and the dependence of the ED duration on these measures is evaluated. For hypothesis (b), effective connectivity measures are combined with network indices so as to quantify the brain network characteristics and identify changes in brain connectivity. Results: A number of measures combined with specific channels computed on the first EEG segment post-TMS correlate with the ED duration. In addition, brain connectivity is altered from pre-ED to ED and post-ED and statistically significant changes were also detected across stages within the ED. Conclusion: ED duration is not purely stochastic, but depends on the dynamics of the post-TMS brain state. The brain network dynamics is significantly altered in the course of EDs.

The aim of the study was to assess causal coupling between neuronal activity, microvascular hemodynamics and blood supply oscillations in the Mayer wave frequency range. An electroencephalogram, cerebral blood oxygenation changes, an electrocardiogram and blood pressure were recorded during rest and during a movement task. Causal coupling between them was evaluated using directed transfer function, a measure based on the Granger causality principle. The multivariate autoregressive model was fitted to all the signals simultaneously, which made it possible to construct a complete scheme of interactions between the considered signals. The obtained pattern of interactions in the resting state estimated in the 0.05–0.15 Hz band revealed a predominant influence of blood pressure oscillations on all the other variables. Reciprocal connections between blood pressure and heart rate variability time series indicated the presence of feedback loops between these signals. During movement, the pattern of connections did not change dramatically. The number of connections decreased, but the couplings between blood pressure and heart rate variability signal were not significantly changed, and the strong influence of the decreased blood hemoglobin concentration on the oxygenated hemoglobin concentration persisted. For the first time our results provided a comprehensive scheme of interactions between electrical and hemodynamic brain signals, heart rate and blood pressure oscillations. Persistent reciprocal connections between blood pressure and heart rate variability time series suggest possible feedforward and feedback coupling of cardiovascular variables which may lead to the observed oscillations in Mayer wave range.

The aim of this study is to quantify acrophobia and provide safety advices for high-altitude workers. Considering that acrophobia is a fuzzy quantity that cannot be accurately evaluated by conventional detection methods, we propose a comprehensive solution to quantify acrophobia. Specifically, this study simulates a virtual reality environment called High-altitude Plank Walking Challenge, which provides a safe and controlled experimental environment for subjects. Besides, a method named Granger Causality Convolutional Neural Network (GCCNN) combining convolutional neural network and Granger causality functional brain network is proposed to analyze the subjects’ noninvasive scalp EEG signals. Here, the GCCNN method is used to distinguish the subjects with severe acrophobia, moderate acrophobia, and no acrophobia in a three-class classification task or no acrophobia and acrophobia in a two-class classification task. Compared with the mainstream methods, the GCCNN method achieves better classification performance, with an accuracy of 98.74% for the two-class classification task (no acrophobia versus acrophobia) and of 98.47% for the three-class classification task (no acrophobia versus moderate acrophobia versus severe acrophobia). Consequently, our proposed GCCNN method can provide more accurate quantitative results than the comparative methods, making it to be more competitive in further practical applications.

While the brain connectivity network can inform the understanding and diagnosis of developmental dyslexia, its cause–effect relationships have not yet enough been examined. Employing electroencephalography signals and band-limited white noise stimulus at 4.8 Hz (prosodic-syllabic frequency), we measure the phase Granger causalities among channels to identify differences between dyslexic learners and controls, thereby proposing a method to calculate directional connectivity. As causal relationships run in both directions, we explore three scenarios, namely channels’ activity as sources, as sinks, and in total. Our proposed method can be used for both classification and exploratory analysis. In all scenarios, we find confirmation of the established right-lateralized Theta sampling network anomaly, in line with the assumption of the temporal sampling framework of oscillatory differences in the Theta and Gamma bands. Further, we show that this anomaly primarily occurs in the causal relationships of channels acting as sinks, where it is significantly more pronounced than when only total activity is observed. In the sink scenario, our classifier obtains 0.84 and 0.88 accuracy and 0.87 and 0.93 AUC for the Theta and Gamma bands, respectively.

