^{ 1 }MRM, Université de Montpellier

^{ 1 }

^{ 1 }

^{ * }Date of publication: 06/18/2020

Starting January 2018, European trading platforms are under the obligation of reporting the trading positions of each agent in the market. This reform follows the entry into force of the second Markets in Financial Instruments Directive (MiFID II), a European legislative framework that intends to improve the functioning of financial markets. Given that one of its main objectives is to increase transparency, MiFID II imposes new reporting requirements, where trading platforms communicate daily to the local market authority the aforementioned positions, and publish an aggregate report of these positions weekly. And thus these regulations apply to Euronext’s commodity products.

It is therefore along these lines that Euronext started publishing in 2018 the weekly Commitment of Traders (CoT) reports, that detail the operations by type of agent (investment firms or credit institutions, investment funds, other financial institutions and commercial undertakings), type of activity (hedging or other) and type of position (long or short). The reports indicate aggregate volumes per category but also the percentage of total open interest and their structure is close to the CoT reports of the US Commodity Futures Trading Commission (CFTC). However, the positions being self-reported and the late availability of these reports do not facilitate complete microstructure analyses.

We take an interest in the rolling activity performed by hedgers, meaning the transfer of the positions held in the nearest contract to other next contracts by closing the current contract and simultaneously opening another one in a further maturity. In other words, rolling a hedge is performed “by closing out one futures contract and taking the same position in a futures contract with a later delivery date” (Hull

Estimating the rollover amount is important when analyzing the information content of trading volumes. As a proxy for information flow (

On the other hand, examining rollovers enables us to better understand the behavior of contract holders, especially hedgers.

For this, we depart from the model of

We then empirically illustrate the new rollover model with the futures contract on milling wheat (EBM), that is considered as a “global price benchmark for the European underlying physical markets” by Euronext (

To the best of our knowledge, the analysis of the rollover activities has never been explored previously, as the only result we can find is that for EBM, 75 to 85% of the activity is led by hedgers (

In light of the above, we try to answer the following research questions: How can an additional hypothesis allow us to have a more accurate rollover measure? And what are the dynamics of the rolling activity in the milling wheat futures contract?

We first formulate the following assumptions: There are 2 categories of investors: hedgers and speculators. The number of hedgers is significantly constant during the nearest contract’s lifetime.

The hedgers are on the nearest contract and they rollover their positions towards the last sessions before expiration (not necessarily to the first next contract). Hedgers avoid rolling over too late for liquidity reasons of liquidity.

The remainder of the article is structured as follows. Section 2 presents the rollover model’s novelty compared to the

To have a clearer picture of the traders’ behavior, we study their rollover strategy. It enables us to induce the number of contracts rolled over that are hedged. For that, the model of

By formulating the links between open interest and volume for the near and next contracts,

They present their model as follows:

_{
}

Where V_{t}
^{i}
_{is the volume for maturity i, with 1 being the nearest maturity; ΔOIt
i is the change in open interest; Et
i is the number of entries (contracts opened); St
i is the number of exits (contracts closed); and rt is the number of contracts rolled over. }

They obtain the following relationship:

This relationship is then generalized for a rollover to

We depart from this relationship that we rearrange as

_{t }is the quantity of contracts rolled, it does not provide any new market information, hence it cannot be used as a proxy for “information arrival”. This implies that

r_{t} can be considered as a hedged quantity, given that only hedgers keep their positions long enough to roll it. Also, by considering that it is rolling, they omit the case where speculators operate the same day on the 2 (or n) contracts (sale with exit of OI in the nearest contract and purchase with entry of OI in the Next (or others)). This way, their bound varies significantly and by omitting to verify that (E-S)>0, the number of rolled over contracts is overestimated.

For this reason, we suggest to rewrite r_{t}, the number of contracts rolled over as follows:

r_{t }is the total number of rolled contracts for the n sessions that form the lifetime of the contract, from the opening to the expiration day.

that can be written as:

is the proportion of contracts rolled on day t, with respect to the total amount of contracts rolled, and

.

Moreover, the hedging open interest for a specific period cannot exceed the lowest open interest of that period. Similarly, the hedging open interest over multiple maturities for a specific period cannot exceed the lowest sum of open interests of these contracts.

This sum becomes the new upper bound for the hedging rollovers that replaces

r_{i}, and we denote it

.

