Statistical time series analysis is a powerful method in characterizing
the dynamics of variables in forward contract markets. Modeling
univariate and multivariate time series variables will enable the
modeler to build forecasting models. Variables in a given forward
contract market (for example: basis, volume and weeks-to-expiration) can
have causal relationship with each other and with their own lagged
values. Variables with significant Granger causality are modeled using
vector autoregressive processes, while variables with insignificant
Granger causality are modeled using autoregressive and moving average
processes. Basis and volume of forward contracted cattle in the United
States exhibit behaviors pertinent to seasonal changes. In this thesis,
we analyzed weekly data on basis, volume and weeks-to-expiration of
forward contracted cattle in the United States. We developed monthly
forecasting models for basis and volume contracted that can be utilized
by farmers and policy makers.