Measuring price discovery in the European wheat market using the partial cointegration approach

Abstract

Understanding price discovery in agricultural spot and futures markets is important for market participants and policy makers, because it can contribute to better management decisions and more informed policy debates on market regulation. Combining partial cointegration with state space modelling, we generate time-varying price discovery metrics for the European wheat market that allow for shifts in the long-run relationship. We find that the futures market dominates price discovery in terms of efficiency, but that this dominance is reduced in phases of higher price volatility. We find evidence of persistent shocks in the long-run relationship between spot and futures prices that appear to be related to variations in the quality of the wheat harvest, and to the concatenation of the futures prices.

Price Discovery of Agri Commodities: An Integrated Approach

“…The government is aiming to remove the information asymmetry between traders and farmers and promote real-time price discovery across the markets. 6 Understanding the price discovery in agriculture is important for market applicants and policymakers because it can contribute to better management decisions and more informed policy debates on market regulation [7]. It would be beneficial to study the flow of information between the markets and the commodities as it could be beneficial for traders and farmers both.…”

Estimating and Comparing Market Efficiency Frontiers

“…The time-varying price transmission analysis has already been the subject of several studies in the agricultural commodity price discovery context (e.g. Vollmer et al, 2020). We propose to explore whether it can also be used to generate insights and extend the literature in our market efficiency benchmark applications.…”

“…Over time, increasingly sophisticated ECMs have been used to account for different types of non-linearity or regime dependence in price transmission. These include asymmetric ECMs (von Cramon-Taubadel, 1998); ECMs that include threshold effects in long-run equilibrium relationships (threshold cointegration - Gonzalo and Pitarakis, 2006) as well as in error correction (threshold autoregression - Abdulai, 2000;threshold ECMs -Goodwin and Piggott, 2001;Greb et al, 2013); nonparametric ECMs (Serra et al, 2006;Guney et al 2019); Markov-switching ECMs (Brümmer et al, 2009); and other forms of ECM with time-varying error correction (such as state space models -Adä mmer and Bohl, 2015Bohl, , 2018Vollmer et al 2020). All of these methods are motivated by efforts to account for the fact that the relationship between two prices in space will not necessarily be constant over time due to phenomena such as changes in trade costs, policies and the direction of trade (von Cramon-Taubadel and Goodwin, 2021).…”

“…The time-varying VECM that we estimate enables us to have short-run interactions between prices in question vary over time. However, the assumption of a time-invariant long-run relationship might be not plausible for heterogeneous agricultural commodities, and the relationship between prices in different markets probably is subject to shocks that could lead to persistent changes in price relationship (Vollmer et al, 2020).…”

“…Consequently, the elasticities and adjustment parameters estimated and analysed for measuring market efficiency in the previous chapters may have changed over the sample period. In this chapter, we divide the whole sample period into four sub-periods and focus on evaluating price The state space estimation of VECM has been used to analyze price discovery (Silverio and Szklo, 2012;Caporale et al, 2014;Bohl, 2015, 2018;Vollmer et al, 2020). Estimates of price transmission using time-varying VECM under state-space form make it appealing to investigate whether the contributions of markets to price transmission shift over time (Adä mmer and Bohl, 2018;Vollmer et al, 2020).…”