Dry Bulk Freight Market Research Articles

Measuring the Impact of Covid-19 Pandemic and the Russian Ukrainian War on Dry Bulk Freight Market

Measuring the Impact of Covid-19 Pandemic and the Russian Ukrainian War on Dry Bulk Freight Market

DRY BULK FREIGHT MARKET RESEARCH BASEDON CORRELATIONANDREGRESSIONANALYSIS

Thecorrelationandregressionanalysisappliedtothefreightratesdemonstrates a fairly good results which is confirmed by the relevant estimates of the models.However, the impact of each determinant on the freight rates is not the same in the different timeperiod. Moreover, it is important to focus on the certain segment of dry bulk freight market moreaccuratelytoobtainthemathematicalmodelreflectingsegmentspecific.The study is aimed to develop the methodological basic for applying the correlation andregressionanalysistothefreightratesontheexampleofBalticPanamaxIndex.Various linear regression model structures for freight rates have been proposed,withappropriate detailing of the underlying market determinants. It has been determined that eachfreightmarketsegment is characterized a certain specificityofdemand,as wellas competitionwith other segments, these should be taken into account when building regression models. Theideas suggested have been applied to Panamax segment. The Panamax bulk carriers serve mostlythe grain, coal and ore; thus, these three types of goods are included in the model as demanddeterminants. The correlation and regression analysis were used to establish and mathematicallydraw the dependence of freight indexes on the set of the major freight market determinants ontheexampleoftheBalticPanamaxIndex.The output shows the obtained regression model can be used to predict the Baltic PanamaxIndex. Based on the forecasting of considered factors -seaborn grain trade, seaborn ore trade,seaborn coal trade, Panamax bulk carriers’ sector of merchant fleet, Panamax bulk carriersshipbuilding,Panamaxbulkcarriersscrapping,thevaluesoftheBalticPanamaxIndexforfollowingtimepointscanbeestimated.Two approaches were considered –the regression analysis for original data and forlogarithmically transformed data. Whether the first approach with the original data or the secondonewiththe logarithmically transformed data is applied,theresultsapprove theregressionanalysis use to establish the relationship between freight indexes (freight rates) and the set of themajor freightmarketdeterminants.Keywords: freight rates, Panamax bulk carriers, model, market determinants, maritimetransportation,forecast.

Analyzing the Impact of Interest Rate on Dry Bulk Freight Market with Time-Varying Causality Method

Faiz oranları hem gemi yatırımlarındaki maliyeti belirleyici rolü hem de dünya ekonomisindeki mallara olan talebi belirleyici rolü ile denizcilik piyasalarını da önemli ölçüde etkilemektedir. Biz bu çalışmamızda diğer denizcilik piyasalarının temeli olan ve onlara göre çok daha dinamik yapıda olan navlun piyasasına olan etkisini incelemeyi amaçladık. Faiz oranlarındaki değişimin dünya ekonomisini etkileyeceğini ancak bu etkinin tüm dönem boyunca varlığını sürdüremeyeceğinden yola çıkarak, önce doğrusal bir yaklaşımla, daha sonra ise zamana göre değişen bir yaklaşımla analizleri uyguladık. Analizlerde küresel faiz oranlarının göstergesi olarak Federal Fon Oranı, kuru dökme yük navlunlarının göstergesi olarak ise Baltık Kuru Yük Endeksi (BKYE) kullanılmıştır. 1995 ve 2020 yılları arasını kapsayan aylık 302 gözlemden oluşan veri seti kullanılarak zamana göre değişen yaklaşım ile yapılan analizler sonucunda, anlamlı ilişkinin tüm örneklem boyunca var olmadığı, sadece belli zamanlarda görüldüğü ve anlamlı ilişkinin görüldüğü dönemlerin BKYE’nin artış ve düşüş eğilimine girdiği dönemler olduğu tespit edilmiştir. Böylece faiz oranlarındaki değişimlerin yalnızca küresel ekonomiyi etkilemekle kalmayıp, kuru dökme yük navlun piyasasının eğilimini de etkilediği belirlenmiştir.

