A novel forecasting model for the Baltic dry index utilizing optimal squeezing
| Autor: | Marwan Izzeldin, Anna Merika, Andreas G. Merikas, Mike G. Tsionas, Spyros Makridakis |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
050208 finance
Time path Computer science Strategy and Management 05 social sciences Bayesian probability Management Science and Operations Research Baltic dry index Regression Computer Science Applications Set (abstract data type) Random search Market risk Order (exchange) Modeling and Simulation 0502 economics and business Econometrics 050207 economics Statistics Probability and Uncertainty |
| Zdroj: | Journal of Forecasting. 39:56-68 |
| ISSN: | 1099-131X 0277-6693 |
| Popis: | Marine transport has grown rapidly as the result of globalization and sustainable world growth rates. Shipping market risks and uncertainty have also grown and need to be mitigated with the development of a more reliable procedure to predict changes in freight rates. In this paper, we propose a new forecasting model and apply it to the Baltic Dry Index (BDI). Such a model compresses, in an optimal way, information from the past in order to predict freight rates. To develop the forecasting model, we deploy a basic set of predictors, add lags of the BDI and introduce additional variables, in applying Bayesian compressed regression (BCR), with two important innovations. First, we include transition functions in the predictive set to capture both smooth and abrupt changes in the time path of BDI; second, we do not estimate the parameters of the transition functions, but rather embed them in the random search procedure inherent in BCR. This allows all coefficients to evolve in a time-varying manner, while searching for the best predictors within the historical set of data. The new procedures predict the BDI with considerable success. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Plný text