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This paper examines the return and volatility spillovers among major cryptocurrency using daily data from 10/08/2015 to 15/04/2018.

The authors employ the Dielbold and Yilmaz (2012) spillover approach and rolling sample analysis to capture the inherent secular and cyclical movements in the cryptocurrency market.

The authors show that there is substantial difference between the behaviour of the cryptocurrency portfolios return and volatility spillover indices over time. The authors find evidence of interdependence among cryptocurrency portfolios given the spillover indices. While the return spillover index reveals increased integration among the currency portfolios, the volatility spillover index experiences significant bursts during major market crises. Interestingly, return and volatility spillovers exhibit both trends and bursts respectively.

This study makes a methodological contribution by adopting Dielbold and Yilmaz (2012) approach to quantify the returns and volatility transmissions among cryptocurrencies. To the best of our knowledge, little or no study has adopted the Dielbold and Yilmaz (2012) methodology to investigate this dynamic relationship in the cryptocurrencies market. The Dielbold and Yilmaz (2012) approach provides a simple and intuitive measure of interdependence of asset returns and volatilities by exploiting the generalized vector autoregressive framework, which produces variance decompositions that are unaffected by ordering.

This paper aims to model the relationship between oil price and six major agricultural commodity prices using monthly data from January 1997 to December 2016.

The authors use both the linear autoregressive distributed lag by Pesaran et al. (2001) and the nonlinear autoregressive distributed lag by Shin et al. (2014), and they also account for structural breaks using the Bai and Perron (2003) test that allows for multiple structural changes in regression models.

These findings are discernible from the authors’ analyses. First, the linear analysis indicates a significant positive effect of oil prices on the agricultural commodity prices, which supports evidence on the non-neutrality hypothesis. Second, oil price asymmetries seem to matter more when dealing with agricultural commodity prices, except for groundnut. Third, it may be necessary to pre-test for structural breaks when modelling the relationship between oil price and agricultural prices regardless of the commodity being analysed. Fourth, the asymmetric effect for the agricultural commodity prices is non-neutral to oil prices, except for rice in the case of structural breaks.

This paper contributes to the on-going debate on the oil–agricultural commodity nexus using the recent technique of asymmetry and also considering the role structural breaks play in the relationship between oil price and agricultural commodity prices.