Microeconomic Determinants of Dry Bulk Shipping Freight Rates and Contract Times

lasagnaseniorManagement

Oct 28, 2013 (3 years and 9 months ago)

176 views

1



Microeconomic Determinants of Dry Bulk Shipping Freight
Rates and C
ontract

Times


Amir H
.

Alizadeh

Cass Busi ness School

Ci ty Uni versi ty

London EC1Y 8TZ

Uni ted Ki ngdom

a.al i zadeh@ci ty.ac.uk


Wayne
K.
Talley

Col l ege of Busi ness and Publ i c Admi ni strati on

Ol d Domi ni on Uni versi ty

Norfol k, Vi rgi ni a 23529

USA

wktal l ey@odu.edu







ABSTRACT

The aim of this paper is to analyze vessel specific and voyage determinants of shipping freight rates in the
dry bulk shipping market. Differences in freight rates across major dry bulk shipping routes, geographical
distribution of shipping activities arou
nd the world, and the duration of the laycan of shipping contracts
are also investigated. While the literature has established macroeconomic determinants of shipping freight
rates, there has been no systematic investigation of the microeconomic determinant
s of shipping freight
(charter) rates. A large sample of individual dry bulk chartered contracts from January 2003 to July 2009
is used to investigate determinants of the freight rates and laycan periods in dry bulk shipping contracts.
Estimated results of

a system of simultaneous
-
equations suggest that the laycan period and dry bulk
freight rates are interrelated and determined simultaneously. Furthermore, vessel deadweight, age and
voyage routes are also important factors in formation of freight rates. De
terminants of the laycan period
are the former determinants as well as the Baltic Dry Index and its volatility.


Key words
: Freight Rate, Shipping, Dry Bulk,

Laycan, Simultaneous System



2


1. Introduction

International sea transportation has been an instru
mental factor in world economic
activity
and
international trade.
T
otal seaborne trade in commodities reached an estimated 8,226 million metric tonnes
(mmt) in 2008


consisting of
3,060 mmt
of
dry bulk

commodities
, 3,052
m
mt
of
liquid bulk
commodities, an
d 2,114 mmt
of
other dry cargo and manufactured goods.
1

T
he dry bulk shipping market
is by far the largest sector of the world
’s

shipping
market
in terms of
cargo
volume and weight.

In 2008
the dry bulk shipping fleet transported 843 mmt
of
iron ore, 794 m
mt
of
coking and thermal coal, 314
mmt
of
grains, 119 mmt of bauxite, alumina and phosphate rock, and 990 mmt of minor dry bulk
commodities
, e.g.,
cement, sugar,
and
fertilizers
.

A
t the end of 2008 the cargo carrying capacity of the
world dry bulk
shipping

fleet of 418 million
metric
tonnes was 34.7% of the total world shipping flee
t,
and
the number of dry bulk ships exceeded 7,000.
2

Therefore, it is not surprising that a large number of
studies have
investigated
the formation and behavior of dry bulk freig
ht (charter) rates, chartering
decisions and policies, transportation strategies, and fleet deployments and operations of the dry bulk
shipping industry

(See section 2).

The existence of different types of ship chartering contracts in the bulk shipping industry provide
charterers greater flexibility to secure their sea transportation requirements, while minimizing their costs.
The contracts vary depending on the terms of

agreement and the type of service that shipowners agree to
provide to charterers. Broadly speaking, chartering contracts can be classified into five different types:
Voyage Charter (VC), Consecutive
V
oyage or Contracts of Affreightment (CoA), Trip Charter

(TC),
Time or
P
eriod
C
harter (PC),
and
Bareboat
C
harter (BC)

contracts
.

The main differences among these
contracts are the: duration of the contract, method of freight rate calculation, cost allocations and
commercial and operational responsibilities
3
.

F
reight rates in the bulk shipping industry fluctuate considerably in
the
short
run

(Kavussanos, 1996a).
Such fluctuations affect the formation of shipping policies, transactions and contracts and ship owners’
and charterers’ cash flows and costs (Brown et
al. 1987 and
Laulajainen, 200
7
).
Hypothesized
macroeconomics determinants of shipping bulk freight rates include
the
state of the
general world
economy, international seaborne trade, the tonnage available for trading, bunker prices, and the changes
in flee
t due to delivery of newbuldings and sending vessels for scrapping (Strandenes 1984, Beenstock
and Vergottis, 1989). In addition, freight rates are dependent on such
vessel characteristics
as
size and age
of vessel
s,

the route in which the vessel is employ
ed

and the terms of charter contracts
.
T
he terms and
conditions of the charter contract
,

e.g., the
loading date in relation to
the
contract date and
the
cargo size
in relation to vessel’s capacity
,

are
also
determinants of freight rate
s
.