Capturing the impact of economic forces on the dry bulk freight market

This study focuses on the construction of a composite indicator which is designed to measure the component of economic activity that drives demand for sea transport of dry bulk commodities. Various economic factors are identified and then reduced to the most critical ones using statistical analysis. The study tests three different approaches for the aggregation and weighing of the selected variables. The statistical results indicate that a Deviation-based Goal Programming model is the most robust method for the construction of a composite indicator in the context of the dry cargo shipping freight market. Granger Causality and Impulse Response analysis are used to investigate the association between the new index and the dry bulk freight rates. Overall, the results demonstrate the robustness of the proposed composite indicator. This new index may allow more effective monitoring of economic developments and more accurate assessment of their aggregate impact. In this regard, the proposed indicator could potentially serve as a decision support tool for ship operators and other maritime practitioners. This is the first time that the most critical elements of the economic environment of the shipping freight market are captured through a composite indicator.

Identifying shipowners’ risk attitudes over gains and losses: Evidence from the dry bulk freight market

This study examines the risk-return trade-off in the dry bulk freight market under different scenarios such as risk measures, risk attitudes and controlling for variables associated with the freight rate cycle. For long-term contracts, there exists a negative association between risk and return, suggesting that shipowners are willing to offer a discount on time-charter rates over spot rates to compensate for the loss of flexibility. Additionally, shipowners are not uniformly risk averse, as finance theory suggests, since their utility functions are concave (risk-averse) for losses and convex (risk-seeking) for gains.

The value of foresight in the drybulk freight market

We analyze the value of foresight in the drybulk freight market when repositioning a vessel through space and time. In order to do that, we apply an optimization model on a network with dynamic regional freight rate differences and stochastic travel times. We evaluate the value of the geographical switching option for three cases: the upper bound based on having perfect foresight, the baseline set by a random strategy, and the case of perfect foresight but only for a limited time horizon. By combining a neural network with optimization, we can assess the impact of varying foresight horizons on economic performance. In a simple but realistic two-region case, we evaluate empirically that the upper bound for large vessels can be as high as 25% cumulative outperformance, and that a significant portion of this theoretical value can be captured with limited foresight of a few weeks. Our research sheds light on the important issue of spatial efficiency in global ocean freight markets and provides a useful upper bound for the value of investing in predictive analysis.

THE EFFECTS OF GLOBAL ECONOMIC GROWTH ON DRY BULK FREIGHT RATES

Dry bulk freight market is one of the most important indicators of global economic activity as it is the market for the transportation of raw materials that make up final products. So, the world economy is one of the most important demand factors for maritime transport. But supply in the maritime market is inelastic and the market cannot immediately respond to sudden demand growth and rates declines in the short run. Therefore, the sudden increases and decreases in the economy result in extraordinary income changes in the freight market. In this framework, the influence of economic growth on freight rates is an important issue to be examined. This study aims to contribute to the current literature by empirically examining the effect of economic growth on the dry bulk market. World GDP (Gross Domestic Product) was selected as a measure of economic activities, and BDI (Baltic Dry Index) was chosen as a measure of dry bulk freight rates. The dataset covers the years between 1985 and 2016 on annual basis and consists of 32 observations. The relationship between variables was tried to be determined by correlation and regression analysis. According to the results of the study, the positive relationship between the variables was determine and the BDI's response to GDP changes was found to be greater. These results confirm how risky the maritime market is.

PREDICTING BALTIC DRY INDEX WITH LEADING INDICATORS

The Baltic Dry Index (BDI) is issued by the Baltic Exchange on a daily basis and it signals for the average cost of shipping raw materials on a number of shipping routes. Baltic Dry Index is considered by both the private and public authorities as an important indicator for freight rates, international trade and economic activity. Conducting a long-term prediction for dry bulk indices is challenging due to the high volatility of the dry bulk freight market; therefore, a linear prediction spanning a shorter time period offers both greater accuracy and can be used as a tool for speculation. The goal of this paper is to form a linear benchmark model through Box-Jenkins approach including explanatory variables selected rigorously to forecast Baltic Dry Index. Using monthly data between January 2010 and June 2017, the analysis results point out an ARIMAX (10,1,0) model with spot prices of gold and silver, United States 10-year bond yield and commodity price index composed of minerals, ores and metals.

