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    Volatility Forecasting in Agricultural Commodity Markets 

    AthanasiosTriantafyllou a , George Dotsis

    b , Alexandros H. Sarris


    This version: 17/12/2013


    In this paper we empirically examine the information content of model-free option implied

    moments in wheat, maize and soybeans derivative markets. We find that option-implied

    risk-neutral variance outperforms historical variance as a predictor of future realized

    variance. In addition, we find that risk-neutral option implied skewness significantly

    improves variance forecasting when added in the information variable set. Variance risk

    premia add significant predictive power when included as an additional factor for

    predicting future commodity returns.

    Key words: Risk neutral moments, Variance Risk Premia, Agricultural Commodities

    JEL classification: G10, G12, Q14

    a PhD candidate, Department of Economics, University of Athens, triant.ath@gmail.com

    b Corresponding author, Lecturer in Finance, Department of Economics, University of Athens, 5 StadiouStr,

    Office 213, Athens, 10562, Greece, tel: +30 2103689373, gdotsis@econ.uoa.gr c Professor of Economics, Department of Economics, University of Athens.aleko@alum.mit.edu,


    mailto:triant.ath@gmail.com mailto:gdotsis@econ.uoa.gr mailto:aleko@alum.mit.edu mailto:alekosar@otenet.gr

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    The period since 2006 has seen considerable instability in global agricultural markets.

    Between September 2006 and February 2008, world agricultural commodity prices rose by

    an average of 70 percent in nominal dollar terms, with prices in some products rising by

    much more than that. The strongest price rises were observed in wheat, maize, rice, and

    dairy products. Prices fell sharply in the second half of 2008, although in almost all cases

    they remained above the levels of the period just before the sharp increase in prices started.

    In 2010 sharp price rises of food commodity prices were observed again, and by early

    2011, the FAO food commodity price index was again at the level reached at the peak of

    the price spike of 2008. In 2011 and 2012 prices fell again and then rose again considerably

    in early 2013. In other words within the past six years many food commodity prices

    increased very sharply, subsequently declined equally sharply, and then again increased

    rapidly to reach the earlier peaks. Such rather unprecedented variability or volatility in

    world prices creates much uncertainty and risks for all market participants, and makes both

    short and longer term planning very difficult. A major issue, therefore, is whether and how

    agricultural price volatility can be predicted. The purpose of this paper is to assess some

    existing methods for predicting agricultural price volatility, examine their validity during a

    market upheaval, like the recent one, and discuss possible improvements.

    Staple food commodity price volatility, and in particular sudden and unpredictable price

    spikes, create considerable food security concerns, especially among those, individuals or

    countries, who are staple food dependent and net buyers. These concerns range from

    possible inability to afford increased costs of basic food consumption requirements, to

    concerns about adequate supplies, irrespective of price. Exporters or net sellers are also

    affected by agricultural commodity price volatility, as they may not be able to appropriately

    plan sales over time, and hence may lose profits. Unpredictability is a fact of life for any

    actor who is involved in agricultural commodity markets, and there are a variety of risk

    management practices that have been developed by these actors to deal with such lack of

    certainty, such as stockholding, advance purchasing or selling, long term contracting, etc.

    All of these practices depend explicitly or implicitly on an assessment of the degree of

    future market uncertainty. Sudden changes in market fundamentals, that may change the

    assessment of future market uncertainty, tend to upset existing risk management practices,

    and can be very costly for market participants. For instance if traders estimate that the

    future market price maybe much more uncertain or variable than what they are used to,

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    they may try to hold more inventories. Such behavior in the aggregate may exacerbate price

    spikes, and is present in all cases of sudden market upheavals. Hence it is important for

    these actors to have a way to assess the degree of future market unpredictability.

    There are two concepts of price volatility that have been discussed in the literature. The

    first one is historic volatility. This is an ex-post concept, and refers to observed variations

    of market prices from period to period. It is normally computed as the standard deviation of

    the logarithmic return of prices over a given period of time multiplied by the square root of

    the frequency of observations. However, the principal concern of market participants and

    policy makers alike is not large ex-post variations in past observed prices per se, but large

    shifts in the degree of unpredictability or uncertainty of subsequent prices. This notion, at

    any one time, refers to the conditional probability distribution of the prices, given current

    information. Such a concept cannot be readily and objectively quantified, as there are no

    corresponding market variables. It can only be inferred from observed market variables

    through some appropriate model. One relatively objective measure of unpredictability is

    “implied volatility”, which is a measure of the market estimate of the ex-ante or conditional

    variance of subsequent price, based on current observations of values of options on futures

    prices in organized exchanges, and using the Black-Scholes (1973) model for the


    Estimates based on the two concepts may point in different directions, depending on data

    and time period. For instance illustrations in Prakash (2011b) indicate estimates over forty

    years, of realized volatilities of cereals, based on observed spot prices in major international

    markets, such as the Gulf (as compiled by FAO), which exhibit mild upward trends.

    However, estimates of implied volatilities of some of the same cereal prices, as inferred

    from option prices in the major exchange trading these derivative instruments, namely the

    Chicago Mercantile Exchange (CME), exhibit strong upward trends over the last twenty

    years, when such instruments have been traded. This suggests that there maybe different

    determinants of the ex-post and the ex-ante volatilities of food commodities.

    During the commodity and credit crisis of 2008, observed as well as implied volatility in

    food and agricultural prices increased dramatically, causing widespread concern about a

    major shift in global agricultural markets (for relevant analyses and policy concerns see

    Prakash, 2011a, Headey and Fan, 2010, Sarris, 2011, FAO, et. al, 2011). The concerns

    arose because basic agricultural food commodities like wheat, maize and soybeans cover to

    a large extent the basic nutrition needs of many countries, especially many Low Income

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    Food Deficit Countries (LIFDC’s). Any method which has the ability to somehow foresee

    the future price variability of these commodities is of crucial importance for market

    participants and policymakers.

    Concerning predictability of agricultural commodity market volatility, Giot (2003) finds

    that for cocoa, sugar and coffee future contracts, implied volatility derived from the Black

    and Scholes (1973) (BS) model predicts more efficiently future volatility compared to

    historical volatility measures or GARCH models. Manfredo and Sanders (2004) examine

    the predictive ability of option implied volatility in live cattle futures contracts and Simon

    (2003) examines the predictive ability of option implied volatility in corn, wheat and

    soybeans futures contracts. Both studies show that option based implied volatility has

    substantial predictive power for subsequent realized volatility. Wang Fausti and Qasmi

    (2012) estimate model-free option implied variance in the maize market. They find that the

    model-free variance is a more effective estimator of future variance, compared to backward

    looking methods of estimating future variance (via the family of ARCH-GARCH models)

    or forward looking option implied volatility methods based on Black’s (1976) model. 1

    Our contribution to the literature is threefold. First, extending the approach of Wang Fausti

    and Qasmi (2012), we also examine the information content of model-free option implied

    skewness of agricultural commodity markets. The risk-neutral skewness captures the slope

    of the implied volatility curve 2 and many studies that examine individual stocks or stock

    index returns have shown that skewness contains useful information. For example,

    Rompolis and Tzavalis (2010) show that option implied skewness


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