The rise of Soybean in international commodity markets: A quantile investigation

Abstract

The complex interplay between agricultural and energy commodities has been a subject of interest in past research, gaining more relevance recently due to geopolitical events such as the conflict between Ukraine and Russia. This conflict has systematically driven up the prices of both energy and agricultural commodities. Deeply understanding the dynamic interconnections between these commodities and the cascading events resulting from the war is crucial for comprehensive market analysis. Our study leverages the connectedness or risk of spillover based on a Quantile Vector Autoregression (QVAR) model, allowing us to track connectedness over time through the examination of extreme quantiles. This approach facilitates the identification of shocks triggered by exogenous events, such as the Russian-Ukrainian war, which are often observable in these extreme quantiles or tails. The investigation encompasses several agricultural commodities: Wheat, Barley, Soybean, Soybean Oil, Soybean Meal, and Sunflower Oil, along with energy commodities represented by Crude Oil and Natural Gas. Furthermore, we considered the prices of crucial fertilizers, DAP & Urea, given their significance in agricultural production. The timeframe for our study extends from January 2010 to January 2023, providing a comprehensive review of market trends during various geopolitical scenarios. This research contributes valuable insights into the intersection of global events, agricultural trends, and energy commodity markets. The study revealed that Soybean and its derivatives consistently play a leading role in the market, with Soybean being the primary shock transmitter. This is particularly true for the upper Quantile, where Soybean and Soybean Meal's influence remains stable. On the other hand, Soybean Oil's, Barley, and Wheat risk of spillover has increased, especially during the Ukraine-Russia conflict. Finally, spillover appears symmetric, with both extreme tails exhibiting around 91-87 % connectedness, while the median Quantile is under 49 %. We observed a diminution in network complexity, manifested as a decline in network connectedness, in correlation with extreme quantiles. Policymakers can use this information to draft proactive measures, ensuring stability and sustainability in both domestic and international markets.

Keywords: Commodity markets; Connectedness index; Quantile VAR; Risk spillover; Soybean international market.

Chart 1

Time series graphical analysis.

Chart 2

Total connectedness index (TCI) (Q 0.5).

Chart 3

represents the Network Plot for quantile 0.5. As before mentioned, soybean plays the leading the studied market, being the main net transmitter followed by Soybean oil and Soybean meal. Together the three the only net transmitting leading the shocks spillover in the market Phosphate fertilizer and Urea the most susceptible commodities and net receivers.

Chart 4

Net pairwise directional connectedness (NPDC) (Q 0.5).

Chart 5

Net total directional connectedness (NTDC) (Q 0.5).

Chart 6

Total Connectedness index (TCI) (Q 0.1).

Chart 7

Network connectedness (NC) (Q 0.1).

Chart 8

Net pairwise directional connectedness (NPDC) (Q 0.1).

Chart 9

Net total directional connectedness (NTDC) lower Quantile (Q 0.1).

Chart 10

Total connected index (TCI) (Q 0.9).

Chart 11

Network connectedness (NC) (Q 0.9).

Chart 12

Net pairwise directional connectedness (NPDC) upper Quantile (Q 0.9).

Chart 13

Net total directional connectedness (NTDC) upper Quantile (Q 0.9).

Chart 14

Tci over various quantiles.