Asymmetric volatility varies in different dry bulk freight rate markets under structure breaks

The past decade has experienced the historical boom and recession in the dry bulk shipping market, which is extremely volatile and more complex than ever before. The asymmetric correlations analysis within the shipping market can provide an understanding of the asymmetric features of risk, which would be helpful for portfolio diversification and risk management. This paper investigates the asymmetric volatility characteristics in dry bulk freight market during the 2008 world financial crisis, which may impose significant impacts on the mechanism of market volatility. First, we apply multiple structural change model to detect the breaking points. Then an improved A-MF-DFA (Asymmetric Multifractal Detrended Fluctuation Analysis) model is applied to capture the asymmetric volatility of the dry bulk shipping market brought by the financial crisis. The empirical results suggest that the financial shocks on the market have a different magnitude of influence on volatility of different vessel types, and the breaking point positions also varies. The positions and scales of the switching points (SPs) of the fluctuation trend detected from A-MF-DFA method also suggest highly asymmetric characteristics in the indices. These findings supply new perspectives for investors in understanding the underlying market risks, crisis effects on bulk shipping market and detailed volatility characters for specific vessel types.

The Value of Foresight in the Drybulk Freight Market

We analyze the value of foresight in the drybulk freight market when repositioning a vessel through space and time. In order to do that, we apply an optimization model on a network with dynamic regional freight rate differences and stochastic travel times. We evaluate the value of the geographical switching option for three cases: the upper bound based on having perfect foresight, the lower bound based on a coin flip, and the case of perfect foresight but only for a limited horizon. By combining a neural network with optimization, we can assess the impact of varying foresight horizon on economic performance. In a simple but realistic two-region case, we show empirically that the upper bound for large vessels can be as high as 25% cumulative outperformance, and that a significant portion of this theoretical value can be captured with limited foresight of several weeks. Our research sheds light on the important issue of spatial efficiency in global ocean freight markets and provides a benchmark for the value of investing in predictive analysis.

Value-at-risk analysis of the asymmetric long-memory volatility process of dry bulk freight rates

This study aims to apply value-at-risk (VaR) models to evaluate the risk of dry bulk freight rates when there is an asymmetric long-memory volatility process. The VaR estimations as well as expected shortfalls for both short and long trading positions are conducted. We use the Fractionally Integrated GARCH, Hyperbolic GARCH and Fractionally Integrated APARCH models to analyse the performance of the VaR models with the normal, Student-t and skewed Student-t distributions. Empirical results suggest that precise VaR estimates may be obtained from an asymmetric long-memory volatility structure with the skewed Student-t distribution. Moreover, the asymmetric FIAPARCH model outperforms than other models in out-of-sampling forecasting. Therefore, our findings provide a more accurate estimation of VaR for dry bulk freight rates. These results present several potential implications for dry bulk freight market risk quantification and hedging strategies.

Analysis of Term Structure in Dry Bulk Freight Market

Based on the monthly data set from January 1992 to May 2012, this paper applies a VAR model and two time-varying coefficient models to analyze term structure in dry bulk freight market. The resulting three main empirical findings are as follows: 1) The response of long-term rate to short- term structural shock is relatively small and not statistically significant but the response of short-term rate to long-term structural shock is large and statistically significant. 2) Compared with the presumption that in backwardation the short-term rate is inclined to decrease but the long-term rate to increase, but in contango the opposite pressure would function, there is lack of evidence for the stable adjustment speed in both equations for the short- and long-term freight rate. 3) When the market is relatively above the normal level (more backwardation), the effect of the implied forward time charter (IFTC) rate on the actual TC rate becomes larger. When predicting the future TC rate based on IFTC rate, it recommends giving more weight to the IFTC rate in backwardation than in contango.

Dynamics of Dry Bulk Freight Market: Through the Lens of a Common Stochastic Trend Model

This paper shows the possibility that, as a simple average, BDI occasionally over- or under-states the status of global dry bulk freight market. In order to overcome this shortcoming, this paper suggests an alternative method of calculating new index by using a common stochastic trend model. This econometric approach to understanding the dynamics of dry bulk freight markets not only provides new index, but also gives a decomposition method. That is, through this lens of common stochastic trend model, we can decompose the freight data into common (or permanent) and idiosyncratic (or transitory) components.

