Mouvement pour une Organisation Mondiale de l'Agriculture
momagri est un think tank présidé par Christian Pèes,  qui rassemble des responsables du monde agricole
et des personnalités d’horizons extérieurs (santé, développement, stratégie et défense,…).
Son objectif est de promouvoir une régulation des marchés agricoles en créant de nouveaux outils d’évaluation
(modèle économique, indicateurs,…) et en formulant des propositions pour une politique
agricole et alimentaire internationale.

Volatilité des prix des matières premières :
causes et conséquences sur les marches agricoles européens

Par Pr. Bertrand Munier, chef économiste de momagri

Dans le cadre des travaux du Parlement européen sur la PAC d’après 2013, momagri a été auditionné par la Commission de l’Agriculture et du développement rural. Le chef économiste de momagri, le Pr. Bertrand Munier, a ainsi présenté le 2 juin dernier le résultat de ses travaux sur la volatilité des prix sur les marchés agricoles, ainsi que le modèle momagri. Sa présentation a été très bien reçue par les membres de la Commission de l’Agriculture et du développement rural.

Nous vous présentons ici la lecture du document rédigé par le Pr. Munier qui a été distribué aux députés européens en support à sa présentation1.

La rédaction de momagri

Bertrand Munier, chef économiste de momagri, “Commodity price volatility : causes and impact on the EU agricultural markets”, note pour la Direction générale des politiques interne, Commission de l’Agriculture et du développement rural, Parlement européen, juin 2010.


“The equilibrium state of production is like the equilibrium
state of exchange, an ideal state, not a real one.
It never materializes […].”
L. Walras (1878)2

Markets evolve, so should markets’ theories do and, to a large extent, they effectively do so3. But many models used for practical policies lag behind. Policy makers often use models of yesterday – or, indeed, general ideas derived from those models - to try disentangling nowadays issues. Inevitably under such conditions, one is at pain understanding facts. Our world has become a complex system, evolving according to trajectories which may suddenly change, and we still try to understand agricultural evolution by using, at the macroeconomic international level at least, more or less sophisticated versions of a mechanical supply and demand scheme explicitly elaborated as an approximation (see Walras’ assertion in front of this text). And Walras’ world was yet way simpler than ours! The image of the auctioneer illustrates how this approximation of markets functioning works: Everybody having revealed some given disposition to buy or to sell at a personally quoted price, the market maker (taken as an auctioneer) would only allow effective transactions once the price balancing supply and demand has been determined (the so-called tâtonnement process of economic theory, or the ‘fixing’ process in stock exchanges). This hypothesis and its natural extension of rational expectations4 rule out, for example, any multiplicity of market prices, any kind of volatility of prices in the long run, and simply assumes out many other important features of market structure (information, uncertainty, etc.) and of competitive behaviour (efforts, adaptive expectations, bounded rationality, etc.). That approximation can therefore help under certain conditions only. This paper shows that agriculture doesn’t meet these conditions and elaborates a hopefully more meaningful because more realistic model. Volatility appears then as a natural phenomenon we have to live with and to some extent tame.

It makes sense to think that meeting these conditions were widespread at the beginning of the XXth Century, but they are way less than universal in today’s world. Yet, many of today’s agricultural macroeconomic models appear as assuming that such conditions always describe our world. One can even say that a widespread modelling practice sees our world as having once become just like these conditions and elaborates reasoning on this exclusive basis. This, in our view, has been a deep source of counterproductive policies based on an epistemological mistake and in some cases simply historical pieces of nonsense. Agriculture is one of the latter cases. The Momagri world model aims at offering a possible solution to break this deadlock5.

Whatever the mathematical refinement of these concepts and tools of economics and econometrics, it does not bridge the gap for the complexity of our world and allow making predictions years ahead of commodity prices or outputs with some small interval error, like was done in the Fifties and Sixties. Such predictions of one of the widely used models in economic policy have proved to be consistently wrong (see figure 1).

Consistent mistakes in predicting American agricultural output using one of the standard models in use.

