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Review of Quantitative Finance and Accounting

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14-09-2017 | Original Research

Symmetric and asymmetric nonlinear causalities between oil prices and the U.S. economic sectors

Authors: Jinghua Wang, Geoffrey Ngene

Published in: Review of Quantitative Finance and Accounting | Issue 1/2018

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Abstract

This study investigates the causal dynamics of the U.S. sector price changes and oil price changes using the symmetric nonlinear and asymmetric nonlinear causality tests. We find a unidirectional causality from each sector to the oil market using the Granger and MWald linear causality tests. However, the symmetric nonlinear and asymmetric nonlinear causality for negative price changes tests yield unidirectional causality from the oil to the sector price changes which sharply contrast the evidence using the linear models. We find bidirectional causality using the asymmetric nonlinear test for positive price changes, suggesting temporal, dual and nonlinear information flow during bull markets. Our results from the nonlinear and asymmetric causality tests remain robust after accounting for structural breaks. The empirical findings unravel nonlinear interactions between sector price and oil price changes as well as the importance of signs of changes in the interacting variables, implying oil returns may need to be priced when forecasting sector returns.

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Before testing for Granger causality, we performed unit root tests using ADF-GLS and KPSS methods. All the series become stationary after first differentiation. They are thus integrated of order zero, I(0). We also used multiple information criteria (see results in Table 3) to identify optimal lag order. The results are available upon request.

See a nice application of the Toda-Yamamoto model in the study by Nazlioglu and Soytas (2011).

A sample larger than fifty observations.

To skirt the possibility of the ARCH effects contaminating our results, where causality may be primarily driven by ARCH-type disturbances, we first filter the ARCH effects to ensure the results of the Granger causality and the M-G model are valuable and edifying.

For each pair of sector returns-oil returns, we select an optimal lag order using the Swartz Bayesian Information criterion (SBC). However, Ng (2002) notes that co-integration and causality tests are highly sensitive to lag orders hence the need to test for linear Granger causality at different lag orders.

GC is also sensitive to presence of heteroskedasticity or non-constant variance of residuals. Since results in Table 1 show presence of ARCH disturbances, we filter out the ARCH effects before running the GC tests.

The presence of nonlinearities, which define the dynamic interactions between sector price returns and changes in oil price, suggest that the dependence structure is certainly chaotic and deterministic (nonrandom). To discriminate between nonlinearities due to stochastic behavior (such as ARCH-type residuals or heteroskedasticity identified in Table 1) and nonlinearities induced by chaotic behavior, we first purge the linear dependence by running an AR model whose delay parameters are selected using Akaike Information criterion (AIC) and Bayesian information criterion (BIC).

To complement our test, we also minimize the Bayesian Information Criterion (BIC) to identify the breaks. The BIC approach permits for a penalty factor as the dimensions of the model increase.

Prior to establishing the results in Table 7, we first estimated the delay parameters, \(t_{1}\) and \(t_{2}\) and the exponents, \(c_{1}\) and \(c_{2}\), of the M-G model [see Eq. (2a) and (b)]. The parameters are selected a prior using the AIC. The results of the prior parameters are available upon request.

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Title: Symmetric and asymmetric nonlinear causalities between oil prices and the U.S. economic sectors
Authors: Jinghua Wang
Geoffrey Ngene
Publication date: 14-09-2017
Publisher: Springer US
Published in: Review of Quantitative Finance and Accounting / Issue 1/2018
Print ISSN: 0924-865X
Electronic ISSN: 1573-7179
DOI: https://doi.org/10.1007/s11156-017-0668-3

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