Nonlinear Granger Causality: Guidelines for Multivariate Analysis

Diks, Cees; Wolski, Marcin

doi:10.2139/ssrn.2366321

Cited by 14 publications

(26 citation statements)

References 26 publications

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“…This restraint in the specification of models tends to make researchers choose the explanatory variables to come primarily from the same domain (e.g., country or industry) as the variable to be forecast. This even applies to recent contributions such as Bora et al (2016) and Diks and Wolski (2016). As will be shown in this article, such restraint may be mistaken when the focus is on prediction.…”

Section: Introductionmentioning

confidence: 81%

Generalization of information, Granger causality and forecasting

Rötheli

2017

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Purpose This paper aims to analyze forecasting problems from the perspective of information extraction. Circumstances are studied under which the forecast of an economic variable from one domain (country, industry, market segment) should rely on information regarding the same type of variable from another domain even if the two variables are not causally linked. It is shown that Granger causality linking variables from different domains is the rule and should be exploited for forecasting. Design/methodology/approach This paper applies information economics, in particular the study of rational information extraction, to shed light on the debate on causality and forecasting. Findings It is shown that the rational generalization of information across domains can lead to effects that are hard to square with economic intuition but worth considering for forecasting. Information from one domain is shown to affect that from another domain if there is at least one common factor affecting both domains, which is not (or not yet) observed when a forecast has to be made. The analysis suggests the theoretical possibility that the direction of such effects across domains can be counter-intuitive. In time-series econometrics, such effects will show up in estimated coefficients with the “wrong” sign. Practical implications This study helps forecasters by indicating a wider set of variables relevant for prediction. The analysis offers a theoretical basis for using lagged values from the type of variable to be forecast but from another domain. For example, when forecasting the bond risk spread in one country, introducing in the time-series model the lagged value of the risk spread from another country is suggested. Two empirical examples illustrate this principle for specifying models for prediction. The application to risk spreads and inflation rates illustrates the principles of the approach suggested here which is widely applicable. Originality/value The present study builds on a probability theoretic analysis to inform the specification of time-series forecasting models.

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Section: Introductionmentioning

confidence: 81%

Generalization of information, Granger causality and forecasting

Rötheli

2017

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“…Following (Hall and Minnotte, 2002), the idea behind DS is to slightly perturb the original dataset in order to obtain desirable estimator properties (here it is the reduced bias). Diks and Wolski (2013) show that, besides reducing the estimator bias, DS does not affect other asymptotic properties of the test statistic in a similar Granger causality setting. Therefore, it seems to be a straightforward extension to CoVaR-NGraCo for shorter samples.…”

Section: Numerical Performancementioning

confidence: 87%

Exploring Nonlinearities in Financial Systemic Risk

Wolski

2013

SSRN Journal

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We propose a new methodology of assessing the effects of individual institution's risk on the others and on the system as a whole. We build upon the Conditional Value-at-Risk approach, however, we introduce the explicit Granger causal linkages and we account for possible nonlinearities in the financial time series. Conditional Value-at-Risk-Nonlinear Granger Causality, or CoVaR-NGraCo as we call it, has regular asymptotic properties which makes it particularly appealing for practical applications. We test our approach empirically and assess the contribution of the euro area financial companies to the overall systemic risk. We find that only a few financial institutions pose a serious ex ante threat to the systemic risk, whereas, given that the system is already in trouble, there are more institutions which hamper its recovery. Moreover, we discover non-negligible nonlinear structures in the systemic risk profile of the euro zone.JEL Codes: C1, G01, G20, G32

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“…There are still amounts of appealing aspects in nonlinear Granger causality test. It is worth noting that Diks and Wolski [ 18 ] extend the test in Diks and Panchenko [ 15 ] which highlight a need for substitutions for the relationship tested in the HJ test.…”

Section: Conclusion and Remarksmentioning

confidence: 99%

A new test of multivariate nonlinear causality

et al. 2018

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The multivariate nonlinear Granger causality developed by Bai et al. (2010) (Mathematics and Computers in simulation. 2010; 81: 5-17) plays an important role in detecting the dynamic interrelationships between two groups of variables. Following the idea of Hiemstra-Jones (HJ) test proposed by Hiemstra and Jones (1994) (Journal of Finance. 1994; 49(5): 1639-1664), they attempt to establish a central limit theorem (CLT) of their test statistic by applying the asymptotical property of multivariate U-statistic. However, Bai et al. (2016) (2016; arXiv: 1701.03992) revisit the HJ test and find that the test statistic given by HJ is NOT a function of U-statistics which implies that the CLT neither proposed by Hiemstra and Jones (1994) nor the one extended by Bai et al. (2010) is valid for statistical inference. In this paper, we re-estimate the probabilities and reestablish the CLT of the new test statistic. Numerical simulation shows that our new estimates are consistent and our new test performs decent size and power.

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Nonlinear Granger Causality: Guidelines for Multivariate Analysis

Cited by 14 publications

References 26 publications

Generalization of information, Granger causality and forecasting

Generalization of information, Granger causality and forecasting

Exploring Nonlinearities in Financial Systemic Risk

A new test of multivariate nonlinear causality

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