The objective of this paper is to test the validity of two views of monetarism in Bangladesh, India, and Pakistan. A Structural Vector Autoregressive (SVAR) model is developed and the objective is accomplished by conducting Granger causality tests and estimating variance decompositions and impulse response functions. The first view of monetarism that changes in the quantity of money cause, lead and are positively related to changes in prices at least in the medium to long time horizon is supported in Bangladesh, India, and Pakistan. The second view of monetarism that changes in the quantity of money cause, lead and are positively related to changes in output at least in the short to medium time horizon is not supported. The implication of such a result is that an expansionary monetary policy only fuels prices with insignificant effects on output. It supports the view of real business cycle theorists who postulate that policy changes only affect prices.

The purpose of this study is to examine the potential linkages among ASEAN-5 currencies, in particular the possibility of a Singapore dollar bloc during the pre- and post-crisis periods by using the Johansen multivariate cointegration test and the Granger causality test. Significant nonstationarity and the presence of unit roots were documented for each currency under both study periods. Using ASEAN-4 exchange rates against the Singapore dollar, the Johansen cointegration test showed that there was no cointegrating relationship during the pre-crisis period. However there were two statistically significant cointegrating vectors among ASEAN exchange rates for the post-crisis period. These findings imply that there is low financial integration before the crisis, but that ASEAN countries are financially more integrated after the crisis. This finding also indicates increasingly role of the Singapore dollar in ASEAN. Therefore, the Singapore dollar may be a possible candidate as the common currency for ASEAN. The analysis is repeated by adding the US dollar to the model. The finding ascertains the influence of the US dollar on ASEAN currencies before the crisis.

This article empirically re-examines the export-led growth hypothesis in the context of Bangladesh using the quarterly data from 1973:1 to 2005:4. The standard time series econometric techniques, such as cointegration and Granger causality tests within the error correction modelling (ECM) are used for this purpose. The results from cointegration analysis suggest that there is stable long-run relationship between exports and income and the results from Granger causality test based on the ECM shows unidirectional causal relationship between exports and income. Thus, these results validate the country's export expansion programs to achieve long-run income growth.

This study examines the relationships between real exchange rate returns and real stock price returns in the stock market of Malaysia. The Kwiatkowski, Phillips, Schmidt and Shin (KPSS) and Dickey and Fuller (DF) unit root test statistics show that all the variables examined are found to be stationary in the first differences. The constant conditional correlation (CCC)-multivariate generalized autoregressive conditional heteroskedasticity (MGARCH) model shows that real exchange rate return of Malaysian ringgit against the United States dollar (RM/USD) and real stock price return of Kuala Lumpur Composite Index (KLCI) are found to be negative and significantly correlated. However, there is insignificant correlation between real exchange rate return of Malaysian ringgit against Japanese Yen (RM/¥) and real stock price return of KLCI. Moreover, the CCC-MGARCH models show that real exchange rate returns and real stock price returns of some stocks are found to be significantly correlated. The KPSS unit root test statistics show that the time invariant conditional variances of real exchange rate returns and real stock price returns are mostly found to be stationary in the levels. There is no evidence of Granger causality between the time invariant conditional variances of real exchange rate returns and real stock price return of KLCI but some evidence of Granger causality between the time invariant conditional variances of real exchange rate returns and real stock price returns. There is a link between the exchange rate market and the stock market in Malaysia but not every real stock price return is significantly linked with real exchange rate return.

This paper examines the dynamic relationship between the consumer price index (CPI) and the producer price index (PPI) in the UK, France and Germany from 1997 to 2013. We employ the momentum-threshold autoregressive (MTAR) cointegration model for empirical analysis. The results show that the CPI and the PPI are cointegrated with bi-directional long-run Granger causality between CPI and PPI, signifying the existence of both demand-pull and the cost-push nature of inflation. The estimates of threshold vector error correction models (TVECMs) indicate asymmetric adjustments to equilibrium, where upward adjustments are statistically significant but downward adjustments are sluggish and insignificant. Moreover, we generate the unconditional half-life estimates as a measure of persistence, which reveal robust evidence of complex non-linearities in the adjustment process. Our overall results provide valuable information for policymakers to formulate inflation-control policies and optimal policy horizons under a non-linear framework.

