Abstract
We present evidence of an asymmetric relationship between oil prices and stock returns. The two regime multivariate Markov switching vector autoregressive (MSVAR) model allow us to capture the state shifts in the relationship between regional stock markets and sectors. Results suggest that oil price risk is significantly priced in the sample used. The impact is asymmetric with respect to market phases, and regimes have been associated with world economic, social and political events. Our study also suggests asymmetric responses of sector stock returns to oil price changes and different transmission impacts depending on the sector analyzed. There is a high causality from oil to sectors like Industrials and Oil & Gas. Companies inside the Utilities sector were more able to hedge against oil price increases between 2007 and 2012. Historical crisis events between 1992–1998 and 2003–2007 do not seem to have affected the relationship between oil and sector stock returns, given the higher probability of remaining smoother. For all sectors there seems to be a turn back to stability from 2012 onwards. Finally, investors gain more through portfolio diversification benefits built across, rather than within sectors.
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Notes
This subgrouping includes the Industry Sector, Energy Sector, Energy Equipment & Services, Oil & Gas & Consumable fuels, Oil & Gas Exploration & Production and Oil & Gas Storage & Transportation indexes.
Results for unit root and cointegration tests are not presented here, but will be made available upon request.
In each m-partition (T1,…,Tm), the associated least squares estimates of the parameters (β1, …, βm) are obtained by minimizing the sum of squared residuals. Substituting the resulting estimates in the objective function and denoting the resulting sum of squared residuals S(T1, …, Tm), the estimated break points \( \left({\widehat{T}}_1,\dots, {\widehat{T}}_m\right) \) are the solution of the minimization of S(T1, …, Tm) over all partitions. Only afterwards are the stability tests implemented, in a sequential procedure. First, stability of the trend is tested against the hypothesis of one break. If stability is rejected, then one break date is imposed on the model, and the hypothesis of one break is tested against the hypothesis of two breaks. The second break date is obtained by testing all the possible models with two breaks after knowing the first break date against the one break model. The procedure is repeated until the number of breaks and the corresponding break dates are determined.
The rest of the results obtained for individual countries will be made available upon request. They showed to be very similar to those of the region representing them, and that is why they have been omitted.
Although results are not presented here for different regions they could be provided upon request.
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Acknowledgments
Empirical work was performed with Matlab. We thank the participants of the first ICEE Energy & Environment Conference, FEP, University of Porto held in 9-10 May, 2013, for their helpful comments. The usual caveat applies.
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Pinho, C., Madaleno, M. Oil prices and stock returns: nonlinear links across sectors. Port Econ J 15, 79–97 (2016). https://doi.org/10.1007/s10258-016-0117-6
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DOI: https://doi.org/10.1007/s10258-016-0117-6