Do commodity derivatives suspensions rein in food price inflation?

Since December 2021, derivatives trading on seven agricultural commodities remains suspended, driven by concerns over excessive speculation and its potential impact on food prices. This article highlights the irrelevance of such suspensions in containing food price inflation and recommends against such abrupt measures – including during periods of high food inflation – as they hinder the development of these markets.

In December 2021, the Securities and Exchange Board of India (SEBI), suspended derivatives¹ trading on seven agricultural commodities in response to rising food price inflation. The suspended commodities included chana (gram/Bengal gram), soybean, crude palm oil, paddy (except for basmati, a long-grained variety), wheat, soy oil, mustard seed, and moong (a type of lentil). Initially announced for one year, the suspension was extended annually and remains in place as of February 2025.

Commodity derivatives suspensions are however, not new in India. Since their launch on national commodity exchanges in 2003, commodity derivatives have seen multiple episodes of trading suspensions (Table 1). Most of these suspensions were announced during the global food price inflation of 2007-08, driven by concerns over excessive speculation and its potential impact on food prices. Suspensions on some of these commodities were lifted, but others, like tur and urad (types of lentil), have remained since 2007. This is in contrast to global markets, where such suspensions are rare, as derivatives play a key role in price discovery² and risk management³ (Gulati et al. 2017).

Table 1. Agri-commodities derivatives suspension in India, since 2000s

Commodity	Trade suspension year	Commodity	Trade suspension year
Raw Jute	2005	Potato	2008, 2014
Rice	2007	Sugar	2009
Wheat	2007, 2021	Guar (cluster bean)	2012
Chana	2008, 2016, 2021	Mustard Seed	2021
Tur	2007	Soybean	2021
Urad	2007	Moong	2021
Soya Oil	2008, 2021	Paddy (non-basmati)	2021
Rubber	2008	Crude Palm Oil	2021

Existing evidence

Does derivatives trading impact food prices? Amid concerns over excessive speculation in derivatives markets, the growing financialisation⁴ of commodities, and rising food prices, several academic studies have examined the impact of derivatives trading on food price inflation. However, the evidence remains mixed. Some studies suggest that institutional investor flows, unrelated to the underlying market fundamentals, influenced commodity prices, particularly during the global food price inflation period of 2007-08.⁵ Others find little evidence to support this claim. Cheng and Xiong (2014) argue that these conflicting results stem from limitations in empirical design, as weak strategies for identification of casual impact may lead to erroneous inferences – an issue that modern methods can better address.

In our study (Aggarwal, Chatterjee and Sehgal 2023), we apply the ‘Synthetic Control Method’ (SCM), developed by Abadie and co-authors (Abadie, Diamond, and Hainmueller, 2010; Abadie and Gardeazabal, 2003) to assess the impact of agricultural trading suspensions on food prices.⁶ This methodology involves constructing a counterfactual, or synthetic control, by creating a weighted combination of unaffected comparison units. By comparing actual price behaviour with that of the synthetic control, we can determine whether food prices would have followed a different trajectory in the absence of derivatives trading suspension.

Synthetic Control Method

The Synthetic Control Method (SCM) provides a data-driven approach to estimating the effects of a policy intervention by constructing a counterfactual based on a combination of unaffected comparison units. The key idea is to compare the outcome variable of the ‘treated’ unit (affected by intervention) with that of the synthetic control estimated from a weighted combination of comparison units that were not affected by the intervention. The selection of these comparison units and their corresponding weights is determined through a systematic, data-driven procedure, which is a key advantage of SCM.

In comparison to the traditional regression approach, the SCM offers several advantages. Regression methods are often not well-suited for case studies where an intervention affects a small number of large units (for example, regions or countries) or a single unit, as seen in commodity derivatives suspensions. Further, regression analysis typically requires large samples and multiple instances of an event, while trading suspensions are rare occurrences. Finally, unlike regression methods, SCM explicitly assigns weights to each comparison unit, reducing the risk of extrapolation bias and improving interpretability of the findings.

Suspension events

We examine three episodes of derivatives suspension in our study: the August 2021 chana derivatives suspension, the October 2021 mustard seed derivatives suspension, and the June 2016 chana derivatives suspension. Both chana and mustard seed contracts were heavily traded commodities on the Indian derivatives market, and therefore serve as a useful case study to analyse if derivatives suspension rein in food price inflation. Besides, in contrast to all other suspension events, these episodes involved a single commodity, which would enable us to identify any impacts.

Using data on prices as well as predictor variables (explanatory factors) including international prices, global production shocks, domestic production, net imports and establishment of mandis (farmers’ markets), we construct the synthetic control for each suspension episode. For instance, to estimate the impact of chana derivative suspension, we construct the synthetic control using data on other pulses produced in the country which did not face derivatives market trading suspension. Comparing the observed price trend for chana with that of this estimated counterfactual helps us answer whether the observed price path for chana would have been any different if derivatives trading were not suspended. Similarly, for mustard, the synthetic control is estimated using other oilseeds produced in the country that were not subject to such trading suspension.

