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The role of uncertainty on agricultural futures markets momentum trading and volatility

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  • Czudaj Robert L.

    (Chemnitz University of Technology, Department of Economics and Business Administration, Chair for Empirical Economics, D-09126 Chemnitz, Germany, Phone: +0049-371-531-31323, Fax: +0049-371-531-831323)

Abstract

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.

Suggested Citation

  • Czudaj Robert L., 2020. "The role of uncertainty on agricultural futures markets momentum trading and volatility," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 24(3), pages 1-39, June.
    • Handle: RePEc:bpj:sndecm:v:24:y:2020:i:3:p:39:n:6
    DOI: 10.1515/snde-2018-0054
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    Cited by:

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    2. Modhurima Dey Amin & Syed Badruddoza & Oscar Sarasty, 2024. "Comparing the great recession and COVID‐19 using Long Short‐Term Memory: A close look into agricultural commodity prices," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 46(4), pages 1406-1428, December.

    More about this item

    Keywords

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    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • Q14 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Agricultural Finance

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