Forecasting the nearby basis of live cattle

Liu, Shi‐Miin; Brorsen, B. Wade; Oellermann, Charles M.; Farris, Apul L.

doi:10.1002/fut.3990140303

Cited by 22 publications

(20 citation statements)

References 18 publications

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“…They further found that flexibility in marketing decisions is another factor responsible for temporal correlation among futures prices of livestock futures, in addition to the common factor of feed prices suggested by Kendall and Scobie (1981). Liu, Brorsen, Oellermann, and Farris (1994) extended Naik and Leuthold's work (1988) to develop four models to forecast live cattle basis during the month preceding contract delivery. The four models are: (1) the futures market variable model, which is a function of lagged spread and lagged open interests; (2) the lagged basis model that depends upon lagged basis and delivery cost; (3) the economic model, which is a function of demand and supply factors and delivery cost; and (4) the joint model, which includes all explanatory variables in the previous three models.…”

Section: Literature Reviewmentioning

confidence: 96%

“…Following the work of Naik and Leuthold (1988) and Liu et al (1994), Garcia and Sanders (1996) evaluated the forecasting performance of the live-hog nearby weekly basis by both economic forecasting models and ARIMA models. They found that basis risk (measured in terms of root mean square error), during the out of the sample period, has not increased in comparison with historical standards.…”

Section: Literature Reviewmentioning

confidence: 99%

“…This variable was used by Naik and Leuthold (1988), Liu et al (1994), and Garcia and Sanders (1996) to capture the temporal relationship among bases.…”

mentioning

confidence: 99%

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Contract modifications and the basis behavior of live cattle futures

Newsome

Wang

Boyd

et al. 2004

Journal of Futures Markets

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1Purcell and Hudson (1985) present an example using expected basis and futures prices to form a forecast for expected future spot price (see pp. 337-338). Within a regression model with heteroskedascity error framework, we found that the level of nearby basis in the period after June 1995 has shifted lower and the average monthly open interest of net commercial long positions has substantially increased after the contract modifications. These empirical results are consistent with the notion that more long activity entered the market in response to the contract modifications. Additionally, an alternative (new) measure of basis calculation (cash price minus weighted mean futures price) produced similar results to two other commonly used measures. In conclusion, the 1995 contract changes have neither increased nor decreased the volatility of live cattle basis.

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Section: Literature Reviewmentioning

confidence: 96%

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Contract modifications and the basis behavior of live cattle futures

Newsome

Wang

Boyd

et al. 2004

Journal of Futures Markets

View full text Add to dashboard Cite

show abstract

“…Some basis forecasts use naïve models; the expected basis equals last year's basis. Econometric models have been developed to explain basis variability (e.g., Hauser, Garcia, & Tublin, 1990;Liu, Brorsen, Oellermann, & Farris, 1994;Ward & Dasse, 1977). However, models that provide good ex-post explanations of basis behavior may be difficult to use for forecasting because it is difficult to make good ex-ante estimates (ancillary forecasts) of the explanatory variables (Taylor & Tomek, 1984).…”

Section: Futures Pricesmentioning

confidence: 99%

Risk Management in Agricultural Markets: A Review

Tomek

Peterson

2001

Journal of Futures Markets

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This article surveys and evaluates the current state of knowledge about producers' marketing strategies to manage price and revenue risk for farm commodities. The review highlights gaps between concepts and their implementation. Many well-developed models of price behavior exist, but appropriate characterization and estimation of the probability distributions of commodity prices remain elusive. Hence, the preferred measure of price risk is ambiguous. Numerous models of optimal marketing portfolios for farmers have been specified, but their behavior appears to be inconsistent with most, if not all, of these models. In addition, some research suggests that farmers can earn speculative profits, which is inconsistent with notions of efficient markets. The conclusions discuss what academic research can and cannot accomplish in relation to assisting producers with risk-management decisions.

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“…In a recent article, Netz (1996) shows that basis risk not only affects the futures position but also the cash-market position for all hedging by risk-averse agents. Numerous articles provide statistical models for predicting the basis (Naik and Leuthold, 1988;Trapp and Eilrich, 1991;Liu et al, 1994), although researchers find it difficult to forecast.…”

Section: Basis Riskmentioning

confidence: 99%