There are two
important dates i
n any vessel charter contract
.
T
he

d
ay on which negotiation
s

between
the
ship

owner and the charterer
are

completed

and the charter party contract is signed
,
i.e., the
fixture
date
or
the
hire

date

and t
he da
y

that the
ship must present he
rself at the loading port ready for
the
loading
of
cargo

(or the delivery day to be delivered to the charterer)

known as the
layday
. The time window
within which the vessel must be presented for loading or be delivered to the charterer is also known as the

layday/cancellation (or laycan) period. If the vessel is presented ready for loading cargo after this time



1

St
atistics are from Clarksons Research Services (Shipping Intelligence Network).

2

Statistics are from Clarkson Research Services Limited for dry bulk carriers greater than 10,000 deadweight tonne capacity.

3

See Stopford, 2009, and Alizadeh and Nomikos, 20
09, for more details on differences among shipping contracts.

3


window, then the charterer has the option to cancel the contract, and potentially claim compensations.
Thus, the time period between the fixture dat
e and the

layday

is the
laycan period
.

O
nce

the

loading or lifting da
y

(layday)

of the

cargo

has been determined
,
the trader (vessel charterer)
will enter
the market to find and charter the most suitable
ship
for transportation of the cargo from
the
loadi
ng port to the destination

port
. Depending on the nature of the trade, the
layday

may

occur
anytime
from
one

day to
couple of

months
after the fixture date.
Hence, assuming vessels are available in the
market at all time and at a constant flow, the trader has the option to enter into the freight market and hire
a vessel anytime until the very last minute
(
as long as it is practical
)

before
the
layday.

Therefor
e, it is the
trader
’s

decision
for all practical purposes
as to when to enter the market and
charter (or hire)
a
ship
. For
instance,

if the conditions are not favo
rable and there is enough time
before the
layday
,

the
trader may
wait and not inform the
ship
broker about the need for a ship.

The charterer’s decision of when to charter a
ship i.e., the fixture date, is dependent on such market conditions as current and expected freight (charter)
rates, the volatility of freight rates, and the cost to be incurre
d of not being able to
find

a ship to charter if
the decision to hire a ship is delayed.

Although numerous
macroeconomic
studies on the formation and behavior of shipping freight
(charter) rates exist,

there has been little investigation of microeconomic
determinants, such specific
vessel and voyage determinants, of shipping freight (charter) rates. Moreover, there is no study in the
literature that investigates determinants of the laycan period in shipping contracts.
This paper attempts to
fill this gap n
ot only with respect to shipping freight
(charter)
rates but also with respect to the
laycan
period o
f
ship
charter contracts
.

In addition, the paper analysis, for the first time the relationship between
freight rate and the laycan period of shipping contr
acts.


The purpose of this paper is threefold: (1)
investigate vessel and voyage determinants

(e.g., vessel age,
hull type, deadweight size of the fixture and route)
of individual dry bulk
shipping freight
(charter)
rates
;
(2) investigate vessel and voyage

determinants of
the individual delivery times of chartered ships (laycan
periods); and (3)
investigate
the relationship between
dry bulk shipping freight (charter) rates and
laycan
period
s

of
individual
charter contracts
. To the knowledge of the authors, this
paper is the first to appear in
the literature to undertake such investigations, i.e., to investigate
microeconomic determinants of
dry bulk
shipping
freight rates and
laycan periods.


When charterers enter
the marke
t
to fix vessels, their decision
may affect
market
demand and
consequently freight rates.
However,
waiting for more favorable freight rates may be risky as freight rates
tend to move very sharply in
a
very short period of time.
From the shipowners’ point o
f view, information
on the role of vessel and voyage specific factors in the determination of freight rates can be used in their
shipping investment, operations, and deployments strategies.

