Leading Indicators for freight rates

139

Leading indicators for Arabian Gulf oil
tanker rates
Eric Tham
Quantitative analyst for commodities, Standard Chartered Bank, 6 Battery Road, #03-00 Singapore 049909

Abstract
In this paper, price drivers for the Arabian Gulf oil tanker rates were derived from the Bayesian logis-
tic regression to form a leading indicator. A universe of price drivers was filtered based on statistical
criteria and speculative backtest results. Results showed that refining margins in Asia, crude produc-
tion in the Arabian Gulf, the vessel utilisation rate and Brent–Dubai spreads were the most signifi-
cant price drivers of TD3. A time series of these drivers indicates that Arabian Gulf production has
a declining importance relative to the Brent–Dubai spreads since 2004. A vector error correction
mechanism analysis of the TD1 and TD3 benchmarks indicates that TD1 returns lag behind TD3
rates.

1. Introduction
The freight market has seen rapid growth over the past few years, with active trading
among ship owners, financial institutions and oil companies alike. An academic review by
Kavussanos and Visiviki (2006) revealed that literature on this growing market has been
relatively little compared with other commodities. This literature has typically examined
the relationships between freight spot and futures rates, and encompasses both wet and dry
freight analyses. For example, Kavussanos and Nomikos (2002) examined the lead–lag
relationship between the forward and spot market, in terms of their returns and volatility
on the Baltic International Freight Futures Exchange contracts. Batchelor and Adland
(2005) examined the relationship between expected volatility and bid ask spreads in the
future markets, and Cuillinane (1992) made short-term forecasts on the spot rates. The
papers employed time series techniques—auto-regressive integrated moving average,
vector auto-regression or vector error correction mechanism (VECM) that relate lagged
and present values of the spot and forward rates.
This paper directly examined the price drivers on the wet freight rates, in particular for
crude. Crude oil from the Arabian Gulf flows mainly to the east or west via very large crude
carriers [(VLCCs) with 250 or 280 kt tonnage]. These routes are identified as either the
TD1 (Ras Tanura to LOOP) or TD3 (Ras Tanura to Chiba) benchmarks. Globally, crude oil

© 2008 The Author. Journal compilation © 2008 Organization of the Petroleum Exporting Countries. Published by

Blackwell Publishing Ltd., 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA.

140 Eric Tham

is transported either via VLCCs or smaller vessels like medium sized tankers (up to
100 kt), in which prices are strongly linked. Over the period of 2000–2007, the TD3 rate,
which is the most important, and liquid wet freight benchmark had been volatile and varied
from $1/b to more than $5/b. This has a direct impact on refining economics, which can
determine where the crude is exported and refined eventually. Refining economics have
complex relationships among different products and vary with different refinery configu-
rations in different regions. However, it is important to understand the drivers behind
freight rates as they are a marginal component of refinery margins, and can also affect
inter-regional arbitrages for crude and products.
Several lead–lag relationships exist in the commodity markets. This is in part because
of the supply chain prevalent in commodities and inter-regional flows between supply and
demand centres. Freight is the lifeblood that connects these regions and products together.
The subject of interest in this paper was the relationships between the Arabian Gulf crude
freight rates and their driving factors. These relationships are frequently dynamic in
nature. Some of the drivers explored in this paper included the Brent–Dubai swaps (traded
on the Singapore Platts window), VLCC utilisation rates, crude prices, Arabian Gulf crude
production, east–west fuel oil arbitrage difference, amount of crude flowing east and west,
and refinery margins (both complex and simple).
In the next section, the data on the freight prices and their drivers are first
described. Following this, an outline of the Bayesian logistic methodology that is rel-
evant to the analysis is given. The findings of the drivers of the TD3 freight rates are
then presented. A time series indicating the importance of these drivers is in the next
section. The lead–lag relationship between this TD3 and the TD1 is then studied. A con-
clusion follows.

2. Data
A historical time series of the TD3 front month swap rates and TD1 spot is shown below.
Both prices are normally quoted in WorldScale rates. The source of the data was the Inter-
national Maritime Exchange (IMAREX). For comparison across years, they are expressed
in USD/b adjusted by the yearly different flat rates. The yearly flat rates reflect changes in
port charges, fuel charges and other miscellaneous expenses that a voyage may incur. By
adjusting for the flat rates yearly, the rates better reflect the residual demand and supply of
the vessels. Both rates show high correlation as expected. In a later section, the two rates
are studied in greater detail.
Figure 1 shows three noticeable spikes of TD3 rates in the late 2004, 2005 and 3Q
2006. The spikes in 2005 were attributed to hurricanes Rita and Katrina, and the temporary
shutdown of Prudhoe Bay. These events resulted in a temporary draw of VLCCs to trans-
port crude to the United States, reducing the supply of vessels in the Middle East. The year

OPEC Energy Review June 2008 © 2008 The Author.

