This paper sheds light on the role of different sources of uncertainty on agricultural futures markets momentum trading and volatility. Momentum trading is proxied by two technical analysis indicators – the moving average convergence divergence and the relative strength index – while we also consider two different concepts of uncertainty – the CBOE volatility index of the S&P500 and daily news about the stance of economic policy in the US. To capture different effects on the transmission mechanism of uncertainty shocks, we implement a Bayesian VAR approach, which accounts for time-variation in the coefficients and the variance covariance structure of the model’s innovations. The results point in favor of a time-dependent uncertainty effect on expectations of daily momentum traders in agricultural futures markets. The corresponding trades in these periods push futures prices upwards and downwards and result in an increased volatility. Direct effects of both uncertainty sources on the volatility of agricultural futures markets confirm this view.
Arias, M. A., A. M. Ibáñez, and A. Zambrano. 2019. “Agricultural Production Amid Conflict: Separating the Effects of Conflict into Shocks and Uncertainty.” World Development 119: 165–184.10.1016/j.worlddev.2017.11.011)| false
Bahloul, W., M. Balcilar, J. Cunado, and R. Gupta. 2018. “The Role of Economic and Financial Uncertainties in Predicting Commodity Futures Returns and Volatility: Evidence from a Nonparametric Causality-in-Quantiles Test.” Journal of Multinational Financial Management 45: 52–71.
Bahloul, W., M. Balcilar, J. Cunado, and R. Gupta. 2018. “The Role of Economic and Financial Uncertainties in Predicting Commodity Futures Returns and Volatility: Evidence from a Nonparametric Causality-in-Quantiles Test.” Journal of Multinational Financial Management 45: 52–71.10.1016/j.mulfin.2018.04.002)| false
Bessembinder, H., J. F. Coughenour, P. J. Seguin, and M. M. Smoller. 1995. “Mean Reversion in Equilibrium Asset Prices: Evidence from the Futures Term Structure.” Journal of Finance 50: 361–375.10.1111/j.1540-6261.1995.tb05178.x)| false
Czudaj, R. L. 2019b. “Dynamics between Trading Volume, Volatility and Open Interest in Agricultural Futures Markets: A Bayesian Time-Varying Coefficient Approach.” Econometrics and Statistics forthcoming. https://doi.org/10.1016/j.ecosta.2019.05.002.
Darby, J., and G. Roy. 2019. “Political Uncertainty and Stock Market Volatility: New Evidence from the 2014 Scottish Independence Referendum.” Scottish Journal of Political Economy 66: 314–330.
Darby, J., and G. Roy. 2019. “Political Uncertainty and Stock Market Volatility: New Evidence from the 2014 Scottish Independence Referendum.” Scottish Journal of Political Economy 66: 314–330.10.1111/sjpe.12186)| false
Fisher, T. J., and C. M. Gallagher. 2012. “New Weighted Portmanteau Statistics for Time Series Goodness of Fit Testing.” Journal of the American Statistical Association 107: 777–787.10.1080/01621459.2012.688465)| false
Gerritsen, D. F. 2016. “Are Chartists Artists? The Determinants and Profitability of Recommendations Based on Technical Analysis.” International Review of Financial Analysis 47: 179–196.10.1016/j.irfa.2016.06.008)| false
Glosten, L. R., R. Jagannathan, and D. E. Runkle. 1993. “On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks.” Journal of Finance 48: 1779–1801.10.1111/j.1540-6261.1993.tb05128.x)| false
Han, Y., K. Yang, and G. Zhou. 2013. “A New Anomaly: The Cross-Sectional Profitability of Technical Analysis.” Journal of Financial and Quantitative Analysis 48: 1433–1461.10.1017/S0022109013000586)| false
Liu, Y., L. Han, and L. Yin. 2018. “Does News Uncertainty Matter for Commodity Futures Markets? Heterogeneity in Energy and Non-Energy Sectors.” Journal of Futures Markets 38: 1246–1261.10.1002/fut.21916)| false
Mougoué, M., and R. Aggarwal. 2011. “Trading Volume and Exchange Rate Volatility: Evidence for the Sequential Arrival of Information Hypothesis.” Journal of Banking & Finance 35: 2690–2703.10.1016/j.jbankfin.2011.02.028)| false
Smith, D. M., N. Wang, Y. Wang, and E. J. Zychowicz. 2016. “Sentiment and the Effectiveness of Technical Analysis: Evidence from the Hedge Fund Industry.” Journal of Financial and Quantitative Analysis 51: 1991–2013.
Smith, D. M., N. Wang, Y. Wang, and E. J. Zychowicz. 2016. “Sentiment and the Effectiveness of Technical Analysis: Evidence from the Hedge Fund Industry.” Journal of Financial and Quantitative Analysis 51: 1991–2013.10.1017/S0022109016000843)| false
Yin, L., Q. Yang, and Z. Su. 2017. “Predictability of Structural Co-Movement in Commodity Prices: The Role of Technical Indicators.” Quantitative Finance 17: 795–812.10.1080/14697688.2016.1225977)| false
SNDE recognizes that advances in statistics and dynamical systems theory can increase our understanding of economic and financial markets. The journal seeks both theoretical and applied papers that characterize and motivate nonlinear phenomena. Researchers are required to assist replication of empirical results by providing copies of data and programs online. Algorithms and rapid communications are also published.