The following s
ection
of the paper
present
s

a review of
the literature
on
determinants of bulk
shipping
freight
(charter) rates
. Section 3 present
s

the methodology (i.e., econometric equation specifications) for
investigating vessel and voyage determinants of individual freight (charter) rates and laycan period
s for
bulk shipping contracts
. The data are
described
in section 4,
while

section 5 presents the empirical results
.
Finally,
conclusions are presented in
section 6
.



4


2.

Review of literature

Like an
y market, dry bulk freight (charter)
markets are characterize
d by the interaction of supply and
demand


for this market the supply and demand for charter dry bulk ships (Stradenes 1984, Beenstock
and Vergottis 1993, and Tsolakis 2005)
. The demand for
charter dry bulk ships
is a derived demand
which depends on the economics of the
commodity

markets and
international seaborne
trade, world
economic activity
such as
imports and consumption of energy commodities (see Stopford 2009).
The
s
upply of
charter dry bulk ships
, on the ot
her hand, depends

o
n
the size of the shipping fleet, the fleet’s
tonnage
that is
available for trading, shipbuilding activities,
bunker
fuel prices,
the
scrapping rate of the
fleet

and the productivity of the shipping fleet at any point in time
.
Heretofore
, studies analyzing,
modeling and forecasting
freight
(charter)
rates

have done so from a macroeconomic perspective.

Studies by
Hawdon
(
1978
)
, Strandenes
(
1984
)
,
and
Beenstock and Vergo
ttis (1989, 199
3
)
,
among
others,
argue that
the shipping
freight
(cha
rter)
rate is
determined
through the interaction between supply
and demand
for sea transportation. T
hey find that world economic activity
, the growth in

industrial
production
,

seaborne trade in commodities, oil prices, availab
i
l
ity

of
tonnage

or stock of f
leet
, new

vessel
building
s

on
order,
and shipbuilding
deliveries

and scrapping rates

determine
freight rates

for sea
transportation
. More recent studies by Dikos et al. (2006) and Randers and Göluke (2007) also use
macroeconomic variables in a system dynamic setting to model and forecast freight
(charter)
rates.


Other studies
have
examine
d

the time series properties
of shipping

freig
ht
(charter)
rates such as their
dependence on past values
;

further, they
use univariate or multivariate time series models to capture the
dynamics of freight rates. These models are then used
to
forecast shipping freight
(charter)
rate
s

and their
volati
li
ties (Veenstra and Franses, 1997, Kavussanos and Alizadeh, 2002,
Adland and
Cullinane
, 2005
and 2006,
Lyridis

et al., 2004, and Batchelor et al., 2007
).
Moreover,
studies such as Kavussanos and
Alizadeh (2001) investigate the seasonal behavior of dry bulk
shipping freight (charter) rates and explain
how seasonal production, trade and transportation of commodities impact ship charter rates.
T
hese
studies
utilize
macroeconomic
economic data in an
attempt to capture the dynamics and fluctuations in
shipping
freight
(charter)
rates
; however,
their performance in predicting shipping freight rates has been poor and
inaccurate at best. The poor and underperformance of
structural and time series
model
s

using
macroeconomic variables
for
forecasting shipping freight

(charter)
rate
s

may be attributed to an
aggregation bias. In addition to macro forecasts of shipping freight rates,
ship

owners and charterers
also
use micro forecasts for
cash flow analys
e
s, budgeting, and making operational decisions
, e.g., micro
foreca
sts of shipping freight (charter) rates for specific routes.

Micro vessel variables as determinants of shipping freight (charter) rates are found in studies by
Tamvakis (1995) and Tamvakis and Thanopolou (2000).
Tamvakis and
Thanopolou (2000)

investigate

vessel age as a determinant
of a two
-
tier dry
-
bulk ship charter market
. The
empirical results based upon
the time period 1989 to 1996 (that covered different stages of the shipping cycle)
,

however,
found no
significant difference between freight
(charter)

rates paid for
newer
versus older
vessels
. Laulajainen
(2007)
has investigated
differences in shipping freight rates and operational profitability
for
different
shipping routes. He
concludes
that
the
ratio of demand
to
available
ship
tonnage, weighted by
sailing
distance to a discharging/loading region
,

is an
important factor in explaining dry bulk freight rates for
individual routes.



5



3.