A fuzzy extended DELPHI method for adjustment of statistical time series prediction: An empirical study on dry bulk freight market case

This paper investigates the forecasting accuracy of fuzzy extended group decisions in the adjustment of statistical benchmark results. DELPHI is a frequently used method for implementing accurate group consensus decisions. The concept of consensus is subject to expert characteristics and it is sometimes ensured by a facilitator’s judgment. Fuzzy set theory deals with uncertain environments and has been adapted for DELPHI, called fuzzy-DELPHI (FD). The present paper extends the recent literature via an implementation of FD for the adjustment of statistical predictions. We propose a fuzzy-DELPHI adjustment process for improvement of accuracy and introduced an empirical study to illustrate its performance in the validation of adjustments of statistical forecasts in the dry bulk shipping index.

Intra-week Trends in Dry Bulk Freight Market: Models of Short Term Estimation

Intra-week Trends in Dry Bulk Freight Market: Models of Short Term Estimation

Intra-week Trends in Dry Bulk Freight Market: Models of Short Term Estimation

Intra-week Trends in Dry Bulk Freight Market: Models of Short Term Estimation

A Mixed-Regime Model for Dry Bulk Freight Market

This paper constructs a mixed-regime model for dry bulk freight market. Then it performs maximum likelihood estimation and provides the results. Inter alia, the hypothesis that there is bimodality in the supply curve of shipping freight markets is strongly supported by analyzing the effect of the term structure of time-charter rates on the time-varying volatility.Furthermore, the adjustment speed of 1-year time-charter rate in lowvolatility regime is larger than in high-volatility regime, which means the market players consider the backwardation shock in low uncertainty as more important than in high uncertainty. Especially the Cape-size markets respond positively to the back-wardation shock in low-volatility regime but respond negatively in high-volatility regime. Finally, the estimated time-varying variance using real-time data has a highly statistically significant relation with the next squared error terms.

Judgmental Forecasting in the Dry Bulk Shipping Business: Statistical vs. Judgmental Approach

This paper investigates the accuracy of judgmental forecasting methods for dry bulk freight market. Judgmental forecasting is a method that implements the purely judgmental aspects of a system. The research investigates judgmental forecasting for the Baltic Dry Index, and compares it with statistical extrapolation methods. Post-sample forecasting accuracy indicates the superiority of the proposed approach over the conventional statistical methods. Empirical studies are performed with an expert group and the accuracy of judgmental point estimates is compared with exponential smoothing, the X12 ARIMA, and the TRAMO/SEATS algorithms. The results of expert predictions outperformed traditional time-series methods.

Predictive power and unbiasedness of implied forward charter rates

AbstractThis paper examines the efficiency and predictive power of implied forward shipping charter rates. In particular, we examine whether implied forward 6‐month time‐charter rates, which are derived through the difference between time‐charters with different maturities based on the term structure model, are efficient and unbiased predictors of actual future time‐charter rates. Using a dataset for the period January 1989 to June 2003, results of different statistical tests, including the cointegration approach, suggest that implied forward rates are in fact unbiased predictors of future time‐charter rates in the dry bulk freight market. In addition, it is found that implied forward rates yield superior forecasts compared to alternative univariate and multivariate time series models. However, while the unbiasedness hypothesis is found to hold, on average, we find that chartering strategies based on simple trend‐following trading rules in this cyclical market are able to generate economic profits even out‐of‐sample. This highlights how standard tests for unbiasedness do not always capture cyclical predictable components in the market behaviour. Copyright © 2007 John Wiley & Sons, Ltd.

Asset Bubbles in Shipping? An Analysis of Recent History in the Drybulk Market

The purpose of this paper is to investigate the hypothesis that the freight market boom in the drybulk freight market between 2003 and 2005 caused asset values in the second-hand market to deviate from underlying fundamentals. We test the instantaneous equilibrium relationship between the markets for second-hand ships, newbuildings and freight in a Vector Error Correction Model (VECM) framework. We also estimate and account for the time-varying delivery lag in the newbuilding market. Our empirical results suggest that the second-hand market was closely cointegrated with the fundamental freight and newbuilding market with no evidence of a short-term asset ‘bubble’.