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We find it very interesting and satisfying to see that our model expectations fit well with the regression outcome. Most variables are found to be significant determinants of the freight rate on a 5% level. Every variable besides Oil Production influence the freight rate in the projected way. However, the variable is not significant when the whole period is optimized, and has a positive sign in the optimized model from 2004-2008 and negative sign in the following

First of all, the analysis finds there to be no relationship between number of days forward and the freight rate when the whole period is analyzed. This was the variable we were the most unsecure of, but we were eager to test due to the findings in Alizadeh and Talley (2011b).

Though, it did show a positive correlation with contract duration in our data and is significant on a 1% level in the first period. The sign of the coefficient indicates that increased number of days forward leads to lower freight rates. This is opposite of what Alizadeh and Talley (2011b) find for the tanker market.

DWT is also found to not be a significant determinant of the freight rate in any of our regressions. This is all in all due to its correlation with deck area. DWT would have proven to be a significant determinant if deck area was excluded from the model. This was found when experimenting and working with the data, but is not presented in the thesis. M² is found to be the best and most logical determinant of the two.

Deck area, M², is found to be a significant determinant of the freight rate in all of our analyses.

It is one of the three remaining variables in our model optimization attempt, see Appendix 3.1.

The positive relationship between deck area and the freight rate is in accordance with our hypothesis that a vessel with larger deck area receive higher freight rate. All in all it is found to be the most important vessel specification. BHP is found to be significant in both the linear and the original semi-logarithmic regression analysis along with the DP2 dummy. Increasing engine size and DP-class have the anticipated effect on the freight rate. Of the two, only DP2 is significant on a 10% level from 2004-2008. Both variables are significant from 2009-2015 on a 1% level. Figure 6.3 shows that freight rates for vessels with DP2 has been consistently higher compared to freight rates for vessels without DP2. In accordance with our hypothesis, charterers value DP2 higher and are willing to pay for the quality it brings. In low freight rate environments the difference in the freight rate is reduced however. The reduced difference can be explained by supply and demand balance in the market at the time. When capacity is scarce, charterers will outbid each other to charter higher quality vessels and freight rate differences arise between DP-classes. When there is excess capacity and low freight rates, a charterer is likely to choose a vessel with DP2 first when the difference in costs between a DP1 and DP2 is minimal. In other words, the DP2 “premium” diminishes in a market trough.

Dummy variables for scope of work appear to be key determinants of the freight rate. Both production- and drilling support achieve significantly lower freight rates than other scopes of

work. This is true for all time periods investigated and in accordance with our expectations.

We consider it to be due to the predictable and standardized nature of these scopes of work.

Figure 6.3 DP-class' Average Freight Rate

Increasing vessel age is estimated to result in lower freight rates in all of our analyzes, in line with expectations. The same can be interpreted about contract duration. Vessels on longer contracts appear to achieve lower freight rates. At first, it surprised us is that contract duration is one of the most significant determinants in the period from 2004-2008 while being insignificant in the following period. However, some explanations to this seem logical. In the latter period, by observing the thick order book and excess supply of vessels, charterers may have chosen to fix vessels on shorter contracts as they were convinced that they will always be able to charter a vessel cheaply. Charterers would not need to charter vessels on long-term contracts in this scenario unless they are eager to fix a specific vessel. Also, a high correlation between Brazil and contract duration is present in the most recent period and makes it difficult for both variables to be significant. We experimented with the data and found contract duration to be significant on a 1% level when all variables except the Brazil dummy was included in the model.

The dummy variable for where the scope of work is taking place proves to be a very significant determinant of the freight rate. The dummy variable is insignificant in the period 2004-2008 but is one of the most significant variables from 2009-2015. The explanation for this is the same as above; the correlation between the Brazil dummy and contract duration makes it

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Freight Rate

Year

DP1 DP2

difficult for both variables to be significant at the same time. When experimenting with the data, the Brazil dummy is found to be negative and significant on a 1% level in the first period when all variables except contract duration is included in the model. However, contract duration appears to be the best determinant of the two from 2004-2008 and Brazil appears to be best from 2009-2015.

The significance of the region variable proves our assumption that there are differences in freight rates between the regions. We believe the main reason for this to be the fact that costs related to operations in Brazil have increased dramatically in the recent period. Figure 6.4 displays the yearly average term charter freight rates in the North Sea and Brazil. Compared to the North Sea, freight rates in Brazil were significantly lower before the costs started rising but have been higher from 2008 until today.

Figure 6.4 Regions’ Yearly Average Term Charter Freight Rates

As expected, the oil price is found to be the most important determinant of the freight rate when analyzing the whole period. It is the most significant variable in all regression analyses except in the time period 2004-2008, due to the already explained correlation with Oil Production. Increased oil price will in general imply increased freight rates. The worldwide oil production has an opposite pattern. It is among the least significant determinants in all regressions except the first period where it is the most significant variable.

The monthly average spot freight rate is consistently one of the most significant variables in our analyses. The spot has been a leading variable throughout the whole examination period.

0

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

Freight Rate

Year

Brazil North Sea

The correlation matrix and descriptive statistics found in the appendices show interesting facts about the periods. The spot freight rate has a correlation of 0,44 with the term charter freight rate from 2004-2008 and 0,35 from 2009-2015. This is opposite of what we expected. The higher correlation in the former period might imply that the spot- and term charter freight rates were more interrelated before the financial crisis. Monthly average spot freight rates were 36.534 USD/day in the first period and 17.490 USD/day in the last period, while the monthly average term charter freight rates were 24.413 USD/day and 24.195 USD/day, respectively. It indicates that the spot market has weakened while the term market has remained at the same freight rate level. These findings appear to be in line with Figure 3.2. When the market is peaking, spot freight rates are substantially higher than term freight rates. The opposite appears to be true during a trough. In addition, the variation in spot freight rates is high in a high freight rate environment and low during a lay-up freight rate environment. The standard deviations in Figure 3.6 display this well, especially when having the freight rates in Figure 3.4 in mind.

Figure 3.3 captures the underlying trends in Figure 3.4.