Linear Granger Causality Tests Research Articles

Does fine wine price contain useful information to forecast GDP? Evidence from major developed countries

This study provides the first attempt to examine the ability of the price of fine wine to forecast the Gross Domestic Product (GDP) for the major developed countries. Considering the limitation of a linear Granger causality test in detecting nonlinear causal relationships, a nonlinear Granger causality test is also employed. The results from our nonlinear causality test show that this new variable contains useful information to forecast GDP for the US, the UK, and Australia, suggesting that we may include it as a forecasting variable in GDP forecasting models, especially nonlinear models, for these three countries.

How strong are the causal relationships between Islamic stock markets and conventional financial systems? Evidence from linear and nonlinear tests

Past studies suggest that the Islamic finance system is only weakly linked or even decoupled from conventional markets. If this statement is true, then this system may provide a cushion against potential losses resulting from probable future financial crises. In this article, we make use of heteroscedasticity-robust linear Granger causality and nonlinear Granger causality tests to examine the links between the Islamic and global conventional stock markets, and between the Islamic stock market and several global economic and financial shocks. Our findings reveal evidence of significant linear and nonlinear causality between the Islamic and conventional stock markets but more strongly from the Islamic stock market to the other markets. They also show potent causality between the Islamic stock market and financial and risk factors. This evidence leads to the rejection of the hypothesis of decoupling of the Islamic market from their conventional counterparts, thereby reduces the portfolio benefits from diversification with Sharia-based markets. A striking result shows a connection between the Islamic stock market and interest rates and interest-bearing securities, which is inconsistent with the Sharia rules. The results also suggest that modeling Islamic stock markets should be done within a nonlinear VAR system and not through a regression equation.

Electricity Consumption and Economic Growth in Turkey: Cointegration, Linear and Nonlinear Granger Causality

The purpose of this study is to examine causal relationships between the electricity consumption and economic growth in Turkey for the period 1967–2007. To accomplish this purpose, we utilize three analytical tools in time series econometrics: The bounds testing cointegration approach, the linear Granger causality test and the nonlinear Granger causality test. We depart from the existing literature on the energy consumption and economic growth nexus in Turkey by testing nonlinearity in the series and carrying out the nonlinear Granger causality test. The cointegration analysis shows that the electricity consumption and economic growth are cointegrated in the long-run. The linear Granger causality test based on the error correction model shows the bi-directional Granger causality in both the short- and the long-run between the electricity consumption and economic growth in Turkey. After filtering out the linear properties of the series with cointegration analysis, we carry out the Brock, Dechert, and Scheinkman nonlinearity test and find evidence of nonlinearity in the series. Therefore, the inferences from the nonlinear Granger causality test are more appropriate than the linear Granger causality analysis for Turkey. The nonlinear causality analysis supports the neutrality hypothesis, implying that electricity conservation policy will not damage the growth in the Turkish economy.

Long- and Short-Run Relationships between House and Stock Prices in South Africa: A Nonparametric Approach

This paper provides empirical evidence on the long- and short-run relationships between real house and stock prices of South Africa. Standard linear tests may not detect the existence of these relationships between time series especially in the presence of structural shifts or regime changes, which, in turn, may cause nonlinearities in the observed series. Thus, in this study, both linear and nonparametric cointegration and Granger causality tests were conducted. Results from the linear cointegration test showed no long-run relationship between house and stock prices. The linear Granger causality test produced no evidence of causality either. In contrast, the nonparametric cointegration test revealed a long-run one-to-one relationship between the two series, with the nonparametric Granger causality test indicating a bi-directional causality. Therefore, stability in the housing market drives stability in the equity market and vice versa.

Granger causal relations among Greater China stock markets: a nonlinear perspective

This article uses linear and nonlinear Granger causality tests to study Granger causal relations among the stock markets of Greater China. In sharp contrast to the results disclosed by its linear counterpart, a nonlinear causality test provides evidence of isolated bi-directional causal relations between two Chinese stock markets and between the HKSE and the TWSE in the earlier period of 1992 to 1999. The nonlinear causality test further shows that, in the later period of 2000 to 2008, Granger causal relations have been strengthened and that Chinese stock markets are well connected with their neighbour markets, the HKSE and the TWSE, and they are playing a most influential role among the stock markets of Greater China.