Since the open interest is equal to sum of the number of hedging contracts

and the number of speculative contracts (E-S), and since the latter cannot be negative, we add an additional restriction. Whenever (E-S) is negative in the data, we lower the number of hedging contracts such that (E-S) becomes non negative and that the open interest stays unchanged. To do that, we simply subtract a variable

to

when needed. This verification is not performed by

The model is presented considering that the rollovers are performed from the near contract to the second next. However, we can also consider that the rollover can happen from the near to all the other next contracts (up until the 12^{th} next contract).

We apply our model to the futures contract EBM, traded on Euronext. For this purpose, we use the daily volume and open interest data available on Euronext’s website that we collect and synthesize. We carry out our analysis for different contracts: from the November 2011 maturity contract to the September 2019 maturity contract, while excluding the August 2012 maturity that comprises outlier values. We are then left with 33 contracts. Since the traded volume is low at the beginning of the contract’s life, and since different contracts have different life lengths, we start the analysis 300 trading sessions before expiration for all contracts, to be able to aggregate our results.

We consider the 33 contracts, and for each contract we compute the daily sum of the near and the first next volumes, but also the daily sum of the near volume and all other next contracts volumes, up to 12 maturities, when available. The same total values are computed for the open interest. We then proceed in two steps:

We compute the parameters of our model, separately for the near and next contract in the first place, and then for the near and the 12 other next contracts. These parameters are: r_{t} the number of contracts rolled, X the number of hedging contracts per session and (E-S) the number of speculative contracts.

These parameters allow us to obtain the variables

,

and ∑r_{t}, and to compute

.

is the open interest devoted to hedging with our approach after correcting for the non-negative speculative contracts, ∑r_{t} is the open interest devoted to hedging according to

is the open interest hedged on the near contract, hence

allows us to compare our measure to the one of

For each contract, we start the analysis 20 sessions before expiration, because as stated in our assumptions, we suspect rollover to start few sessions before expiration. But also in order to have the same period of analysis for all contracts.

First, we look at the evolution of α, the percentage of contracts rolled by session () for the last 20 sessions. We notice that hedgers leave the near contracts before the last week (the last 5 days) in the case where they roll to the first next but also in the case where they roll to all the other next contacts.

Second, by looking at the evolution of the number of hedging contract

for the last sessions (), we notice an increase with time, as the hedged open interest in the near and all other contracts almost doubled (it is multiplied by 1.67) from the January 2012 maturity contract to the September 2019 maturity contract. We also note that hedgers do not necessarily stay in the near and first next contract: on average, the number of contracts for hedging is 124 631 for the near and first next contracts, and 247 129 for the near and all other next contracts. It reaches important levels: 230 730 for the near and first next contracts, and 407 940 for the near and all other next contracts (Table 1 and Table 2). Graphically, we see that the hedging contracts still open (

) are roughly the same for the near contract, whether the rollover takes place in the first next or all the other next contracts, except for some contracts where the open interest is higher for rollovers in all the other next contracts.

illustrates the percentage by which we decrease

), that counts the hedging open interest in the near contract, devoted to hedging. We compare it to

obtain with our model, and that also represents the hedging open interest in the near contract. Hence is the plot of 1-

. On average, the bound is decreased from 100% to 29% for the near and first next contract, and from 100% to 31% for the near and all other next contracts (Table 1 and Table 2). These cases occur when the amount of speculative contracts is negative.

titre du tableau
Min
59 927
34 477
41 451
-0.068
1
^{st} Qu.90 014
62 360
89 320
0.245
Median
115 674
74 841
110 565
0.295
Mean
124 631
77 885
110 655
0.285
3
^{rd} Qu.149 169
90 596
134 030
0.335
Max.
230 730
142 402
180 808
0.459

titre du tableau
Min
134 924
30 782
41 961
-0.063
1
^{st} Qu.205 034
55 156
85 590
0.266
Median
248 483
73 681
115 762
0.316
Mean
247 129
76 716
112 499
0.309
3
^{rd} Qu.285 029
88 298
130 762
0.346
Max.
407 940
139 631
189 494
0.676

For this study, we analyze the 20 last sessions by studying the rollover behavior of hedgers. First, we extend

This work was supported by the French National Research Agency under the Investments for the Future Program, referred as ANR-16-CONV-0004 and by the University of Montpellier. They had no involvement in study design; in the collection, analysis and interpretation of data; in the writing of the report; and in the decision to submit the article for publication.