Measuring Liquidity Costs in Agricultural Futures Markets

Estimation of liquidity costs in agricultural futures markets is challenging because bid-askspreads are usually not observed. Spread estimators that use transaction data are available,but little agreement exists on their relative accuracy and performance. We evaluate fourconventional and a recently proposed Bayesian estimators using simulated data based onRoll’s standard liquidity cost model. The Bayesian estimator tracks Roll’s model relativelywell except when the level of noise in the market is large. We derive an improved estimatorthat seems to have a higher performance even under high levels of noise which is common inagricultural futures markets. We also compute liquidity costs using data for hogs and cattlefutures contracts trading on the Chicago Mercantile Exchange. The results obtained formarket data are in line with the findings using simulated data.