Trading Volume and Exchange Rate Volatility: Evidence for the Sequential Arrival of Information Hypothesis

The relationship between trading volume and volatility in foreign exchange markets continues to be of much interest, especially given the higher than expected volatility of returns. Allowing for non-linearities, this paper tests competing hypotheses on the possible relationship between volatility and trading volume using data for three major currency futures contracts denominated in US dollars, namely the British pound, the Canadian dollar and the Japanese yen. We find that trading volumes and return volatility are negatively correlated, implying a lack of support for the mixture of distributions hypothesis (MDH). Using linear and nonlinear Granger causality tests, we document significant lead-lag relations between trading volumes and return volatility consistent with the sequential arrival of information (SAI) hypothesis. These findings are robust and not sample dependent or due to heterogeneity of beliefs as proxied by open interest. Furthermore, our results are insensitive to the modeling approach used to recover volatility measures. Overall, our findings support the contention that short to medium term currency relationships may be dominated by trading dynamics and not by fundamentals.

New evidence on the relation between return volatility and trading volume

AbstractIn the empirical literature, it has been shown that there exists both linear and non‐linear bi‐directional causality between trading volumes and return volatility (measured by the square of daily return). We re‐examine this claim by using realized volatility as an estimator of the unobserved volatility, adopting a stationary de‐trended trading volume, and applying a more recent data sample with robustness tests over time. Our linear Granger causality test shows that there is no causal linear relation running from volume to volatility, but there exists an ambiguous causality for the reverse direction. In contrast, we find strong bi‐directional non‐linear Granger causality between these two variables. On the basis of the non‐linear forecasting modeling technique, this study provides strong evidence to support the sequential information hypothesis and demonstrates that it is useful to use lagged values of trading volume to predict return volatility. Copyright © 2009 John Wiley & Sons, Ltd.

New Evidence on the Relation between Return Volatility and Trading Volume

In his seminal paper, Brooks argues that the relation between return volatility and trading volume can be both linear and nonlinear. Adopting both linear and nonlinear Granger causality tests, he shows that there exists both linear and nonlinear bi-directional causality between trading volumes and return volatility (measured by square of daily return). We re-examine his claims by using realized volatility as a more precise estimator of the unobserved volatility, adopting a stationary de-trended trading volume and applying a more recent data sample with robustness tests over time. Our linear Granger causality test shows that there is no causal linear relation running from volume to volatility but there exist an ambiguous causality for the reverse direction. In contrast, we find strong bi-directional nonlinear Granger causality between these two variables. Our results provide strong support for the sequential information theory and offer a promising prospect for developing nonlinear models to predict return volatility using trading volume.

Multivariate Causality Tests with Simulation and Application

The traditional linear Granger causality test has been widely used to examine the linear causality among several time series in bivariate settings as well as multivariate settings. Hiemstra and Jones (1994) develop a nonlinear Granger causality test in a bivariate setting to investigate the nonlinear causality between stock prices and trading volume. In this paper, we first discuss linear causality tests in multivariate settings and thereafter develop a nonlinear causality test in multivariate settings. A Monte Carlo simulation is conducted to demonstrate the superiority of our proposed multivariate test over its bivariate counterpart. In addition, we illustrate the applicability of our proposed test to analyze the relationships among different Chinese stock market indices.

The relationship between CO 2 emissions and economic growth: The case of Korea with nonlinear evidence

Using STAR models, we investigate the nonlinear dynamic properties and the interdependence of CO 2 emissions and economic growth for Korea. The estimation results indicate that the growth rate of both CO 2 emissions and industrial production exhibit a significant nonlinear asymmetric dynamics. While the linear Granger causality test finds no causality in any direction, the results of the nonlinear Granger causality tests show a two-way causality between CO 2 emissions and economic growth. The strong mutual causation between CO 2 emissions and economic activities indicates that the economic impact from CO 2 mitigation is expected to be higher in Korea. This suggests that the appropriate energy and environmental policy be to mitigate CO 2 emissions while having less impact on the economy.