Journal compilation © 2008 Organization of the Petroleum Exporting Countries

Leading indicators for Arabian Gulf oil tanker rates 141

8

7
Time series of
6 TD1 & TD3 rates

5
$/bbl

4

3

2

1

0
2003 2004 2005 2006 2007

TD1 spot TD3 front month

Figure 1 Time series of TD1 and TD3 in USD/b on a daily basis (October 2002 to November 2007).

2004 saw a temporary dislocation in VLCC supply. While these are known on hindsight,
the paper sought to find out if the increases are prior identified by other drivers. Potential
drivers include crude prices, the Brent–Dubai spread, refinery margins, VLCC utilisation
rates and the marginal flow of crude east or west and the Arabian Gulf crude production.
The fundamental effects of these drivers are discussed more extensively in the next
section.

2.1. The fundamentals
High refinery margins (and thence product demand) tend to raise freight rates, as refiners
likely increase their crude imports to improve profitability. This is especially true for
exporting countries like Taiwan and Korea, which import almost all their crude from the
Middle East. The lack of crude storage capacity in these countries relative to the more
developed countries means often product demand is met by transporting and refining more
crude, and providing more support to the freight rates. As the Middle East region is the
only region that has crude spare capacity, incremental crude is likely to be met from this
region, increasing the TD3 rates.
This observation is, of course, not straightforward with the interplay of refining eco-
nomics from simple and complex refining margins.1 Most of the refining profits are from
middle and light products, with the latter more so for North America and the former for
Asia. This is because of the relatively higher usage of gasoline and diesel/jet in these
respective regions. Optimised use of the refinery upgrading units means that any incre-

© 2008 The Author. OPEC Energy Review June 2008


142 Eric Tham

14

12

10
USD/bbl

8

6

4

2

0
2003 2004 2005 2006 2007

Figure 2 Time series of Brent–Dubai spreads on a daily basis (October 2001 to December 2007).

mental crude intake flows out from the crude distillation units. Given that crude from the
Middle East is heavy, this will result in a high content of residual products, which does not
necessarily improve refining margins as much. For this reason, both simple and complex
refining margins are included in the testing. This is because while simple margins consider
incremental yields from the distillation unit, the complex margins consider all profits from
the whole refinery complex.
The heavy–light crude differential reflected in the Brent–Dubai spread is another
factor that is likely to affect the demand of heavy crude from the Middle East. Given that
several African and Former Soviet Union (FSU) grades are priced off the Brent bench-
marks, a wide Brent–Dubai spread makes these grades less attractive to both refiners in
America and Asia alike. This in turn is likely to increase the demand for Middle East
crude and correspondingly, the crude freight rates. The correlation of the Brent–Dubai
spread with the Middle East freight rate was apparent in late 2004, when both rates
spiked, as can be observed in Fig. 2 above for Brent–Dubai rates. This relation is,
however, not apparent in the subsequent years, indicating that there are other factors
driving the freight rates.
The availability of the VLCCs is also another factor. As of the end of 2007, the number
of VLCCs in the world is about ~500.2 Each year, there are new builds and deletions of
VLCCs because of old age. Single-hull vessels in particular have been replaced over the
past 2–3 years with double-hull vessels to meet increasing environmental restrictions. In
mid-2007, when dry freight rates have increased because of increased demand for metal
ores and coal, VLCCs were converted to very large ore carriers, thereby reducing the
supply of VLCCs. Given that it takes time for builds and deletions to take place, a temporal