Findings

The synthetic control analysis reveals that chana prices in August 2021 would have followed a similar trajectory even if derivatives trading had not been suspended (Figure 1). We note that the observed price series follows a very similar path as the synthetic control price series in both the pre- and post-intervention periods. The same holds for mustard seed derivatives suspension in October 2021 (Figure 2), wherein we find that while prices fell immediately after the suspension, the synthetic control analysis indicates that a similar price decline would have occurred even without the suspension. If derivatives suspension had impacted the prices, the synthetic control prices for both the commodities should have shown an upward trend. The absence of such an increase, and the presence of the same trend in the synthetic control price as the observed price series for both the commodities suggests that even without derivatives suspension, the prices of mustard and chana would have seen the same behaviour as observed with derivatives suspension. This implies that derivatives suspension had no impact on prices of these commodities. The evidence from the 2016 chana derivatives suspension, also points towards the absence of any impact of suspension on the prices. In fact, data from later years reveal that chana prices stabilised only after a bumper crop in the following year.

Figure 1. Weekly price trends in chana and its synthetic control for the August 2021 suspension

Figure 2. Weekly price trends in mustard oil and its synthetic control for the October 2021 suspension

Notes: (i) The graphs show the time series of the synthetic and observed weekly prices. (ii) The grey dotted line in both graphs indicates the end of the estimation period and beginning of the test/validation period. (iii) The black dashed line indicates the week when derivatives in chana and mustard oil, respectively, were suspended.

To explain our findings, we examine the degree of association between derivatives volumes and spot prices. A visual inspection of derivatives volumes and spot prices presents no direct relationship between the two. Furthermore, ‘Granger causality tests’ conducted to examine whether changes in derivatives trading volumes lead spot price changes show no evidence of forecastability. Across all three episodes, we find no indication that past changes in trading volumes contained information useful for predicting future spot price movements. The absence of any statistically significant relation between derivatives volumes and spot prices explains why the suspension of trading activity had no impact on spot prices in these three episodes.

Key learnings and policy implications

Our empirical analysis suggests that suspending derivatives trading had no discernible impact on controlling food price inflation. While identifying the exact drivers of observed price trends is beyond the scope of our study, we find indicative evidence that mustard oil prices in India closely followed international market trends. The Covid-19 lockdowns and the subsequent reopening of economies worldwide disrupted supply chains, impacting food prices globally. Similarly, production shortfalls and supply-demand mismatches contributed to the rise in chana and pulse prices during 2015-16.

Several studies, including Cheng and Xiong (2014), emphasise the role of derivatives markets in facilitating price discovery and risk management. However, abrupt trading suspensions negatively affect value chain participants⁷ and impede the development of a liquid⁸ derivatives market. Reports suggest that farmer producer organisations (FPOs) benefit from securing prices through derivatives markets (NCDEX, 2022). Efficient functioning of these markets can enhance stability in agricultural markets and protect farmers from sharp declines in spot prices during peak seasons. The findings from our study highlight the irrelevance of derivatives suspensions in containing food price inflation and recommend against such abrupt measures, including trading suspensions during periods of high food inflation, which hinder the development of these markets.

Notes:

A derivative is a financial instrument whose value depends on another asset, known as the ‘underlying’. The underlying can be a financial asset (like stocks, bonds, or currencies), or a physical commodity (like gold, crude oil, wheat, or soybeans). Derivatives can help businesses, farmers, and consumers manage price uncertainty (hedging), anticipate price movements (speculation), and determine fair market values (price discovery).
Price discovery is the process of determining the fair price of a commodity, stock, or asset in a market. It happens through the interaction of buyers and sellers as they trade based on supply, demand, and other factors. Derivatives help in price discovery by reflecting market participants' expectations about future prices. In actively traded derivatives markets, prices adjust based on supply, demand, weather conditions, and global events related to the underlying asset. This continuous buying and selling helps establish a fair market price. By analysing derivatives prices, farmers, businesses, and traders can make informed decisions about production, purchasing, and investments.
Price risk in agricultural commodities arises from uncertainty about the prices that a farmer, trader, business, or consumer will pay or receive in the future. For example, when sowing a crop, a farmer does not know what price they will get at the time of sale. One way to manage this risk is by using a derivatives contract, which allows them to lock in a price today for selling the crop at a future date. This helps eliminate uncertainty associated with the future price of the crop.
Since the early 2000s, commodity derivatives have gained popularity among portfolio investors, similar to stocks and bonds. This trend is known as the financialisation of commodity markets.
Studies, including Gilbert (2010) and Singleton (2014), argue that non-information based trading by institutional investor who do not have direct exposure to underlying commodities impacted commodity prices during the 2008-09 boom. Others such as Hamilton and Wu (2015) and Chari and Christiano (2017) find little evidence to support the claim.
See Abadie (2021) for a review of the methodology.
Value chain participants in agricultural commodities include all those entities involved in the process of bringing a crop from farm to consumer. This includes farmers who grow the crops, input suppliers providing seeds and fertilisers, traders and aggregators who buy and sell in bulk, processors and millers who convert raw crops into food products, wholesalers and distributors who transport goods, retailers who sell to consumers, and finally, the consumers themselves.
A liquid derivatives market allows buyers and sellers to trade contracts easily, without delays or major price swings, ensuring fair market prices. It enables businesses, farmers, and traders to buy and sell derivatives contracts smoothly and effectively manage their price risk.