A consistent nonparametric test for nonlinear causality—Specification in time series regression

Nishiyama, Yoshihiko; Hitomi, Kohtaro; Kawasaki, Yoshinori; Jeong, Kiho

doi:10.1016/j.jeconom.2011.05.010

Cited by 260 publications

(320 citation statements)

References 24 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%

“…However, it can be shown that it is impossible to combine the different statistics for each 1,2, … , into one statistic for the joint null in Eq. (14) because the statistics are mutually correlated (Nishiyama et al, 2011).…”

Section: Methodsmentioning

confidence: 99%

“…To efficiently address this issue, we include a sequential-testing method as described Nishiyama et al (2011) with some modifications. Firstly we test for the nonparametric Granger causality in the 1st moment ( 1).…”

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%

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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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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: 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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“…Against this backdrop, the objective of our paper is to compare the predictive ability of cay and cay MS not only for real stock returns, but also their volatility, using the nonparametric causality test of Nishiyama et al (2011). This test is developed to study higher order causality and is inherently based on a nonlinear dependence structure between the variables.…”

Section: Introductionmentioning

confidence: 99%

Predicting stock returns and volatility using consumption-aggregate wealth ratios: A nonlinear approach

Bekiros

Gupta

2015

Economics Letters

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Recent empirical evidence based on a linear framework tends to suggest that a Markovswitching version of the consumption-aggregate wealth ratio (cay MS ), developed to account for structural breaks, is a better predictor of stock returns than the conventional measure (cay) -a finding we confirm as well. Using quarterly data over 1952:Q1-2013:Q3, we however provide statistical evidence that the relationship between stock returns and cay or cay MS is in fact nonlinear. Then, given this evidence of nonlinearity, using a nonparametric Granger causality test, we show that it is in fact cay and not cay MS which is a stronger predictor of not only stock returns, but also volatility.JEL Codes: C32, C58, G10, G17

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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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A consistent nonparametric test for nonlinear causality—Specification in time series regression

Cited by 260 publications

References 24 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

Predicting stock returns and volatility using consumption-aggregate wealth ratios: A nonlinear approach

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

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