A Consistent Nonparametric Test for Causality in Quantile

Jeong, Kiho; Härdle, Wolfgang Karl

doi:10.1017/s0266466611000685

Cited by 367 publications

(399 citation statements)

References 35 publications

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“…We present here a novel methodology, as proposed by Balcilar et al, (forthcoming), for the detection on nonlinear causality via a hybrid approach based on the frameworks of Nishiyama et al, (2011) andJeong et al, (2012). We denote dollar-based exchange rate returns as (y t ) and the differential between own-country EPU and the U.S. EPU as (x t ).…”

Section: Methodsmentioning

confidence: 99%

“…We denote dollar-based exchange rate returns as (y t ) and the differential between own-country EPU and the U.S. EPU as (x t ). Following Jeong et al, (2012), the quantile-based causality is defined as follows: 1 does not cause in the -quantile with respect to the lag-vector of…”

Section: Methodsmentioning

confidence: 99%

“…In an extension of the Jeong et al, (2012) framework, we develop a test for the 2nd moment. In particular, we want to test the volatility causality running from the differential of own and foreign-country EPUs to exchange rate returns.…”

Section: Methodsmentioning

confidence: 99%

“…(6) - (9) with . Incorporating the Jeong et al, (2012) approach we overcome the problem that causality in the conditional 1st moment (mean) imply causality in the 2nd moment (variance). In order to overcome this problem, we interpret the causality in higher order moments using the following model:…”

Section: Methodsmentioning

confidence: 99%

“…The choice of dollar-based exchange rates when analysing uncertainty is clear, given the widely-accepted safe-haven notion associated with the U.S. dollar (see Ciner et al, (2013) for a detailed discussion in this regard). The causality-in-quantiles test that we employ in this paper, combines the frameworks of k-th order causality of Nishiyama et al, (2011) and quantile causality of Jeong et al, (2012), and hence, can be considered to be a more general version of the former. The causality-in-quantile approach has the following novelties:…”

Section: As Reported In the Triennial Survey Of Global Foreign Exchanmentioning

confidence: 99%

See 4 more Smart Citations

Does Economic Policy Uncertainty Predict Exchange Rate Returns and Volatility? Evidence from a Nonparametric Causality-in-Quantiles Test

et al. 2016

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Recent studies have analysed the ability of measures of uncertainty to predict movements in macroeconomic and financial variables. The objective of this paper is to employ the recently proposed nonparametric causality-in-quantiles test to analyse the predictability of returns and volatility of sixteen U.S. dollar-based exchange rates (for both developed and developing countries) over the monthly period of 1999:01-2012:03, based on information provided by a news-based measure of relative uncertainty, i.e., the differential between domestic and U.S. uncertainties. The causality-in-quantile approach allows us to test for not only causality-in-mean (1 st moment), but also causality that may exist in the tails of the joint distribution of the variables. In addition, we are also able to investigate causality-in-variance (volatility spillovers) when causality in the conditional-mean may not exist, yet higher order interdependencies might emerge. We motivate our analysis by employing tests for nonlinearity. These tests detect nonlinearity, as well as the existence of structural breaks in the exchange rate returns, and in its relationship with the EPU differential, implying that the Granger causality tests based on a linear framework is likely to suffer from misspecification. The results of our nonparametric causalityin-quantiles test indicate that for seven exchange rates EPU differentials have a causal impact on the variance of exchange rate returns but not on the returns themselves at all parts of the conditional distribution. We also find that EPU differentials have predictive ability for both exchange rate returns as well as the return variance over the entire conditional distribution for four exchange rates.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: As Reported In the Triennial Survey Of Global Foreign Exchanmentioning

confidence: 99%

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Does Economic Policy Uncertainty Predict Exchange Rate Returns and Volatility? Evidence from a Nonparametric Causality-in-Quantiles Test

et al. 2016

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

Meng,

Mo,

Nie

2023

Journal of Futures Markets

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

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Impacts of geopolitical risks and economic policy uncertainty on Chinese tourism‐listed company stock

Jiang

Tian

et al. 2020

Int. J Fin Econ

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This paper investigates how geopolitical risk (GPR) and economic policy uncertainty (EPU) impact the Chinese tourism stock return using the quantileon-quantile method and causality-in-quantiles approach. Compared to the conventional linear regression model and the quantile regression, the quantileon-quantile method can capture more factors of uncertainty and provide more accurate and detailed empirical results. Besides, we consider the upper quantile and lower quantile of stock return as the tourism peak season and offseason respectively. Overall, the empirical results indicate GPR exerts a lasting negative effect on tourism stock return and that the negative effect of GPR at low quantile is more significant than that at high quantile. Besides, the Chinese categorical EPU influences both negatively and positively the stock returns at different distribution levels. And during tourist off-season, the asymmetric phenomena between GPR, EPU and stock return are more obvious at lower quantiles and upper quantiles.

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A Consistent Nonparametric Test for Causality in Quantile

Cited by 367 publications

References 35 publications

Does Economic Policy Uncertainty Predict Exchange Rate Returns and Volatility? Evidence from a Nonparametric Causality-in-Quantiles Test

Does Economic Policy Uncertainty Predict Exchange Rate Returns and Volatility? Evidence from a Nonparametric Causality-in-Quantiles Test

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

Impacts of geopolitical risks and economic policy uncertainty on Chinese tourism‐listed company stock

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