1.00

0.96
Per cent availability

0.92

0.88

0.84

0.80

0.76

0.72
2004 2005 2006 2007 2008

Figure 3 Time series of availability of very large crude carriers on a monthly basis (January 2004 to
December 2008).3

dislocation of supply and demand of VLCCs can result in structurally higher or lower
equilibrium freight rates. The diagram above (Fig. 3) is a time series of the availability of
the VLCC since 2004.
The amount of crude that flows east or west is another factor that affects the freight
rates. The sail time to the Atlantic Basin is approximately 35 days, while it takes the VLCC
about 45 days to travel to Chiba. The necessary transit time for crude to sail back and forth,
directly impacts the near values of the freight rates. A higher relative volume of crude
flowing either direction impacts the spreads between these two freight routes. The lead–lag
relationship between the TD1 and TD3 is examined later.
Other fundamental factors that may affect VLCC freight rates include OPEC produc-
tion cuts and refinery operations in the Middle East. This is especially important given the
new refineries that are to be set up in the Middle East in the next few years up to 2011.
This can result in a structural shift of driving factors, as less VLCCs will be required out
of the Arabian Gulf and more product vessels. The occasional use of VLCCs for floating
storage is another factor that reduces the supply of VLCCs, although this is only economi-
cal at times of low freight rates. Apparently, these were occurrences in mid-2007, when
Iran stocks up crude in floating vessels. The use of a floating storage is also a common
occurrence in Singapore. There have also been residual fuel oil being transported from
the FSU in VLCCs, although this number is small, approximately two to three times a
month, to have a definitive impact on rates. Following the discussion on fundamental
factors that may affect VLCCs freight rates, a discussion on the statistical methodology
used in this paper is next.



144 Eric Tham

3. Methodology
The statistical methodology used is the ordered logistic regression. This regression type
differs from ordinary least squares regression in that the explanatory variables are discrete
variables. The methodology is well described in most statistical textbooks, e.g. Menard
(2001), and only its relevance and application in this paper will be described here. All work
is done using the Eviews software. In this regression, the discrete latent variable ‘y’ is
regressed against a functional form g, of the discrete ‘x’, given by the following:

yt* = g ( xt′β ) + ε t (1)

where et is the independent and identically distributed random variable. The observed yt
is determined from yt* using the rule:

⎧0 yt* ≥ c1
⎪
⎪1 c1 ≥ yt* ≥ c2
⎪
⎪
yt = ⎨2 c2 ≥ yt* ≥ c3 (2)
⎪
⎪ c3 ≥ yt* ≥ cn
⎪
⎪M
⎩ cM ≥ yt*
where the yt* is bounded by the thresholds ci for i = 1, 2, . . . . . . m.
In this paper, yt* ’s refer to the categories of bullishness or bearishness for the leading
indicators of the TD3 freight rates. The c’s are the threshold values partitioning the sum of
the market driving factors xt′ that are prior observed. The xt′ ’s are sum weighted by the b
coefficients to form the leading indicator yt*. The non-linear relationship between yt* and
xt′ expresses the probabilities of yt* as odds of happening in each category ci+1 to ci as:
1
Prob( ci +1 ≥ yt* ≥ ci ) = pt* = . (3)
1 + e −( xx β )
′

Here, the leading indicator yt* has the highest probability of being located in the middle
‘range’ for larger intervals of [ci, ci+1] and the least probabilities of being overly bullish or
bearish, that is [c1, •] or [-•, cM]. This is a reflection of the actual normal distribution
observed in market returns. Another advantage of this discrimination analysis is that cat-
egorisation reduces predictive noise in the system. As it is almost impossible to make
precise value calls on the freight rates, categorical calls are made instead. The sigmoidal
shape of the logistic regression is also symbolic of the market reactions to movements
in xt′. As observed in Fig. 4, when news first arrive, the initial reaction of yt* is greater
than when after equilibrium had set in. Equilibrium sets in after higher values of the
driving factors xt′ had persisted, and the market has adjusted to new price levels. In our




Logistic regression model
1.2
Reaction tapers off in
aftershocks

0.9
yt*

0.6 Greater initial reaction
to the driving shock
seen by the steeper
gradient
0.3

0.0
0 5 10 15 20 25
xt′b + et

Figure 4 Reaction of market rates to initial and subsequent shocks.

study, xt′ refers to a universe of factors that can affect yt*. These factors were discussed
earlier in the fundamentals section.

3.1. Pre-processing of data
The data used for analysis is for a period of 5 years from October 2002 to November 2007.
Before this period, there was no active trading on the freight swap rates on IMAREX, and
price discovery is not efficient. The TD3 data for the front and second month rolling con-
tracts from IMAREX were used for the analysis. Other data sources for the price drivers
were mainly from the reporting agency Platts, which is the most reliable source for energy
data in Asia. The data set includes from Platts Singapore—the Dubai, Brent–Dubai swap
rates and for the products gas oil, jet, naphtha, fuel oil and gasoline to calculate the simple
and complex refining margins. VLCC utilisation rate was taken from the PIRA reports,3
while the amount of crude sailing east or west is taken from the publication Oil Move-
ments.4 The Arabian Gulf crude production is taken from official OPEC sources, although
these data were lagged by about 1–2 months.
These data were all normalised by their means and standard deviations before input
into the model. Normalisation is often found to be useful, as it indicates the ‘historical rela-
tive norm’ of an input, for example, relative to 3- or 5-year averages. This is common in the
energy industry, where stocks, price levels and other indicators are often compared with
historical highs/lows or averages like 3- or 5-year averages.