Figure 1. Consistent mistakes in predicting American agricultural output using one
of the standard models in use.

To be sure, there are impressive macroeconomic models of agriculture with thousands of equations on agricultural issues. Most of their conclusions are roughly that commodity prices – oil, cereals, fruits, meat, and many others – should gently fluctuate by a few percent around their respective equilibrium levels, if only we were wise enough to get rid of markets barriers of all sorts and make the world look closely like the perfect market of the theory. And no one could deny that tremendous efforts have been put to that end in the last thirty years on the basis of such traditional models. Yet, during all that period, all the agricultural commodity prices we’ve just mentioned have been wildly fluctuating – unlike, interestingly, most industrial and services prices: Figure 2 offers an example of this fact. In recent years, moreover, the range of fluctuations of these products has become dazzling. While many gurus believed, at the beginning of the last decade, cereals prices to be consistently and smoothly increasing, they were surprised by the prevalence of fluctuations of these very prices, and of non-cyclical fluctuations. The worst example has been the fall of these prices – in some cases by almost 60% - experienced between April and December, 2008. At that last date, the prices of these same products were seen as falling due to the impact of the financial and economic crisis. Yet, that crisis is far from over and the prices have been meanwhile up and down again

Differences in volatility between agricultural and industrial products.

Figure 2. Differences in volatility between agricultural and industrial products.
Source: Chadwick Investment Group (sugar), and Economagic (tomatoes, automobiles).

Such mistakes in predicting prices and in anticipating volatility are understandable when looking at the grounding of some widely known and used models. If, for instance, we consider the World Bank Model mainly used to ground such assertions as the gains in welfare expected from global deregulation, etc. the following observations can be made6 :

1) the word “risk” is not mentioned a single time throughout the 103 page-long technical document 2) the word “uncertainty” is mentioned only once, (p. 84) and said to be, in macroeconomics, “mainly linked to inflation” 3) the word “climate” does not exist either throughout the said document and 4) the word “expectation” is evoked only once (p. 4) to be immediately ruled out in the model, which explicitly and exclusively explains markets situations through population increase, technological progress, accumulation of capital and productivity changes. There is thus room, by design, for some gentle price variations, never for anything like observed volatility in prices. What it means, for policy making, is that volatility is a phenomenon of secondary importance for economic welfare. We think (fig. 2) that this may be true for many sectors in industry and the services, but not for agriculture and for some other markets, among which the financial market, oil, electricity, most primary commodities, real estate being in-between. We should, however, have learned our lesson after all the economic crises lived since the seventies and in particular the exuberance of some markets in the last decade...

If we consider the 45 page long paper on the ‘Mirage’ Model7, we can make similar observations: no effort is produced to introduce and model uncertainty in the behaviour of economic agents, even under the simplified form which economists call ‘risk’ (i.e. the case where uncertainty is measured by a single probability distribution, known by everybody, which is already restrictive). Be it for this reason or for some other one, the authors admit in their conclusion that, within their model “agriculture is modelled the same way as industry. Even if the model is not specifically designed to evaluate commercial agricultural policies, a more realistic description of that sector would be desirable”. Yet, they assert in the immediately following lines that economic modelling should call upon “well-identified and robust mechanisms” exclusively, leaving it to the users to interpret the results yielded by the model as to introduce the impact of non modelled variables: “the interpretation requires then an adjusted analysis, taking into account the set of problems which one focuses upon as well as the important mechanisms which are not considered in the model” (p. 137). But how should one do that ‘adjustment’? Do the authors care to explain which adjustment, on the basis of what?

This view of economic modelling is typical of a generation of economists and even, one could say, of the lost time of economic separation between nations. We cannot but offer a different view today. More specifically, we shall indeed argue here that the standard market model - in the sense where we described it at length here above – and its asserted adjustment is insufficiently relevant to present agricultural commodity markets, because it ignores the historical fact that agricultural commodity markets have become “financialized” ones, in the minimal sense where their respective outputs have come to be massively used as underlyings of financial operations. To be sure, this has been the case in the past during some periods and for some limited number of products of secondary significance, like coffee. But the scale and scope of the phenomenon have been considerably enlarged. This “financialization” has at least four consequences for economic modelling:
    1) It has turned these markets into expectations driven. It is the dynamic interplay between these social expectations and the natural-technical structure of the agro-systems in use which is now the bone of the rationale of the evolution of these markets, not anymore the mere confrontation in one shot of supply and demand according to a tâtonnement process as recalled above.