Do Information Transmissions among Stock Markets of Greater China Become Stronger? A Nonlinear Perspective

This paper uses linear and nonlinear Granger causality tests to study information transmission among stock markets of Greater China. In sharp contrast to the results disclosed by its linear counterpart, a nonlinear causality test provides evidence of isolated bi-directional causal relations between two Chinese stock markets and between HKSE and TWSE in the earlier period of 1992-1999. The nonlinear causality test further shows that information transmissions have recently been strengthened and Chinese stock markets are well connected with their neighbor markets HKSE and TWSE. In fact, Chinese stock markets are playing a most influential role among the stock markets of Greater China.

Causal relationship between stock price and exchange rate: evidence for India

The purpose of this study is to investigate the long and short-run relation between stock index and exchange rates for India. The study uses cointegration methodology to test for the long-run relationship. Empirical results suggest that there is no long-run relationship between them. Furthermore, the study examines the causal relationship between two series using linear and non-linear Granger causality tests. The non-linear causality is investigated using noisy Mackey-Glass model. The results of both the causality tests reveal evidence of bi-directional relationship between stock index and exchange rates. The findings imply that regulators can consider developments in these two markets into account to promote stability and economic growth.

Did speculative activities contribute to high crude oil prices during 1993 to 2008?

By applying two nonlinear Granger causality testing methods and rolling window strategy to explore the relationship between speculative activities and crude oil prices, the unidirectional Granger causality from speculative activities to returns of crude oil prices during the high price phase is discovered. It is proved that speculative activities did contribute to high crude oil prices after the Asian financial crisis and OPEC's output cut in 1998. The unidirectional Granger causality from returns of crude oil prices to speculative activities is significant in general. But after 2000, with the sharp rise in crude oil prices, this unidirectional Granger causality became a complex nonlinear relationship, which cannot be detected by any linear Granger causality test.

Testing for causality in the transmission of Eurodollar and US interest rates

This article employs linear Granger causality tests and the nonlinear causality test of Baek and Brock (1992) and Hiemstra and Jones (1994), as recently modified by Diks and Panchenko (2005b), to re-examine the dynamic relation between daily Eurodollar and US certificate of deposit interest rates during the period 4 January 1971 to 15 July 2005. Although we find significant linear causality only from the US certificate of deposit interest rates to the Eurodollar interest rates, we find significant bidirectional nonlinear causality between Eurodollar and US certificate of deposit interest rates.

Tests of Informational Efficiency in Financial Markets: A Granger Causality Approach

In this study we examine a causal relationship between series of returns and traded volumes in high-frequency data. Our analysis is based on the methodology of Ghysels, Gourieroux and Jasiak (2000), who develop a qualitative framework in which dynamics of financial series are restricted to transitions between a finite number of states, represented by Markov chains with specific transition probabilities. Linear and nonlinear Granger causality tests are used to evaluate statistical causality between series of volumes and returns of various investment vehicles within the parametric and nonparametric frameworks, and for trading and calendar time specifications. Results demonstrate evidence of three causality types, unidirectional from volumes to returns and vice versa, and instantaneous causality. The frequency with which causal relationships are observed increases rapidly as the transaction grid becomes finer, allowing us to infer that while informational efficiency of financial markets holds, opportunities for economic gains may exist at high-frequencies.

An empirical study on information spillover effects between the Chinese copper futures market and spot market

This study employs a parametric approach based on TGARCH and GARCH models to estimate the VaR of the copper futures market and spot market in China. Considering the short selling mechanism in the futures market, the paper introduces two new notions: upside VaR and extreme upside risk spillover. And downside VaR and upside VaR are examined by using the above approach. Also, we use Kupiec’s [P.H. Kupiec, Techniques for verifying the accuracy of risk measurement models, Journal of Derivatives 3 (1995) 73–84] backtest to test the power of our approaches. In addition, we investigate information spillover effects between the futures market and the spot market by employing a linear Granger causality test, and Granger causality tests in mean, volatility and risk respectively. Moreover, we also investigate the relationship between the futures market and the spot market by using a test based on a kernel function. Empirical results indicate that there exist significant two-way spillovers between the futures market and the spot market, and the spillovers from the futures market to the spot market are much more striking.

