The dynamic analysis of Baltic exchange dry index

Abstract

Baltic Exchange Dry Index (BDI) is an independent response of maritime market information for the trading and settlement of physical and derivative contracts. In this paper, we propose fuzzy set theory and grey system for modeling the prediction of BDI, and employ the ARIMA model for the calibration of the data structure to depict the trend. The empirical results indicate that for both short-term and long-term BDI data, the fuzzy time series model has the lowest prediction error; the structural change ARIMA model fits better for prediction in the long term, while the GM (1,1) model compared to proposed models has the greatest prediction error. Moreover, the relationship between current BDI and previous BDI is highly significant. In addition, the external interference is negatively related to the current BDI index. The conclusion of this paper provides the bulk shipping industry with a beneficial reference for market and risk assessment.

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

“…In addition, they asserted that, with some degree of speculative efficiency, forward rates help in the forecasting of spot rates using ARIMA or vector autoregression (VAR) models for the prediction of forward rates. The relationship of the BDI with trade and income was investigated by Lin and Wang (2014), and by Kaloupsidi (2014), who explored the nature of fluctuations in world bulk shipping by quantifying the impact of demand uncertainty and timeto-build on BDI prices. More recently, Papailias, Thomakos, and Liu (2016) showed that the BDI has a cyclical pattern that has been stable, except for a period after the 2008 crisis, which suggests that such a pattern has implications for improved forecasting.…”

“…More recently, (Lin and Wang, 2014;and Kaloupsidi, 2014) explored the nature of fluctuations in world bulk shipping by quantifying the impact of demand uncertainty and time-to-build on This article is protected by copyright. All rights reserved.…”

“…Further, comparisons of volatility in the dry-cargo ship sector have been conducted by Kavussanos and Nomikos (1999), while seasonal properties and forecasting in the dry bulk shipping sector have been investigated by Cullinane (1995), Cullinane, Mason, and Cape (1999), and Kavussanos and Alizadeh, (2001). More recently, Lin and Wang (2014) and Kaloupsidi (2014) explored the nature of fluctuations in world bulk shipping by quantifying the impact of demand uncertainty and time-to-build on investment and prices. Tsioumas et al (2017) show that a VARX model is superior in predicting BDI, compared with autoregressive integrated moving average (ARIMA).…”

Determining the Causal Relationship Between Baltic Dry Index (Bdi) and Macroeconomic Variables by Toda-Yamamoto Analysis

“…Especially in the last few years, the idea that the BDI can be an important indicator of economic dynamics has spread. Besides that, BDI, which appears in global trade, consists of transportation costs, and monitors price offers for transportation, is considered a powerful economic indicator by many economic authorities (Lin and Wang, 2014;Saraç, Zeren and Başar, 2015;Şahin et al, 2018;. In addition to indices such as Supramax, Panamax, and Capesize, each of which belongs to a separate field, economists and market investors daily follow the BDI.…”

“…Today's businesses need accurate information to maintain their competitive advantage. The information sources need to be quite valid and reliable, thus BDI is accepted as a critical reference source for understanding the bulk shipping market (Lin & Sim, 2013;Lin & Wang, 2014). Moreover, this indicator is an essential basis for predicting stagnations, growths, and recessions that may occur in the economy.…”