    2) A new category of players has appeared on these commodity markets, which has changed the type of behaviour of these markets. Indeed, short-term investors do not have a neutral role of self-cancelling intermediaries, so that they need to be modelled, be it only as a sort of Maxwell’s demons. There are many ongoing discussions as to whether speculators (an important subcategory of short term private investors) are leading the price game or not, but virtually everybody now credit them with some influence on these markets. Even researchers at IFPRI – who denied it until recently - have now started to acknowledge that there might be something to it8.

    3) Financialization also means connections with the financial markets properly said, as well as with monetary policy. Most researchers agree on some role for monetary policy on commodity prices, but few models have tried to include it in some meaningful way, and the leading models have not done it altogether.

    4) All markets have become more intensely interconnected at world level. This has at the same time stabilizing consequences when transportation costs are low and some kind of domino effect through financial transactions. More generally speaking, however, this means that policy making should look at the system as a whole, neither at a single market, nor at a few branches, but at a general interconnection at world level.

The present paper represents a new effort to tackle with these four issues, which we see as compelling in modelling agricultural markets. The model tries to offer an admissible solution to the issues raised above. It is organized as follows. Section 2 introduces the Momagri modelling approach, designed to give an account of volatility on commodity markets. Subsection will point to empirical facts justifying the approach. Section 3 sketches the impacts of volatility in terms of general economic welfare, briefly reminds that Europe’s CAP in its present state is not fit to cope with the issues involved and quotes possible steps to be taken. Section 4 concludes.


Pour télécharger « Momagri’s agricultural modelling approach », cliquez ici


The view presented above of agricultural commodity markets lead to revise a series of concepts or schemes of reasoning and call for adapted policy choices.
    1) Volatility doesn’t result anymore, since the late 1800’s, from the sole natural uncertainty, but is an endogenously produced and lasting phenomenon. We certainly can try to limit it, by adjusting market structures and specifying regulatory and fiscal policies within some bounds, we never will eliminate it altogether. So, we certainly should give some tools to individual farmers to cope with some of uncertainty. Forward markets should be maintained and short term investors as well, to maintain liquidity. But these markets should be organized and OTC’s versions should be kept to a minimum or altogether banned. Insurance schemes should be considered, along th experience of some countries.

    2) Most importantly, opening borders used to be a tool to decrease volatility in the XVIIIth Century, because uncertainty was then limited to an exogenous phenomenon, mainly linked to climate and other natural events, which are varying from one area to the other. Adding two statistically independent distributions yields a smaller variance and was a good way to decrease volatility. But opening international borders meant at that time having free markets for goods, not for factors of production (this was even the economic definition of a nation or of an integrated economic area). Today, factors move as well, but with very unequal speeds, capital being by far more mobile than labor - and labor than land, of course. We have forgotten about the lessons that Ragnar Nurkse wanted to remind us about in his “International Monetary Experience” Princeton leaflet. Opening borders means today heading also toward a completely free and deregulated short term capital movements world. This effect tends to increase volatility. In sum, the total effect of a full liberalization / opening of borders as understood today leads definitely to an increase in volatility, contrary to what many policy makers and/or politicians assert.

    3) In particular, we should be wary about financial innovations, mostly based on the illusory belief of perfect information and rationality of agents, but paradoxically equipped with dangerous technical approximations (like the VaR, etc.) and leading economic agents to irrational beliefs like the ‘disappearance of risk’ when spread over to the market! We have experienced the consequences of such unregulated financial innovations. We are experiencing the same today when worrying about countries debts.

