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The purpose of this paper is to examine the potential gains in hedge ratio calculation for agricultural commodities by incorporating market linkages and prices of related commodities into the hedge ratio estimation process.

A vector autoregressive multivariate generalized autoregressive conditional heteroskedasticity (VAR‐MGARCH) model is used to construct a time‐varying correlation matrix for commodity prices across linked markets and across linked commodities. The MGARCH model is estimated using a two‐step approach, which allows for a large system of related prices to be estimated.

In‐sample and out‐of‐sample portfolio variance comparison among no hedge, bivariate GARCH, and MGARCH models indicates that hedge ratios estimated using the MGARCH approach reduce agricultural producers' and commercial consumers' risks in futures market participation.

The application is limited to an examination of Montana wheat markets.

Agricultural producers who use futures markets to reduce market risk will have a better method for determining hedging positions, because MGARCH estimated hedge ratios incorporate more information than hedge ratios estimated using existing practices.

Portfolio variance reduction is analogous to utility improvement for agricultural producers. More efficient hedging strategies can lead to better implementation of futures markets and increased social welfare.

This research substantially extends current literature on agricultural hedge strategies by illustrating the advantages of using an hedge ratio estimation approach that incorporates important information about prices at linked markets and prices of other commodities. Providing evidence that market portfolio variance can be lowered using the multivariate estimation approach, the research offers commercial agricultural producers and consumers a practical tool for improving futures market strategies.

The aim of this paper is to show how provisions of the Supplemental Revenue Assistance Payments (SURE) program impacts production practices, and empirically examine changes in crop insurance participation rates as a means of measuring producer responses to the program.

The structure of the SURE program is described and a stylized theoretical model is used to show the SURE program's effects on farm‐level crop insurance and production decisions. A county‐level cross‐sectional empirical specification with regional fixed effects is used to test the hypothesis that producers who are most likely to benefit from production practice re‐optimization are more likely to participate in crop insurance.

Results from empirical analyses of corn, soybean, and wheat production areas show that the SURE program has had substantial impacts on crop insurance participation by producers who are more likely to receive SURE indemnities and exploit moral hazard opportunities.

Because the program has only recently been introduced, empirical estimates of the program's long‐run impacts are not estimable.

Results indicate that the program can have unexpected market consequences, with increased frequency and size of SURE indemnity claims than the Congressional Budget Office anticipated and increases in aggregate tax payer subsidies for both the crop insurance and SURE program. These outcomes can have important implications on motivating a restructuring of the program in the next farm bill.

Increased tax payer expenditures on the SURE and crop insurance programs in the form of subsidies can lead to non‐trivial reductions in social welfare.

This research is the first to develop a rigorous model of the SURE program's impacts on producer responses and associated effects on crop insurance participation. The study also provides empirical evidence of these effects.

For over 20 years, decoupled agricultural support programs have played a large role in farm policy. The purpose of this paper is to investigate the effects of decoupled agricultural support payments on farm-level debt.

A two-stage least squares model is used to estimate the impact of decoupled payments, farm production characteristics, demographics, regional risk factors, as well as cross-sectional and temporal fixed effects on farm debt, along with weather-related instrumental variables.

Results indicate that a negative and statistically significant relationship exists between decoupled payments and farm debt. This study also provides evidence that research results not accounting for the endogenous relationship between acres operated and farm-level debt should be interpreted with caution.

The constantly changing sets of policy options provide a challenge in identifying the impact of a single policy, ceteris paribus. Therefore, one notable limitation is in extrapolating the results in this study to make implications on the elimination of the direct payments program, as part of the 2014 Farm Bill.

This implies that farmers likely use annual decoupled payments to reduce their debt, potentially influencing their exposure to financial risks, capacity to withstand financial instability, and access to credit. The methodology used may establish a foundation for continued research that seeks to empirically identify and measure the complex interrelationships among agricultural public policies and farm-level financial measures.

Decoupled payment programs may indirectly influence debt decisions, which can influence production decisions in the long run.

In order to accurately identify the impact of direct payment programs on farm debt levels, this study is the first of its kind to account for the endogenous relationship between production decisions and debt and use a large unbalanced panel of data available from the Agricultural Resource Management Survey.