Methodology

The model’s dependent variable for investigating determinants of shipping freight (charter) rates is
defined

as the difference between an individual shipping freight rate and the value of the Baltic Index
freight rate for that specific class of vessel on the fixture date. Since Capesize and Panamax fixtures are
considered, the Baltic Capesize Average Four Trip
-
Charter and the Baltic Panamax Average Four Trip
-
Charter Rates are used. These freight indices are used by the industry to monitor the overall shipping
market movements, to trade and settle freight derivatives
4
, and to benchmark operating performance of a
vessel or a fleet. Therefore, it can be argued that the Baltic Average 4TC Rates reflect the movement of
the ship freight charter market with respect to changes in macroeconomic factors. Consequently, the
difference between the freight rate for a particula
r charter contract and the Baltic 4TC Rates, at any point
in time, should reflect the factors specific to the vessel or the voyage under which the vessel is contracted
to operate. In other words, the difference between a single fixture rate and the benchma
rk index rate
should be a function of the route over which the vessel operates,
RT
, the laycan period of the fixture,
LC
,
the size of the vessel,
SZ
, the age of the ship,
AG
, and the volatility in the market,
VOL
. Thus, the
d
eterminants of freight rates fo
r dry bulk trip charter contracts are investigated using the following
hypothesized freight (charter) rate regression model:


(
1
)



w
here

is the difference between the log of fixture rate for contract
i

at time
t
,

fr
i
,
t
,
, and
the log of

Baltic benchmark freight rate (Baltic Average 4TC Rates) at time
t
,
bfi
t
=ln(B4TC
t
)
. The

variable, age
-
squared, appears in equation (1) since the operational performance, technological efficiency
and quality standard of ships decline, as they get older, and consequently their hire rate is assumed to be a
nonlinear function of age. T
he dummy
variables for
individual
routes
,

RT
i,j
,

also appear in equation (1)
.


Determinants of charterers’ decisions on the timing of trip
-
charter hires

are investigated by
regressing
the
fixture
laycan period
on the hypothesized explanatory
variables
:

route
, dry
bulk market
condition, and vessel characteristics such as vessel size and age. The laycan regression model is specified
as follows:


(
2
)


Since during the process of hiring a vessel, freight rates and other terms of shipping contracts are
negotiated and agreed at the same time,
it might be the case that freight rates and laycan periods are
interrelated and should be modeled as a system of si
multaneous equations. If so,
individual
estimation
s

of
the above two equations using the Ordinary Least Squares (OLS) method will yield biased and
inconsistent parameter estimates.
Whether a simultaneous relationship exists between freight (charter)



4

See Alizadeh and Nomikos (2009) for more detail on Baltic Indices and freight derivatives trading.




6


rates
and laycan periods can be investigated by using the Hausman (1978) test for simultaneity.
The
Hausman test is performed using the two step method of Davidson and Mackinnon (1993), i.e., by first
estimating freight equation

(
1
)

and then using the estimated residuals as observations for the explanatory
variable

in the laycan equation. The significance of the coefficient of residuals of the frei
ght
equation in the laycan equation is an indication that there is a simultaneous relationship between the
laycan period and freight rate of shipping contracts. In addition, the test can be performed by first
estimating laycan
equation
(
2
)

and then using the estimated residuals as observations for the explanatory
variable

in the freight equation. Again, significance of the coeffic
ient of residuals of the laycan
equation in the freight model can be evidence of the existence of a simultaneous relationship between the
two variables.


If a simultaneous interrelationship exists between the
freight rate and the
laycan period of a shippin
g
contract, then equations
(
1
)

and
(
2
)

must be re
-
specified to reflect this simultaneous interrelationship


i.e., by including
LC
i
,
t

as an explanatory variable in the d
fr
i
,
t
equation
(
1
)

and including d
fr
i
,
t

as an
explanatory variable in the
LC
i
,
t

equation
(
2
)
. Therefore, we define the following system
:





(
3
)


w
here

i
is the vector of residuals which follows a bivariate distribution with zero mean and variance
-
covariance matrix,

.

††
When speci晹ing a system o映 simultaneous equations, the牥 a牥 two main points that should be
conside牥d.The晩牳tissueisthep牯blem

o映identi晩cation,whicha物seswhenpa牡mete牳o映thest牵ctu牡l
equationcannotbededucedusingthepa牡mete牳o映the牥duced景牭model.The牥景牥,景爠asystemo映
simultaneousequationstobeidenti晩ed,boththeo牤e爠andthe牡nkconditionsshould

besatis晩ed.
5

The
system of equations presented in
(
3
)

satisfies both the order and the rank conditions.


