Hedging with Options under Variance Uncertainty: An Illustration of Pricing New‐Crop Soybeans

Hauser, Robert J.; Andersen, Dane K.

doi:10.2307/1241304

Cited by 11 publications

(5 citation statements)

References 20 publications

(1 reference statement)

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“…Expected price variance is thus assumed to be seasonal. The seasonality of price variance has been empirically demonstrated on the futures markets of agricultural commodities (Hauser and Andersen, 1987) but is yet to receive attention on spot markets. It should be noted that the model was also formulated using alternate variance expectation schemes, for instance where the expected price is derived from a naive scheme.…”

Section: Introductionmentioning

confidence: 99%

A simple model of endogenous agricultural commodity price fluctuations with storage

Mitra

Boussard²

2011

Agricultural Economics

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A debate has been raging for centuries regarding the effects of interannual storage on commodity prices. Most analysts consider storage to function as a price stabilizer, while others place it at the core of an explanation of intriguing features of commodity price series, such as skewed distributions. Most studies have been developed in the context of the theory of competitive storage where random shocks affect supply or demand. Recently, the endogenous chaotic behavior of markets has become another possible hypothesis regarding the origin of commodity price fluctuations. We develop a nonlinear cobweb model with intra-and interannual storage, risk averse agents, and adaptive expectations. Like the theory of competitive storage, this nonlinear cobweb model with storage can reproduce some of the stylized facts of agricultural commodity prices (autocorrelation of first rank, low kurtosis, and skewness). In addition, the effects of storage on price variation are mixed. In the presence of interannual storage, chaotic price series show less variation compared to a situation without interannual storage but we find that storage contributes to the endogenous volatility of prices by making chaotic dynamics more likely.JEL classification: D84, E39, Q11

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Section: Introductionmentioning

confidence: 99%

A simple model of endogenous agricultural commodity price fluctuations with storage

Mitra

Boussard²

2011

Agricultural Economics

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“…However, these solutions are usually expressed in fractions of contracts purchased or sold, and do not reflect the restriction implied by futures and options contracts of fixed sizes. This is especially important in analyzing models containing both futures and options, since options, with their selection of strike prices, can mitigate the effects of fixed futures contract sizes [Hauser and Andersen (1987)…”

Section: Empirical Specificationmentioning

confidence: 99%

Robust live hog pricing strategies under uncertain prices and risk preferences

Adam

Garcia

Hauser

1993

Journal of Futures Markets

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roducers of agricultural commodities are faced with numerous and complex pricing P decisions. The number of pricing alternatives has been greatly expanded with the introduction of options on agricultural futures. The choice of appropriate marketing strategies is further complicated by the uncertain and changing nature of agricultural prices and differences in producers' risk preferences. The best pricing alternative is often sensitive to the producer's expectation of prices and preference for risk.In this context, it is useful to consider the "robustness" of alternative marketing strategies to the changing distribution of ending prices and to the level of risk that producers are willing to accept. Identifying strategies which provide favorable returns over a range of final distributions of cash and futures prices across different risk preferences can provide producers simpler, more effective marketing alternatives. Similarly, it may permit insight into the development of more general producer marketing strategies that will be effective even under changing market conditions [Babb et al. (1985)l.The objective of this article is to determine the importance of producer price expectations and risk preferences in selecting optimal marketing strategies, and to identify strategies that are relatively robust to differences in producer's attitudes toward risk and expectations about prices. Using a two-period simulation model with parameters appropriate for a hog producer, the choice of marketing strategies is analyzed. Changes in ex ante utility are used to assess the sensitivity of marketing strategies to different scenarios of the distribution of prices and to producer risk preferences.An emphasis of the analysis is to identify "near-optimal" hedging strategies. Sets of strategies that achieve near-optimal utility-a measure of utility in a neighborhood around the utility maximizing point-for a range of price scenarios are identified as robust marketing strategies. In addition, strategies that are robust to differences in producers' levels of risk aversion are identified. Robust strategies are compared to strategies commonly recommended by market advisors.

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“…Anderson (1985) introduced the state-variable concept and showed that seasonal effects are of greater importance than the maturity effect. Subsequently, Hauser and Neff (1985), Hauser and Andersen (1987), and Kenyon, Kling, Jordan, Seale, and McCabe (1987) each analyzed the relationship between futures price volatility and fundamental economic phenomenon. Wilson, Fung, and Ricks (1988) recently analyzed selected agricultural option premiums and made comparisons to those estimated from the Black pricing model.…”

Section: Previous Studiesmentioning

confidence: 99%

“…Black (1976) derived the model by assuming a constant variance rate in the diffusion process. In the commodity futures market, variance has been shown to be not constant due to maturity, seasonal effects, and option market fundamentals (see Samuelson (1965), Anderson (1985), Milonas, (1986, Hauser and Andersen (1987), and Kenyon, Kling, Jordan, Seale, and McCabe (1987). However, variance of the futures price can be allowed to change as a function of time (see Merton (1973), Kolb and Gay (1985)).…”

Section: Empirical Proceduresmentioning

confidence: 99%

Information content of volatilities implied by option premiums in grain futures markets

Wilson¹,

Fung²

1990

Journal of Futures Markets

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Hedging with Options under Variance Uncertainty: An Illustration of Pricing New‐Crop Soybeans

Cited by 11 publications

References 20 publications

A simple model of endogenous agricultural commodity price fluctuations with storage

A simple model of endogenous agricultural commodity price fluctuations with storage

Robust live hog pricing strategies under uncertain prices and risk preferences

Information content of volatilities implied by option premiums in grain futures markets

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