A novel forecasting model for the Baltic dry index utilizing optimal squeezing

Abstract

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 Baltic Dry Index with considerable success.

Information Search and Financial Markets under COVID-19

“…Third, given that financial markets are tightly intertwined with global economic activities, we also added the global economic activity measured by the Baltic Exchange Dry Index (BDI) as an additional endogenous variable in Equation ( 2 ). The BDI, published daily by the Baltic Exchange in London, effectively represents the changes in the global real activity and has been used extensively in the recent literature [ 29 , 30 , 31 ]. Fourth, for US financial markets, we also performed analysis with US-based Google search data, instead of global Google search data (see Figure A6 and Figure A7 in the Appendix A ).…”

“…With increasing importance of online search and social platforms, publicly available data from platforms such as Google, Twitter, and Facebook proves indispensable in empirical research as it allows the analyst to tap micro-level data to form economic and psychological variables, such as preference and risk perception that enable the analyst to study various economic implications of these variables during uncertain times, such as during a pandemic [ 8 ]. This is clearly the growing trend in the literature [ 10 , 31 ], with fruitful applications in economics and finance.…”

Spillovers of the COVID-19 Pandemic: Impact on Global Economic Activity, the Stock Market, and the Energy Sector

“…The daily global COVID-19 cases were taken as a proxy for the COVID-19 pandemic. On the other hand, Baltic Exchange dry index (BDI) 7 was chosen as a proxy for global economic activities, since the real global activities are reflected in the BDI (Kilian 2009;Fan and Xu 2011;Qiu et al 2012;Makridakis et al 2020;Yilmazkuday 2020). The MSCI world index (MSCI) 8 was chosen to represent world financial market activities.…”

Risk perception and oil and gasoline markets under COVID-19

“…(2) . The BDI, published daily by the Baltic Exchange in London, effectively represents the changes in the global real activity and has been used extensively in the recent literature ( Fan & Xu, 2011 ; Kilian, 2009 ; Makridakis, Merikas, Merika, Tsionas, & Izzeldin, 2020 ). Last but not least, on March 8, 2020, Saudi Arabia initiated a crude price war with Russia (commonly known as the Russia–Saudi Arabia oil price war of 2020) by discounting its crude and raising production, which crashed oil prices by as much as 30% ( Raval & Sheppard, 2020 ).…”