The second issue is the estimation me
thod. Because of the correlation among explanatory variables
and error terms in each equation as well as cross correlation between error terms, the OLS estimation
method is not an appropriate estimation technique for estimating parameters of a system of si
multaneous
equations. An appropriate estimation technique is the Three Stage Least Squares (3SLS) estimation
technique, since it y
ields
unbiased, consistent and
efficient estimates
of the parameters of the
system
in
the presentence

of

heteroskedasticity an
d contemporaneous correlation in the errors across equations

(Gujarati 2002)
.






5

There are two conditions which must be satisfied for the system of simultaneous equation
to be identified. These are the order
and the rank conditions of identifications. The order condition, which is a necessary but not sufficient condition for identi
fication
of simultaneous system of equations, requires the number of excluded and predetermin
ed variables in each equation to be at least
less than the number of endogenous variables minus one. The two equations in the system defined in
(
3
)

satisfy this condition;
that is, the freight equation is exactly identified and the laycan equation is over
-
identified. The over
-
identification is not a major
problem because we use the 3
SLS
estimation method,

which yield efficient, unbiased and consistent estimates.




7



4.

Description of Data

The
world’s dry bulk

fleet of ships
is
broadly
differentiated into f
ive size classes: Handysize (20,000
to 35,000 dwt), Handymax (35,000 to 45
,000

dwt), Supramax (45,000 to 55
,000 dwt),
Panamax (60,000
to 8
0,000 dwt) and
Capesize (more than 8
0,000 dwt, normally
120,000 to 18
0,000 dwt)
.
6

Capesize

bulk
carriers

are
almost exclusively
involved in
transportation of major dry bulk commodities, i.e., iron

ore and
coal, between exporting and importing regions. Panamax vessels are also involved in transportation of
iron ore and coal in addition to grain
.
Midsize dry bulk carriers, using Supramax and Handymax vessels,
are involved in transportation of grain,
bauxite and alumina, and phosphate rock, in addition to minor
bulk commodities. Handysize and smaller bulk ships are usually equipped with cargo handling gears
(cranes) and transport small
-
shipment
-
size bulk commodities between ports with relatively shallo
w water
depths.

The data for this study
were

collected from Clarkson’s Research S
ervices Ltd

website, Shipping
Intelligence Network (SIN)
, and

comprise information on
Panamax and Capsize trip
-
charter

fixtures
over
the

period January 200
3

to
July

2009.
The

larger
-
size
d

Panamax and Capsize ships were selected for the
study, since
the trading routes for these ships are distinct and
their
trading activity is concentrated in
a
number of

major shipping routes



thereby simplifying the empirical analysis of this
paper
. The trading
routes for smalle
r bulk ships are very

scattered
,

since the market for these vessels are quite fragmented
,

e.g., their trade routes utilize
almost any combination of seaports.

Around 45% of spot market activities in
the Capsize sector are trip
-
charter with the remaining contracts being voyage charter contracts. In the
Panamax sector, more than 90% of the spot fixtures are based on trip
-
charter contracts.
The data include
inform
ation on vessel characteristics, voyage characteristics, shipowners, and charterers. After filtering

the data for missing values,

omitted information, and other unusa
ble observations, a total
of 3,039 and
9,076

fixture
s

observations
remain for
Capsize and
Panamax dry bulk ships,

respectively.
7

Specifically,
the information on each fixture consists of the vessel’s name, size, age, type, dwt, and the owner of the
vessel as well as the delivery and redelivery locations or regions, the freight rate for the fixt
ure, the
fixture’s date, the layday, the cargo type and size, and the charterer.

The Baltic Capesize and Panamax freight indices (earnings) were also collected from
Clarkson’s
Research S
ervices Ltd

website
. These two freight series, which are compiled and

reported by the Baltic
exchange, are based on the average of reported trip
-
charter fixtures on a daily basis on four major routes.
These routes are the most active Baltic trade routes and cover most of the Baltic fixtures (see Table 1).
The detail and def
inition of these series can be found in the Baltic Exchange website.