A consistent characteristic function-based test for conditional independence

Abstract Y is conditionally independent of Z given X if Pr { f ( y | X , Z ) = f ( y | X ) } = 1 for all y on its support, where f ( · | · ) denotes the conditional density of Y given ( X , Z ) or X . This paper proposes a nonparametric test of conditional independence based on the notion that two conditional distributions are equal if and only if the corresponding conditional characteristic functions are equal. We extend the test of Su and White (2005. A Hellinger-metric nonparametric test for conditional independence. Discussion Paper, Department of Economics, UCSD) in two directions: (1) our test is less sensitive to the choice of bandwidth sequences; (2) our test has power against deviations on the full support of the density of ( X , Y , Z ). We establish asymptotic normality for our test statistic under weak data dependence conditions. Simulation results suggest that the test is well behaved in finite samples. Applications to stock market data indicate that our test can reveal some interesting nonlinear dependence that a traditional linear Granger causality test fails to detect.

Linear and nonlinear causality between signals: methods, examples and neurophysiological applications

In this paper, we will present and review the most usual methods to detect linear and nonlinear causality between signals: linear Granger causality test (Geweke in J Am Stat Assoc 77:304-313, 1982) extended to direct causality in multivariate case (LGC), directed coherence (DCOH, Saito and Harashima in Recent advances in EEG and EMG data processing, Elsevier, Amsterdam, 1981), partial directed coherence (PDC, Sameshima and Baccala 1999) and nonlinear Granger causality test of Baek and Brock (in Working Paper University of Iowa, 1992) extended to direct causality in multivariate case (partial nonlinear Granger causality, PNGC). All these methods are tested and compared on several ARX, Poisson and nonlinear models, and on neurophysiological data (depth EEG). The results show that LGC, DCOH and PDC are not very robust in relation to nonlinear linkages but they seem to correctly find linear linkages if only the autoregressive parts are nonlinear. PNGC is extremely dependent on the choice of parameters. Moreover, LGC and PNGC may give misleading results in the case of causality on a spectral band, which is illustrated by our neurophysiological database.

A sneeze in the U.S., a cough in Japan, but pneumonia in Taiwan? An application of the Markov-Switching vector autoregressive model

This paper examines the lead–lag relationships among the output of Taiwan, Japan and the U.S. Three testing methods are employed: the traditional linear Granger causality test, Hiemstra and Jones' [Hiemstra, C., Jones, J.D., 1994. Testing for linear and nonlinear Granger causality in the stock price-volume relation. Journal of Finance 49, 1639–1664] nonlinear Granger causality test and Warne's [Warne, A., 2000. Causality and regime inference in a Markov-S switching VAR, Working Paper no. 118, Sveriges Riksbank, Stockholm.] Granger causality test under the Markov-Switching model. We find that the causal ordering is unclear and depends on the model we used. Because Markov-Switching model imposes few restrictions in estimation, we tend to use its estimated results but bear in mind that the evidence is sensitive. First, the common shock hypothesis is found that most probably exists between Taiwan and the U.S. Next, we conclude that Japan tends to lead Taiwan's output, to a certain extent. Last, there is no causal ordering between the U.S. and Japan economies.

An alternative approach in investigating lead–lag relationships between stock and stock index futures markets – comment

This study re-examines and reinterprets the empirical results of Brooks et al. () which investigated the lead–lag relationship between stock indices and stock index futures markets. Contrary to the contention of Brooks et al. that the stock index futures market leads the stock market, it is found that their linear Granger causality tests exhibit overwhelming evidence of a contemporaneous relationship and a bidirectional relationship between spot and futures returns. The interpretation of the empirical evidence of Brooks et al., although different from theirs, is equally supportive of the theoretical predictions of the cost-of-carry model and the efficient market hypothesis.