    4) We should also change our way to assess welfare changes entailed by policies. Simply replacing equivalent monetary variations by expected equivalent monetary variations would be a mistake. For volatility also has an impact on the equivalent variations are computed, on one hand; and, on the other hand, volatility has an impact on investment and growth and on political instability, magnifying again this impact (Timmer 2002; Dawe 2001, 2010, Myers, 2006). Within a more limited volatility than today, the growth impact had been estimated between .5% and 1% yearly Domestic Income growth in developing countries, the bulk of it falling on producers rather than on consumers (Myers, 2006). Countries relying too much on resources from volatile commodities experience terms of trade volatilities which have a clearly adverse effect on investment and growth (Aghion et al., 2006). The question of choosing between stabilization and growth might be a biased question. Countries relying excessively on commodity revenues to grow experience high costs in terms of welfare due to shifts in physical and human capital investment away from non-commodity based sectors when commodity prices are high may be difficult to reverse when these prices are low (Guriev et al., 2009). Diversification per se is welfare improving in this sense.

    5) For similar reasons (in opposite directions), excess-dependence on commodities without real substitutes may be costly in terms of welfare. The optimal situation here depends highly on the level of international uncertainty, beyond price volatility. But it is important to note that there is also an aspect to food dependence strictly linked to efficiency and growth.

    6) In this last perspective as well as to curb excessive speculation, a more reasonable inventory policy than pursued in the last twenty years should be restored. We know that volatility of prices is quite sensitive to inventory levels and variations (Deaton and Laroque, 1992, etc.). To further curb excess speculation, price limits and everything that decrease speculative leverage (compulsory guarantee deposits, taxation schemes, etc. should be part of a consistent policy.


We thus have shown that volatility is a major aspect of commodity markets, more generally of financialized markets, and specifically of agriculture. The Momagri model does effectively integrate uncertainty and a dynamics of expectations, including short term investors, a dynamics which interacts with the state of the markets, at a world interconnection level. It is a modular and sequential model, which can be used policy wise for medium term issues. It can shed an important light on volatility and other important phenomena for about 10-20 years.

We also have now to worry about issues like climatic change and its impact on economic results and welfare, which pertain to the very long period (N. Stern, 2007). Traditional wisdom in this respect – based on the standard hypotheses evoked at the beginning of this paper- has been that the impact of volatility on economic results would decrease in the long run. Empirical research had confirmed the intuition on very long periods (200 years), calling upon the alleged phenomenon which economists call ‘mean reversion’ (around a trend, economic variables are bound to often return to the central tendency). J. Siegel has investigated the issue for the stocks market (....). But the world has changed: empirical economists tend today to lay the accent on the uncertainty about the trend itself, which finally leads to conclude that total resulting volatility in the long run increases. As we said in opening this paper, our world is a complex one and its trajectories follow each other in a way which is very difficult to predict. Unless we establish specific policies to limit it, volatility will become permanent.

The Momagri model is therefore an important tool of decision aid in agricultural policy. It can support simulations to evaluate price and income effects taking the volatility situation into consideration, which existing models hardly could do, for the reasons developed above. The model will therefore enable us to more thoroughly evaluate such or such scenario considered within the Doha negotiations.

Similarly, scenarios envisioned for the CAP will be more thoroughly appreciated to the extent that they can be evaluated within the volatility framework. The Momagri model provides Members of the European Parliament as well as officers in charge of elaborating the next CAP both a theoretical background and a simulation tool taking volatility effects into consideration.

Several features of the world deciphered by Momagri should be more particularly considered when redesigning the CAP, in particular (i) the increase of volatility entailed by a liberalization scheme, which would leave the way open to speculation, both in the short and in the long run (ii) the acute level which such an increase of volatility can reach in less developed countries experiencing hunger situations and massive rural migration to big cities (iii) the severe impact it also can have in Europe on some sectors suffering difficulties like milk or cereals (iv) the connection of volatility with food security already sketched above and all the costs precautions in this respect can help to avoid (v) the necessity to define some optimum in borders opening as explained above, in order to prevent every return to protectionism, which might trigger similarly negative - if not worse - effects.