An extension of transfer entropy, called partial transfer entropy (PTE), is proposed to detect causal effects among observed interacting systems, and particularly to distinguish direct from indirect causal effects. PTE is compared to a linear direct causality measure, the Partial Directed Coherence (PDC), on known linear stochastic systems and nonlinear deterministic systems. PTE performs equally well as PDC on the linear systems and better than PDC on the nonlinear systems, both being dependent on the selection of the measure specific parameters. PTE and PDC are applied to electroencephalograms of epileptic patients during the preictal, ictal and postictal states, and PTE turns out to detect better changes of the strength of the direct causality at specific pairs of electrodes and for the different states.

Transfer entropy (TE) captures the directed relationships between two variables. Partial transfer entropy (PTE) accounts for the presence of all confounding variables of a multivariate system and infers only about direct causality. However, the computation of partial transfer entropy involves high dimensional distributions and thus may not be robust in case of many variables. In this work, different variants of the partial transfer entropy are introduced, by building a reduced number of confounding variables based on different scenarios in terms of their interrelationships with the driving or response variable. Connectivity-based PTE variants utilizing the random forests (RF) methodology are evaluated on synthetic time series. The empirical findings indicate the superiority of the suggested variants over transfer entropy and partial transfer entropy, especially in the case of high dimensional systems. The above findings are further highlighted when applying the causality measures on financial time series.

In this paper, we test for causal relationship between China's stock markets by using returns and a measure of volatility for the Shanghai Composite index, the Shenzhen Composite Subindex, and the Hong Kong Hang Seng Index. We also show that the stock index series are nonstationary and that cointegrating vectors and error correction models do not exist for the series.

Based on these tests, for the return series, we conclude that Shenzhen Granger caused Shanghai before 1994. For the volatility data, we find that there exists a positive feedback relationship between Shanghai and Shenzhen stock markets, and that Hong Kong volatility Granger causes Shanghai volatility, but not vice versa.

It is generally argued that with lifting of barriers to the flow of capital across countries by respective governments, the capital markets have come closer and are now more integrated. This paper examines the existence (or absence) of integration among stock indices of 11 developed and emerging stock markets from three continents: Asia, Europe and America. Using synchronous weekly closing index values from November, 1990 through December, 2001, the study found that all the 11 stock markets are cointegrated. The cointegration analysis was carried out using an error correction vector autoregression (VECM) model. The study goes further to test whether there are any causal relationships among the indices and has used a hitherto empirically untested methodology to explore the causal relationships. Results show that capital market indices from European countries and the USA are not Granger caused by any index. On the other hand, causality effects are much pronounced in Asian capital markets. The capital market in Hong Kong "leads" the other markets in Asia. This learning would help fund managers in managing their exposure in Asian capital markets. The regulators may use the causality results to identify the markets driving movements in a country's capital market and take corrective measures.

Vector error-correction models (VECM) are increasingly being used to capture dynamic relationships between financial variables. Estimation and interpretation of such models can be enhanced if zero restrictions are allowed in the coefficient matrices. Conventional use of full-order models may weaken the power of statistical inferences due to over-parameterization. The paper demonstrates the usefulness of this approach for the analysis of exchange rate relationships. Specifically, the paper examines the relationship between the money supply and the Euro and provides a test of purchasing power parity (PPP) in Japan. The latter test results shed light on the adjustment mechanisms through which PPP is achieved. In addition, it is clear that the proposed ZNZ patterned VECM modeling provides better insights from this kind of financial time-series analysis. The paper also shows that causality detection in an I(d) system can be revealed identically from the ZNZ patterned VECMs or the equivalent VAR models.

This study is an attempt to examine whether the deviations of purchasing power parity and uncover interest rate parity Granger-cause the 1997 Asian financial crisis by using vector autoregression and Granger causality tests. The results show that the purchasing power parity and uncover interest rate parity do not hold for most Asian markets. We find weak evidence to support that the deviations of purchasing power parity and uncover interest rate parity have the power to explicate the origin of the financial crisis.

Weekend effects have been well known in many financial markets. Australia however displays its effect on Tuesdays rather than on Mondays. In this study, we investigate on the possible linkage between the US Monday and Australian Tuesday returns. We document that the Tuesday effect in Australia is one-way Granger caused by the weekend effect in the US conditional on the weekend effects in the UK and Japanese markets. Furthermore, in the post-1987 period where the US Monday returns are positively significant, the Australian Tuesday return also turns out to be positive. This latter finding provides further evidence that the anomaly in Australia is induced by the weekend effect in the US.