The trade routes for Capsize and Panamax dry bulk carriers utilized in the empirical analysis are
found in Table 2. The first four routes listed for both Capsize and Panamax carriers are
Baltic trade



6

In
January 2009
, the
dry bulk fleet
was
comprised of 820 Capesize (142.5 million dwt), 1,556 Panamax (114.51 million dwt),
1708 Handymax (82.93 million dwt), and 2,927 Handysize

(78.14 million dwt), vessels. The Supramax fleet is relatively small as
this a new class of dry bulk carriers. The percentage of dry bulk fleet in terms of cargo carrying capacity is Capesize 34.1%
,
Panamax 27.4%, Handymax 19.8%, and Handysize 18.7%.


7

The data used here is dry bulk trip charter contracts by observations every day. Therefore, although the structure of the dat
a can
be considered both cross sectional and time
-
series, the difference here is that the number of observations every day may var
y
depending on the number of actual fixtures reported.

8


routes
. The non
-
Baltic
routes
for Panamax carriers
are: Mediterranean to Far East, PG
-

Indian Ocean to
Far East, Far East to PG
-
Indian Ocean, Continent to PG
-
Indian Ocean,
and
PG
-
Indian Ocean to
Continent
.
See the Appendix for further descrip
tion of these routes.
The

routes identified represent about
86.8
% of the observed trip
-
charter fixtures; the remaining fixtures are for
numerous
minor routes with
less trading activities, while the four main Baltic routes represent 67% of the trip
-
charter
fixtures
,

thus
indicating
how concentrated the market is.

Table
2
2

also presents descriptive statistics for these routes via vessel type with respect t
o the number
of fixtures and the laycan period. It is interesting to note that the average (or mean) and the standard
deviation of the laycan period are directly related to vessel size. For instance, the average and standard
deviation of laycan periods for

Capesize fixtures are 7.5 and 6.7 days, respectively, while the average and
standard deviation of the laycan period for Panamax fixtures are 4.4 and 4.6 days, respectively.
The
average laycan period for the Baltic routes (first 4 routes) also seem
s

to be
longer for Capesize vessels
compared to Panamax ships.
In addition, the most liquid route seems to be
the
Trans Pacific Round
Voyage route
for both vessel type
s
,
with 54% and 35% of the activity in the Capesize and Panamax
sectors, respectively.


The descriptive statistics
for the
variables



vessel age,
vessel
size,
Laycan period,
freight rate and the
Baltic
Average 4TC Rates


are reported in
Table
3
2
. The results reveal similar average age
s

and standard
deviation
s

for both Capesize and Panamax fleets
.
As expected
,

the average Capesize vessel has a
deadweight capacity of 165,000 mt, with
standard deviation of 14,900 mt, while
the
average deadweight
capacity of Panamax vessel
s

is 72,300 mt, with a standard deviation of 3,900 mt.
The largest Capesize
vessel fixed over the sample period has a 301,800 dwt capacity. The average Capesize trip
-
c
harter rate
over the sample was 66,964 $/day, with a standard deviation of 47,611 $/day. The maximum Capesize
trip
-
charter rate was 303,000 $/day, and the minimum rate was 1,000 $/day. The average trip
-
charter rate
for Panamax ships was 32,001 $/day, with
a standard deviation of 20,877 $/day. The maximum freight
rate was 125,000 $/day, and the minimum rate was 1,000 $/day. These statistics illustrate the existence of
very high volatility in the dry bulk shipping markets that has been documented in the liter
ature. The
descriptive statistics of the Baltic Average 4TC Rates reveal an average earning of 72,451 $/day with a
standard deviation of 46,378 $/day for Capesize vessels and an average earning of 32,506 $/day with a
standard deviation of 20,189 $/day for
Panamax vessels.
Finally, the Jarque and Bera (1980) tests indicate
that distributions of all variables are significantly different from the normal distribution.


5.

Empirical Results

The data described above are used to estimate the parameters of equation 1

and 2 independently of
each other for the purpose of investigating determinants of freight (charter) rates and the laycan period of
dry bulk shipping fixtures. This investigation is also undertaken by simultaneously estimating the
parameters of the simult
aneous
equation set
(
3
)

assuming that the laycan period and freight rates of dry
-
bulk fixtures are interrelated.
The
Baltic Average 4TC


rates for Capesize and Panamax vessels are used
as measure of the freight rate levels, while volatility of these indices (VOL) are estimated
as
exponentially weighted average variances of Capesize and Panamax freight
rates (average 4TC)
over the
sample pe
riod.