Momagri, on the ground of the model presented above and beyond it, is elaborating a project of markets organization and regulation designed to contain the abovementioned impacts as well as the crises they might trigger. Such a policy should be pursued under constant budgetary spending, which calls for designing renewed and modernized principles of government intervention. Such a project might be presented to European Institutions in the months to come.

It is crucial that the CAP to come be (i) adjusted to the specificity of the agricultural sector but also (ii) able to cope with present and future challenges in terms of price volatility limitation as well as (iii) with challenges offered by issues of natural and markets risk management.


P. Aghion, P. Bacchetta, R. Ranciere and K. Rogoff (2006), “Exchange rate volatility and productivity growth: the role of financial development”, CEPR Discussion Paper 5629.

Allais, M., 1972, “Forgetfulness and Interest”, Journal of Money, Credit and Banking, 4, 1, 40-73.

Armington, P., 1969, "A Theory of Demand for Products Distinguished by Place of Production", IMF Staff Papers, 16, 159-178.

Arrow, K.J., and M. Nerlove, 1958, “A Note on Expectations and Stability”, Econometrica, 26 , 297-305.

Bandara, J. S., 1991, "Computable General Equilibrium Models for Development Policy Analysis in LDCs" Journal of Economic Surveys, 5.

Brock, W.A. and C.H. Hommes, 1997, “A Rational Route to Randomness”, Econometrica, 65, 1059-1095.

Boussard, J-M., 1996, “When Risk Generates Chaos”, Journal of Economic Behavior and Organization, 29, 433-446.

Bouzit, A.M., and G. Gleyzes, 1997, “Empirical Estimation of RDEU Preference Functionals in Agricultural production, in: Huirne, R. B. M; and al. (eds.), Risk Management Strategies in Agriculture, State of the Art and Future Perspectives, EUNITA, Mansholt Studies, Mansholt Institute, Wageningen. Distributed by Backhuys Publishers, Leiden.

Brock, W.A. and C.H. Hommes, 1998, “Heteregenous Beliefs and Rational Routes to Chaos in a Simple Asset Pricing Model”, Journal of Economic Dynamics and Control, 22, 1235-1274.

Chatrath, A., A. Bahram, and K. Dhanda, 2002, “Are Commodity Prices Chaotic?”, Agricultural Economics, 27, 123-137.

Deaton, A. and G. Laroque, 1992, “On the Behavior of Commodity Prices,” Review of Economic Studies, 59, 123.

Drouhin, N., B. Munier and M. Trommetter, 2006, L’organisation des échanges internationaux agricoles en perspective, MOMAGRI report, February, Mimeo. 44 p.

Frankel, J. and K. Froot, 1986, “Understanding the U.S. Dollars in the Eighties: The Expectations of Chartists and Fundamentalists”, Economic record, 62, 24-38.

Grandmont, J-M., 1998, “Expectations Formation and Stability in Large Socioeconomic Systems”, Econometrica, 66, 741-781.

Guriev, S., A. Plekhanov and K. Sonin, 2009, “Developement based on Commodity Revenues”, EBRD working papers , n° 108.

Hommes, C.H., 1998, “On the Consistency of Backward-Looking Expectations: The Case of the Cobweb”, Journal of Economic Behavior and Organization, 33, 333-362.

Just, R. E., 2001, “Addressing the Changing nature of Uncertainty in Agriculture”, American Journal of Agricultural Economics, 83, 1131-1153.

Kirman, A., 1991, “Epidemics of Opinion and Speculative Bubbles in financial Markets”, in: M. Taylor (ed.), Money and Financial Markets, Oxford, Blackwell, 354-368.

Leroy-Ladurie, E., 2004, Histoire humaine et comparée du climat, Paris, Fayard. Vol.I, II and III (2009).

Lesourne, J., 1991, Economie de l’ordre et du désordre, Paris, Economica.

Maunder, W.J., 1989, The Human Impact of Climate Uncertainty,Weather Information, Economic Planning and Business Management, London and New York, Routledge.

