DYNAMIC CORRELATION BETWEEN SELECTED CEREALS TRADED IN COMMODITY EXCHANGE MARKET IN AP VOJVODINA
Keywords:Agricultural commodities, dynamic conditional correlations, DCC-GARCH model
This paper investigates the level of pairwise dynamic correlations between prices of four agricultural commodities – corn, wheat soybean and barley that are traded in Novi Sad commodity exchange market. We use DCC-GARCH model, which is specially designed for this type or research. The results of the estimated dynamic conditional correlations show that low and positive correlation exist between all the pairs of the selected agricultural commodities, where the highest correlation is recorded between wheat and barley (24%), corn-barley pair follows (20%), while all other dynamic correlations are below 20%. The results indicate that price movements of the selected agricultural cereals are independent, which means that price discovery of one agricultural commodity does not provide information about the price of another agricultural commodity. Therefore, our results strongly suggest that traders in this market do not rely on the price co-movements between particular agricultural assets when they plan their selling or buying strategies, but to analyze fundamental macroeconomic factors.
Asai, M. (2013) Heterogeneous Asymmetric Dynamic Conditional Correlation Model with Stock Return and Range. Journal of Forecasting, 32(5), 469-480. doi: 10.1002/for.2252
Baffes, J., & Haniotis, T. (2016) What Explains Agricultural Price Movements? Journal of Agricultural Economics, 67(3), 706–721. doi: 10.1111/1477-9552.12172
Bollerslev, T., & Wooldridge, J. M. (1992). Quasi-maximum likelihood estimation and inference in dynamic models with time-varying covariances. Econometric Reviews, 11(2), 143–172. doi: 10.1080/07474939208800229
Bonato, M. (2019) Realized correlations, betas and volatility spillover in the agricultural commodity market: What has changed? Journal of International Financial Markets, Institutions and Money, 62, 184-202. doi: 10.1016/j.intfin.2019.07.005
Boroumand, R. H., Goutte, S., Porcher, S., & Porcher, T. (2014) Correlation evidence in the dynamics of agricultural commodity prices. Applied Economics Letters, 21(17), 1238–1242. doi: 10.1080/13504851.2014.922742
Dawson, P. J., & White, B. (2002) Interdependencies between agricultural commodity futures prices on the LIFFE. The Journal of Futures Markets, 22(3), 269–280. doi: 10.1002/fut.2217
De Nicola, F., De Pace, P., & Hernandez, M. A. (2016) Co-movement of major energy, agricultural, and food commodity price returns: A time-series assessment. Energy Economics, 57, 28–41. doi: 10.1016/j.eneco.2016.04.012
Đurić, D., Ristić, J., Đurić, D., & Vujanić, I. (2017) Export of agricultural and food products in the function of economic growth of republic of Serbia. Economics of Agriculture, 64(3), 887-900. doi: 10.5937/ekopolj1703887d
Engle, R. E. (2002) Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models.Journal of Business and Economic Statistics, 20(3), 339-350. doi: 10.1198/073500102288618487
Frommel, M. (2010). Volatility Regimes in Central and Eastern European Countries’ Exchange Rates. Finance a úver-Czech Journal of Economics and Finance, 60(1), 2-21.
Gardebroek, C., Hernandez,, M. A., & Robles, M. (2016) Market interdependence and volatility transmission among major crops. Agricultural Economics, 47(2), 141–155. doi: 10.1111/agec.12184
Gulan, B. (2014). Stanje i perspektive poljoprivrede i sela u Srbiji. Republika Srbija, Beograd: Privredna komora Srbije. [in English: Gulan, B. (2014) Condition and perspectives of agriculture and village in Serbia. Republic of Serbia, Belgrade: Economic Chamber of Serbia.]
Hou, Y., & Li, S. (2016). Information transmission between U.S. and China index futures markets: An asymmetric DCC GARCH approach. Economic Modelling, 52, 884-897. doi: 10.1016/j.econmod.2015.10.025
Jiang, Y., Jiang, C., Nie, H., & Mo, B. (2019) The time-varying linkages between global oil market and China’s commodity sectors: Evidence from DCC-GJRGARCH analyses. Energy, 166, 577-586. doi: 10.1016/j.energy.2018.10.116
Jones, P. M., & Olson, E. (2013) The time-varying correlation between uncertainty, output, and inflation: Evidence from a DCC-GARCH model. Economics Letters, 118(1), 33-37. doi: 10.1016/j.econlet.2012.09.012
Kang, S. H., & Yoon, S. M. (2020). Dynamic correlation and volatility spillovers across Chinese stock and commodity futures markets. International Journal of Finance & Economics, 25(2), 261-273.
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. Finance a úvěr-Czech Journal of Economics and Finance, 64(4), 296-311.
Li, Z., & Lu, X. (2012) Cross-correlations between agricultural commodity futures markets in the US and China. Physica A: Statistical Mechanics and Its Applications, 391(15), 3930–3941. doi: 10.1016/j.physa.2012.02.029
Marković, M., Krstić, B., & Rađenović, Ž. (2019) Export competitiveness of the Serbian agri-food sector on the EU market. Economics of Agriculture, 66(4), 941-953. doi: 10.5937/ekopolj1904941m
Milani, B., & Ceretta, S. P. (2014). Dynamic Correlation between Share Returns, NAV Variation and Market Proxy of Brazilian ETFs. Engineering Economics, 25(1), 21–30. doi: 10.5755/j01.ee.25.1.4274
Onay, C., & Ünal, G. (2012) Cointegration and Extreme Value Analyses of Bovespa and the Istanbul Stock Exchange. Finance a úvěr-Czech Journal of Economics and Finance, 62(1), 66-90.
Singhal, S., & Ghosh, S. (2016) Returns and volatility linkages between international crude oil price, metal and other stock indices in India: Evidence from VAR-DCC-GARCH models. Resources Policy, 50, 276-288. doi: 10.1016/j.resourpol.2016.10.001
Živkov, D., Njegić, J., & Pavlović, J. (2016). Dynamic correlation between stock returns and exchange rate and its dependence on the conditional volatilities – the case of several Eastern European countries. Bulletin of Economic Research, 68(S1), 28-41. doi: 10.1111/boer.12059