The Impact of Stocks on Correlations for Commodities using Semi Parametric Quantile Regression

Music City Center 

Crop yields and harvest prices are often considered to be negatively correlated, thus acting as a natural risk management hedge through stabilizing revenues. Storage theory gives reason to believe that the correlation is an increasing function of stocks carried over from previous years. In this paper, we use semi-parametric quantile regression (SQR) with penalized B-splines to estimate a stock-conditioned joint distribution of yield and price. The method enables sampling from the empirical joint distribution using SQR. Then it is applied to approximate the stock-conditioned correlation for both corn and soybeans in the United States. For both crops, Cornbelt core regions have more negative correlations than do peripheral regions. We find strong evidence that correlation becomes less negative as stocks increase and also upon moving north. We suggest three channels through which stocks can predict revenue. The first two channels are currently addressed in premium rate-setting procedures. The third is not and we provide yield auto-correlation evidence to suggest that this could be a concern. We conduct a rating game to evaluate our methodology for assessing premium rates.

Crop Insurance

Quantile Regression

Agricultural Economics

Price-yield correlation

Insurance premium rate setting 

Business and Economic Statistics Section