Specification Analysis for Regime Switching Models in Financial Markets

Volatile commodities and markets can often be difficult to model and forecast given significant breaks in trends through time. To account for such breaks, regime switching methods allow for models to accommodate abrupt changes in behavior of the data. However, the difficulty often arises in beginning the process of choosing a model and its associated parameters with which to represent the data and the objects of interest. To improve model selection for these volatile markets, this research uniquely applies Bayesian specification analysis with regime switching models and argues that such synthesis ameliorates financial modeling. Using spot prices and futures from dairy markets as the chief data of interest, the integration of these methods will assist in better modeling past data and thereby improve forecasting as well