What Bayesian quantiles can tell about volatility transmission between the major agricultural futures?

https://doi.org/10.17221/127/2019-AGRICECONCitation:Živkov D., Kuzman B., Subić J. (2020): What Bayesian quantiles can tell about volatility transmission between the major agricultural futures? Agric. Econ. – Czech, 66: 215-225.
download PDF

This paper investigates an idiosyncratic volatility spillover effect between the four agricultural futures – corn, wheat, soybean, and rise. In order to avoid biased measurements of the volatilities, we use the Markov switching generalized autoregressive conditional heteroskedasticity (MS-GARCH) model. The created volatilities are imbedded in the Bayesian quantile regression framework which can produce accurate quantile estimates. We report that soybean and wheat receive relatively high levels of volatility shocks from the other markets, and that excludes soybean and wheat as primary investment assets in a portfolio. On the other hand, rice receives the lowest amount of volatility shocks from all other agricultural futures. The reason could be the policy of rice price stability that is conducted by countries in the Asia and Pacific region. This result favours rice futures, from the four commodities, as the primary asset in a portfolio. All other futures are suitable to be an auxiliary asset in a portfolio with rice, because rice receives the weakest volatility shocks spillover effect from the other three markets.

Ari Y., Keykubat A.A., Papadopoulos A. (2019): Bayesian estimation of Student-t GARCH model using Lindley’s approximation. Economic Computation and Economic Cybernetics Studies and Research, 53: 75–88.
Baldi L., Peri M., Vandone D. (2016): Stock markets’ bubbles burst and volatility spillovers in agricultural commodity markets. Research in International Business and Finance, 38: 277–285. https://doi.org/10.1016/j.ribaf.2016.04.020
Bali T.G., Cakici N. (2008): Idiosyncratic volatility and the cross-section of expected returns. Journal of Financial and Quantitative Analysis, 43: 29–58. https://doi.org/10.1017/S002210900000274X
Beckmann J., Czudaj R. (2014): Volatility transmission in agricultural futures markets. Economic Modelling, 36: 541–546. https://doi.org/10.1016/j.econmod.2013.09.036
Benoit D.F., van den Poel D. (2017): bayesQR: A Bayesian approach to quantile regression. Journal of Statistical Software, 76: 1–32. https://doi.org/10.18637/jss.v076.i07
Czapkiewicz A., Jamer P., Landmesser J. (2018): Effects of macroeconomic indicators on the financial markets interrelations. Czech Journal of Economics and Finance, 68: 267–292.
Dybczak K., Galuščak K. (2013): Changes in the Czech wage structure: Does immigration matter? Czech Journal of Economics and Finance, 63: 108–128.
Frommel M. (2010): Volatility regimes in Central and Eastern European countries’ exchange rates. Czech Journal of Economics and Finance, 60: 2–21.
Gozgor G., Memis C. (2015): Price volatility spillovers among agricultural commodity and crude oil markets: Evidence from the range-based estimator. Agricultural Economics – Czech, 61: 214–221.
Gray S.F. (1996): Modelling the conditional distribution of interest rates as a regime-switching process. Journal of Financial Economics, 42: 27–62. https://doi.org/10.1016/0304-405X(96)00875-6
Grófová Š., Srnec K. (2012): Food crisis, food production and poverty. Agricultural Economics – Czech, 58: 119–126. https://doi.org/10.17221/91/2011-AGRICECON
Hamadi H., Bassil C., Nehme T. (2017): News surprises and volatility spillover among agricultural commodities: The case of corn, wheat, soybean and soybean oil. Research in International Business and Finance, 41: 148–157. https://doi.org/10.1016/j.ribaf.2017.04.006
Huang W., Huang Z., Matei M., Wang T. (2012): Price volatility forecast for agricultural commodity futures: the role of high frequency data. Romanian Journal of Economic Forecasting, 15: 83–103.
Investing.com (2019): Investing.com website. Available at: https://www.investing.com/
Kirkulak-Uludag B., Lkhamazhapov Z. (2017): Volatility dynamics of precious metals: Evidence from Russia. Czech Journal of Economics and Finance, 67: 300–317.
Lee Y.-H., Fang H., Su W.-F. (2014): Effectiveness of portfolio diversification and the dynamic relationship between stock and currency markets in the emerging Eastern European and Russian markets. Czech Journal of Economics and Finance, 64: 296–311.
Maestri V. (2013): Imputed rent and distributional effect of housing-related policies in Estonia, Italy and the United Kingdom. Baltic Journal of Economics, 13: 37–60. https://doi.org/10.1080/1406099X.2013.10840532
Marcucci J. (2005): Forecasting stock market volatility with regime-switching GARCH models. Studies in Nonlinear Dynamics and Econometrics, 9: 1–53. https://doi.org/10.2202/1558-3708.1145
Masood O., Aktan B., Gavurová B., Fakhry B., Tvaronavičienė M., Martinkutė-Kaulienė R. (2017): The impact of regime-switching behaviour of price volatility on efficiency of the US sovereign debt market. Economic Research – Ekonomska Istraživanja, 30: 1865–1881. https://doi.org/10.1080/1331677X.2017.1394896
Matoškova D. (2011): Volatility of agrarian markets aimed at the price development. Agricultural Economics – Czech, 57: 34–40. https://doi.org/10.17221/143/2010-AGRICECON
Qu Y., Xiong P. (2019): Empirical study on the efficiency of the stock index-futures market from the information and functional perspectives – empirical evidence from China. Economic Research – Ekonomska Istraživanja, 32: 3733–3753. https://doi.org/10.1080/1331677X.2019.1674174
Ross S.A. (1989): Information and volatility. The no arbitrage and martingale approach to timing and resolution irrelevancy. Journal of Finance, 44: 1–17.
Sanjuan-Lopez A.I., Dawson P.J. (2017): Volatility effects of index trading and spillovers on US agricultural futures markets: A multivariate GARCH approach. Journal of Agricultural Economics, 68: 822–838. https://doi.org/10.1111/1477-9552.12216
Timmer C.P. (2014): Food security in Asia and the Pacific: The rapidly changing role of rice. Asia and the Pacific Policy Studies, 1: 73–90. https://doi.org/10.1002/app5.6
Vilerts K. (2018): The public-private sector wage gap in Latvia. Baltic Journal of Economics, 18: 25–50. https://doi.org/10.1080/1406099X.2018.1457356
Xiao J., Hu C., Ouyang G., Wen F. (2019): Impacts of oil implied volatility shocks on stock implied volatility in China: Empirical evidence from a quantile regression approach. Energy Economics, 80: 297–309. https://doi.org/10.1016/j.eneco.2019.01.016
Živkov D., Njegić J., Pećanac M. (2014): bidirectional linkage between inflation and inflation uncertainty – The case of Eastern European countries. Baltic Journal of Economics, 14: 124–139. https://doi.org/10.1080/1406099X.2014.993831
Živkov D., Njegić J., Stanković M. (2019): What wavelet-based quantiles can suggest about the stocks-bond interaction in the emerging East Asian economies? Czech Journal of Economics and Finance, 69: 95–119.
download PDF

© 2020 Czech Academy of Agricultural Sciences