Munier, B., 1991, “The Many Other Allais’ Paradoxes”, Journal of Economic Perspectives, 5, 179-199.

Munier, B., 1995, “Fifty years of Maurice Allais’ Economic Writings: Seeds for Renewal in Contemporary Economic Thought”, in: B. Munier (Ed.), Markets, Risk and Money, Dordrecht/Boston, Kluwer Acad. Publ.

Nerlove, M., « Adaptive Expectations and Cobweb Phenomena », Quarterly journal of Economics, LXXII, 227-240.

Newbery, D.M.G. and J. E. Stiglitz, 1981, The Theory of Commodity Price Stabilization, Oxford, Oxford University Press.

Routledge, B., D. Seppi, and C. Spatt, 2000, “Equilibrium Forward Curves for Commodities,” Journal of Finance, 55, 1297-1338.

Serrão, A. and L. Coelho, 2004, “Cumulative prospect theory applied to farmers’ behavior in the Alentejo dryland area of Portugal”, FUR XI Conference, GRID, Cachan, France.

Stein, J., 1987, “Informational Externalities and Welfare Reducing Speculation”, Journal of Political Economy, 95, 1123-1145.

Walras, L., Eléments d’Economie Politique Pure, Paris, Pichon et Durand-Auzias, 1926 (Original edition, 1874). We refer here to : Dockès et al., Œuvres Complètes d’Auguste et Léon Walras, Paris, Economica, 1988, Band VIII.

Westerhoff, F., 2003, “Speculative markets and the Effectivenes of price limits”, Journal of Economic Dynamics and Control, 28, 493-508.

1 NB : Le document a été rédigé en anglais. Une version française officielle est en train d’être traduite par les services du Parlement européen, et sera disponible en septembre.
2 « L’état d’équilibre de la production est comme l’état d’équilibre de l’échange, un état idéal et non réel. Il n’arrive jamais […] », in : L. Walras, Eléments d’Economie Politique Pure, Paris, Economica. Edition by the « Centre Auguste et Léon Walras », 1988. 18è me Leçon, §188, p. 283.
3 Friedman, D., 2007, “Markets theories evolve, and so do markets”, Journal of Economic Behavior and Organization, 63, 247-255.
4 Under the rational expectations hypothesis, economic agent’s anticipations at each period match the exact equilibrium price of the next period, or a least the average on a small interval of the potential values of this values of this equilibrium price. Under such a hypothesis, speculators making profits necessarily stabilize prices. This is the “stabilizing speculation” view, which is, again, relevant under certain conditions only.
5 When Momagri officers came to ask me for participation and coordination of a new world model, I first tended to decline the invitation as beyond my competencies. But I soon realized that something was to be done in this direction. We started to look at existing models and their technical features: We were appalled. Not only were the links to environment and innovation missing in most cases, not to speak of the very notion of risk itself and of any idea of expectation, but the models were grounded on the set of hypotheses already evoked, overly restrictive for agriculture. And this has been all the more frightening because such model are used without their builders warning politicians and indeed policy advisors of the hypotheses made. We decided to take up the challenge and try providing a more appropriate tool to policymakers wherever they may be: this was the origin of the Momagri model, back in early 2006, focusing on the volatility issue. A first expression of our results is appearing in :Munier, B., 2010,“Boundedly Rational Exuberance on Agricultural Commodity Markets”, Risk and Decision Analysis, forthcoming. See also the interesting Comments by Semmler and Bernard there (forthcoming).
6 Van der Mensbrugghe, D., 2005, LINKAGE Technical Reference Document, Version 6.0, Draft dated Dec. 22, 2005, Mimeo, The World Bank.
7 Bchir, M.H., Y. Decreux, J-L. Gurin et S. Jean, 2002, « MIRAGE, un modèle d’équilibre général calculable pour l’évaluation des politiques commerciales », Economie Internationale, 89-90, 109-153.
8 Robles, M., M. Torero and J. von Braun, 2009, “When speculation matters”, IFPRI Issue Brief 57, Washington
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