Abstract
This paper presents new evidence on the existence of asymmetries in the transmission of shocks in oil prices in the main European fuel markets and their relation to the so-called rockets and feathers effect. Our approach differs from the existing literature in two ways: (1) the data used: we use forward prices rather than spot prices because fuel leaders use forward contracts to buy crude oil. (2) The methodological approach is different. We adopt a more sophisticated econometric model, the Markov-switching model, and use it to contrast the robustness of the results obtained with the TAR-ECM methodology with an endogenous threshold (nonzero threshold). In general, the results show evidence of an asymmetric response of gasoline and diesel prices to changes in the price of crude oil, both in the short-run and with respect to the adjustment towards long-run equilibrium. These price asymmetries fall in line with the “rockets and feathers” hypothesis.
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Notes
The different levels of competition between the countries or between the oil segments within the same country (refining, wholesale and retail segments) may trigger price volatility, which in turn may lead to price behaviour asymmetry (Polemis and Panagiotis 2013). However, Peltzman (2000) found no clear link between competition and asymmetric price transmission.
Only Grasso and Manera (2007) estimate an endogenous threshold to detect price asymmetry in the gasoline market. Contrary to our results, their results are inconclusive and they find significant and positive values for the threshold in function of the stage under study (refinery, distribution or both). We, however, find negative and significant threshold values across all the countries for both the gasoline and diesel market.
Boroumand et al. (2016) also use the Markov-switching approach for the diesel market in the French economy. However, they use the Markov-switching approach in a first stage to identify two samples according to the volatility of crude oil price. Once the sample is split in two, according to low or high volatility, they apply the traditional exogenous threshold methodology (positive or negative) to identify possible asymmetries in the behaviour of prices in each of the samples. By contrast, our work uses Markov-switching to characterize asymmetries in the whole dynamics of the transmission mechanism, i.e. in our case, the estimated parameters of the dynamic relation between retail and crude oil prices are regime-switching, obtained by implementing the Markov-switching methodology.
This paper uses a methodology similar to the TAR-ECM of our paper, with the difference that the multiple estimated thresholds are exogenous.
Given the different fiscal systems which characterize the countries under analysis, this choice will ease the comparison of the empirical findings between economies.
We could also consider more restricted versions of Eq. (3) by imposing that certain parameters are the same under both regimes, and only a few of them to change with the regime. We have opted for the most general case by allowing that all the parameters to be different under each regime.
A more detailed explanation can be found in chapter 11 of Hamilton (1994).
Given the presence of only two regimes, we choose regime 1 without loss of generality.
This analysis is available upon request.
For example Bermingham and O’Brien (2011).
Following Hansen (1997) we construct these confidence intervals inverting the likelihood ratio test statistic to test the hypothesis that the threshold is equal to some specific value c\(_{0,}\) given by
$$\begin{aligned} LR\left( {c_{0} } \right) = n\left( {\frac{{\hat{\sigma }^{2} \left( {c_{0} } \right) - \hat{\sigma }^{2} \left( {\hat{c}} \right) }}{{\hat{\sigma }^{2} \left( {\hat{c}} \right) }}} \right) . \end{aligned}$$Notice that LR(\(c_{0})=0\). The 100x \(\alpha \)% confidence interval of the threshold is given by the set consisting of those values of c for which the null hypothesis is not rejected at significance level \(\alpha \). That is:
$$\begin{aligned} \hat{C}_{\alpha } = \left\{ {c:LR\left( c \right) \le z\left( \alpha \right) } \right\} , \end{aligned}$$where \(z(\alpha )\) is the \(100\times \)\(\alpha \) percentile of the asymptotic distribution of the LR statistic. These percentiles are given in Hansen (1997, Table 1) for various values of \(\alpha \). The set \(\hat{C}_\alpha \) provides a valid confidence region as the probability that the true threshold value is contained in \(\hat{C}_\alpha \) approaches \(\alpha \) and the simple size n becomes large. A graphical method used to obtain the region \(\hat{C}_\alpha \) is plotted in the LR statistic represented in Figs. 1 and 2.
In the simulation we have assumed that, prior to the shock, the system is placed in the long-run equilibrium (\(\hat{\epsilon }_{-1}=0\)) and we have normalized to 1 the price of oil prior to the shock (\(p{_{-1}}=1\)).
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Pérez and Ruiz would like to thank the Spanish Ministry of Economy and Competitiveness and FEDER for the financial support provided through Grant ECO2015-67305-P and the Bank of Spain through Grant PR71/15-20229. Martín-Moreno thanks Spanish Ministry of Economy and Competitiveness and FEDER and the Xunta de Galicia for financial support through Grants ECO2015-68367-R and Programa de Consolidación e Estructuración de Unidades de Investigación Competitivas do Sistema Universitario de Galicia 2014, de la Consellería de Cultura, Educación e Ordenación Universitaria (referencia GRC2014/021). The authors are indebted to the participants at the 17th Eurasia Business and Economic Society Conference for their helpful comments. Finally, the authors would also like to thank the editor and an anonymous referee for his/her constructive review, which has strengthened the paper.
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Martín-Moreno, J.M., Pérez, R. & Ruiz, J. Evidence about asymmetric price transmission in the main European fuel markets: from TAR-ECM to Markov-switching approach. Empir Econ 56, 1383–1412 (2019). https://doi.org/10.1007/s00181-017-1388-1
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DOI: https://doi.org/10.1007/s00181-017-1388-1