9




5.1 Determinants of freight rates


Separate OLS estimates of equation
(
1
)

for the two dry bulk vessels


Capesize and Pan
amax


are
presented

i
n
Table
4
.
Diagnostic tests of the residuals
, including the Breusch and Godfry test for serial
correlation and the White test for heteroscedasticity,

s
uggest that the residuals
of
all models
are non
-
spherical. Therefore, the Newey
-
West (1987)

method is applied to correct
the standard errors of estimated
coefficients.

The results suggest that in both the Capesize and Panamax markets, the dependent variabl
e,
which is the difference between the freight rate for individual fixtures and the Baltic Indices (market rate),
can be explained by the laycan period of the contract as well as by the size and age of the vessel. The
significant and positive coefficients
of the laycan and vessel size variables suggest that there exist a
positive relationship between these two variables and the freight differential (d
fr
i
,t
). Estimated coefficients
of vessel age and vessel age
-
squared in the model suggest that there is a negative and nonlinear
relationship between the age of the vessel and hire rate for dry bulk vessels. Moreover, coefficients
measuring the impact of market
volatility on freight rate differential are not significant in both the
Capesize and Panamax markets. Moreover, in the model for Capsize freight rates, significant and positive
estimated coefficient of the route dummy variable,

2
, indicate that on average

freight rates in route
C9_03

(
European Continent to Far
East) is
26.85
% higher than the Baltic Capeszie Average 4TC
Rate
, while
significant and negative

coefficients of route dummy variables,

4
, indicate that freight rates in route
C
11
_03

(
Far East to th
e European Continent)

on average are 32.3% lower than the Baltic Capeszie 4TC
Rate. This is expected because routes C9_03 is considered to be a front
-
haul route, while C11_03 is
considered to be back
-
haul route, and in general back
-
haul routes trade at a d
iscount to front
-
haul routes.

In the
P
anamax market, estimated coefficients for all route dummy variables are significant, which
means that there are significant differences in freight rates amongst the routes. For instance, the results
reveal that Panama
x vessels operating in routes P1A and P2A tend to earn on average 3.68% and 14.94%
higher than the Baltic Panamax Average 4TC
Rate
, respectively. On the other hand, vessels trading in
route P3A and P4 tend to earn on average 9.1% and 17.09% lower than the
Baltic Panamax Average 4TC
Rate
, respectively. Furthermore, vessels operating in routes 5 (Mediterranean to the Far East), 6 (PG
-

Indian Ocean to the Far East),
and
8 (Continent to PG
-
Indian Ocean) earn on average 24.74%, 8.09%, and
19.0% higher than the B
altic Panamax Average 4TC
Rate
, respectively. Whereas, vessels operating in
routes 7 (the Far East to PG
-
Indian Ocean) and 9 (PG
-
Indian Ocean to Continent) earn on average 7.57%
and 15.04% lower than the Baltic Panamax Average 4TC
Rate
, respectively. The
refore, considering that
these routes are pairs and generally represent round trips, it can be observed that while vessels trading on
routes to the Far East demand a premium, vessels trading in routes out of the Far East and back to the
European continent
(routes 7 and 9) are generally traded at a discount.

Finally, the coefficient of goodness of fit, measured by R
-
bar
-
squared of the regression model,
indicates that 58.07% and 34.6% of the difference in fixture freight rates and Baltic Average 4TC Indices

for the Capesize and Panamax vessels, respectively, can be explained through vessel and voyage specific
factors such as laycan period, vessel size, age and the trading route.


10


5.2
Determinants of Laycan Period

Separate OLS estimates of
Laycan
equation (2) for Capesize and Panamax
vessels
are found in Table
5.
In both equations, estimated standard errors of parameters are corrected for the presence of
heteroscedasticity and serial correlation using the Newey and West (1987) method.
Starting with

the
Capesize estimate, it can be seen that there is no significant difference between the laycan periods of the
Capesize charter contracts since coefficients of all dummy variables are not significantly different from
zero. The only exception is
the estim
ated coefficient for the
transpacific route in which the laycan period
is on average 1.38
days
shorter than other routes. The estimated coefficients of other explanatory
variables in equation (2) reveal that laycan period of trip
-
charter contracts are posi
tively related to
the
freight differential (d
fr
i,t
) and log of
Capesize
Baltic 4TC average
,
bfi
t
. This is expected because a higher
freight rate level is an indication that there is shortage of supply. As a result, charterers tend to hire ships
earlier wi
th longer laycan period in order to avoid future freight
rate
increases and
the
possibility of
incurring extra cost due to unavailability of tonnage.

