Specification Analysis for Regime Switching Models in Financial Markets
Class
Article
Graduation Year
2017
College
College of Science
Department
Mathematics and Statistics Department
Faculty Mentor
Dr. Tyler J Brough
Presentation Type
Poster Presentation
Abstract
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.
Location
South Atrium
Start Date
4-13-2017 1:30 PM
End Date
4-13-2017 2:45 PM
Specification Analysis for Regime Switching Models in Financial Markets
South Atrium
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.