146 Eric Tham

4. Discussion of results
4.1. Regression and backtest results
The prior drivers were input into the regression equation (1) as x’s with the independent
variable and yt* ’s as the categories of bullishness or bearishness. The best lag period and
drivers were chosen partly based on statistical criteria, e.g. Akaike information criteria, the
log-likelihood ratio, etc. Regression was done on daily data from October 2002 to Septem-
ber 2005, as the in-sample period. The regression coefficients were then used out of the
sample from October 2005 to the end of November 2007 to form a leading indicator y, of
varying degrees of bullishness or bearishness. The leading indicators were divided into
intervals, with a sliding scale from the most bullish to the most bearish. For each interval,
the winning probability and the average profit or loss are backtested with the 1-month
absolute returns of speculative trades on the second front month contract. These were
plotted against the leading indicator. It was expected that for increasing bullishness of the
indicator, long trades have a higher probability of returning a profit and also higher profits.
Vice versa, indicators with increasing bearishness show increasing probability of return-
ing losses and higher average losses. This is seen below in Fig. 5 with the y-axes indicating
the profit USD/b and the likelihood of winning for long trades.
As the indicator becomes more bullish, profits/b and the winning trades probability
increase. It was noticed that this is even more apparent in a bullish market. The best indi-
cators were thence filtered from the set of indicators built from the different drivers, using
this out of sample backtest results and the statistical significance from the logistic regres-

0.8

Ave absolute PnL 1.0
% win prob

0.5 0.9

0.8
Win probability
$/bbl

0.3
0.7

0.6
0.0
1 6 0.5

-0.3 0.4
Increasing bullish indicator

Figure 5 Out of sample performance of leading indicator on a daily basis (September 2005 to
December 2007).




sion. A leading indicator constructed from prior values of Brent–Dubai swaps, complex
refining margins, forecasts of VLCCs availability and a lagged Arabian Gulf production
showed the best overall results. The equation for the leading indicator for a 1-month return
is thence:

Leading indicator = β1 BDt − 22 + β2 Complex Margin t − 22
+ β3VLCC use ratet + 35 + β3AG Production t − 35. (4)

The simple refining margin factor, crude flows to east or west and fuel oil East–West5
difference were omitted from the list of drivers. It must be noted that the Arabian Gulf
production data is lagged by 35 business days (or approximately 7 weeks) because of the
late release of data. For the refining margins and Brent–Dubai swaps, the data were lagged
by 2 days. The 35-day forward outlook for vessels availability from PIRA reports was used.

4.2. Importance of each driving factors
The relative importance and weights of each the driving factors were deduced from the
size of the coefficients of the normalised drivers. These are -0.48, -0.46, 0.86 and -0.49
for Brent–Dubai, complex margins, AG crude production and the vessels availability rate,
respectively, on average over the regression window. A positive sign in the coefficient indi-
cates that an increase in the driver value is likely to cause the TD3 rates to increase, while
vice versa is true for a negative sign. Thence, expectedly, an increase in AG crude produc-
tion increases TD3 rates, while an increase in vessel availability depresses TD3 rates. An
increase in the Brent–Dubai spread and complex margins on the contrary makes the TD3
rate more likely to decrease, on the average since 2004.
A time series of the coefficients was obtained from a rolling fixed window regression
moving forward one business day at a time. This is important as the coefficients may
change in value over time as seen in Fig. 6 below. The regression is done on a fixed
window period of rolling 1 year, starting from January 2004 to December 2007. The
Arabian Gulf production has a relatively declining effect on the TD3 rates. In turn, the
Brent–Dubai margin has grown in importance since 2005. A fundamental reason behind
this is the increasing production of crude from West Africa since this period, which draws
fleet away from the Arabian Gulf. These West African crude (e.g. Bonny Light, Angola
Girassol) are priced off the Brent benchmark, making the Brent–Dubai swap more impor-
tant as a hedging and speculative instrument. This likely makes it more important as a
price driver on the TD3 rates. In comparison, complex margins have a relatively stable
effect as a price driver.

4.3. Lead–lag relationships between TD3 and TD1
In this section, the lead–lag relationships between TD1 and TD3 spot rates are examined.
As observed in Fig. 1, the two rates showed a high correlation with each other, and quite



148 Eric Tham

1.0

0.5

0.0

-0.5

-1.0
2006M01 2006M07 2007M01 2007M07

Brent—Dubai AG production
Refining margin Asia Vessels utilisation

Figure 6 Time series of driving factors importance on a daily basis (December 2005 to December
2007).

likely a co-integrated relation. A lead–lag analysis of these benchmarks was done using
VECM. This follows a similar approach to Kavussanos and Nomikos (2002). In the latter
paper though, it was the lead–lag relationship between the spot and futures prices on the
Baltic Freight Index that was explored. The futures prices were found to lead the spot
prices and tend to discover new information more rapidly.
Similarly, the following VECM equations were used to model the TD1 and TD3 daily
prices:

⎛ td1t ⎞ p −1 ⎛ Δtd1t −i ⎞ ⎛ td1t −1 ⎞ ⎛ εTD1,t ⎞
⎜ = ∑ Γi + ∏⎜ + εt where ε t = ⎜ ∼ N (0, Σ ) . (5)
⎝ td 3t ⎟ i =1 ⎜ Δtd 3t −i ⎟
⎠ ⎝ ⎠ ⎝ td 3t −1 ⎟
⎠ ⎝ εTD 3,t ⎟
⎠
Here, D denotes the first difference operator, and Γ and Pi are 2 ¥ 2 coefficient ma-
´
trices measuring the short and long run. Results showed that the TD3 and TD1 rates
have a two-way lead effect on each other, but the impact of TD3 on TD1 was greater and
information discovery was faster with the TD3 rates. This is not surprising considering
that both are VLCC routes originating from the Arabian Gulf. Further, the TD3 is a more
liquid market with active future trading, and approximately 70 per cent of crude from the
Arabian Gulf flows to the east with the remainder flowing west.

5. Concluding remarks
This paper presented a logistic regression methodology to obtain the drivers of commodity
returns, in particular the 1-month returns of TD3 rates. It was found that the TD3 drivers




were the refining margins in Asia, Brent–Dubai spreads, Arabian gulf production and
VLCC availability. It must be recognised that these relations are dynamic in nature and
dependent on the market fundamentals, which have changed over the years. As a result of
the increased production of the West African crude, the Brent–Dubai spread has become
more important relative to the Arabian Gulf production. The TD3 rate was also found to be
more important in price discovery than TD1 and led the latter.

Notes
1. In this study, the following yields for the simple margin were used: 16, 19, 18 and 43 per cent,
respectively, for naphtha, jet, gas oil and fuel oil 180 CST, and for complex margins, the yields
were 9, 7, 11, 22, 28 and 14 per cent, respectively, for gasoline 97 RON, gasoline 92 RON,
naphtha, jet, gas oil and fuel oil 380. Dubai crude was used as feedstock. Yields differed by
crude and operating conditions for refinery configuration, but the yields used were typical for
refineries in the far east.
2. PIRA freight market report December 2007.
3. PIRA freight market report April 2004, April 2005, April 2006 and April 2007 reports. These
reports made projections of VLCC utilisation 1 year forward, which were revised with more
accurate data subsequently.
4. http://www.oilmovements.net
5. Difference between the Amsterdam, Antwerp and Rotherdam 180 CST fuel oil and Singapore
Platts 180CST fuel oil benchmarks.

References
Batchelor, S. and Adland, R., 2005. The relation between bid-ask spreads and price volatility in
forward markets. Derivatives Use, Trading and Regulation 11, 2, 105–125.
Cuillinane, K.P.B., 1992. A short term adaptive forecasting model for BIFFEX speculation: a
Box–Jenkins approach. Maritime Policy and Management 23, 103–114.
Kavussanos, M. and Nomikos, N., 2002. Price discover, causality and forecasting in the freight
futures market. Review of Derivatives Research 6, 203–230.
Kavussanos, M. and Visiviki, I.D., 2006. Shipping freight derivatives: a survey of recent evidence.
Maritime Policy Management 33, 3, 233–255.
Menard, S.W., 2001. Applied Logistic Regression Analysis (Quantitative Applications in the Social
Sciences), 2nd edn, Sage Publications, Inc., Thousand Oaks, CA.



Leading Indicators for freight rates

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