Negative and significant coefficients of
vessel
size and age in the laycan equation for capsize vessels
i
ndicate that there is negative relationship between
the
duration of
the
laycan and
vessel
size as well as
vessel
age. In other words, larger vessels and older vessels tend to be hired later than
smaller and newer

C
apsize vessels, everything else being equa
l. This is also expected, because if the charterers have a choice
between a modern and an old ship with similar freight rates, the
newer
vessel will be preferred to the
older ship
;

at the same time, a smaller
C
apsize
vessel
is preferred to larger one
,

sinc
e
the
daily freight rate
of the former will be less than that of the latter.
Of course, the size of the cargo to be loaded on the vessel
is the most important factor in determin
g

the size of the vessel chosen
for hire
by the charter. Finally, the
results r
eveal a negative and significant relationship between the duration of the laycan period of trip
-
charter contracts and
the
volatility of capsize freight rates. Such a relationship can be attributed to the fact
that everything else being equal, the greater t
he uncertainty in the market the later the charterer would like
to fix tonnage (ships) for their transportation requirements. This can be explained through the option
valuation theory in the sense that higher uncertainty will increase the value of the opti
on to wait or delay
fixing a ship. Consequently, charterers realize such a value and try to find the optimum time for execution
of the contract, which means longer delay or waiting time and
a
shorter laycan period when the freight
market is volatile.

Tur
n
ing to the estimation results of (2) for determina
nts

of
the
laycan period of charter contracts for
Panamax
vessels
reported in Table 5
,

i
t can be seen that there are significant differences amongst the
laycan periods of the Panamax charter contracts as indicated by
the
estimated coefficients of
the
route
dummy variables. For instance,
the
laycan period
for
contracts in routes P1A
_03
, P2A
_03

and P3A
_03

of
the Baltic as well as routes 7 (Far East to PG
-
Indian Ocean) and 8 (Continent to PG
-
Indian Ocean) are on
average shorter than fixture in other routes. At the same time,
the
duration of
the
laycan period of charter
contracts in routes 6 (PG
-
I
ndian Ocean to Far East) is on average marginally longer than other routes by
almost a quarter of a day. Finally,
the
duration of
the
laycan periods of charter contracts in routes P4
_03

(Far East to the Continent), 5 (Meditranean to Far East), 6 (PG
-
India
n Ocean to Far East), and 9 (PG
-
Indian Ocean to the Continent) are not significantly different from the average of the laycan period of the
contract in the
sample.

11


The estimated coefficients of other explanatory variables in equation (2) reveal that the
laycan period
of trip
-
charter contracts in the Panamax market are also positively related to
the
freight differential (d
fr
i,t
)
as well as the log

of the benchmark Baltic Panamax 4TC average
,
bfi
t
. As explained earlier, charterers
tend to hire ships earlier

when a
longer laycan period
exists
in order to avoid future freight increases,
when they expect
a
shortage of supply and higher freight rates due to unavailability of
vessel
tonnage. In
contracts to what was observed in the Capesize model, insignificant coefficients of size and age in the
laycan equation for Panamax vessels suggest that there is no relationship between
the
duration of
the
laycan and
vessel
size as well as
vess
el
age
.

Moreover, in line with what was observed in the Capesize
market, the results reveal a negative and significant relationship between the duration of the laycan period
of trip
-
charter contracts and
the
volatility of Panamax freight rates
. Finally, th
e estimated coefficients of
goodness of fit of 5.77% and 3.41% for Capesize and Panamax laycan equations, respectively, suggest
that only small proportion of the variation of laycan period can be explained by variables such as
the
freight rate level, volat
ility, size and age of the vessel.



5.3 Simultaneous
-
Equation Determinants of freight rate and laycan period


The estimated coefficients of the simultaneous system of equations (3) for Capesize and Pananax
freight (charter) rates and laycan periods u
sing the 3SLS estimation method are reported i
n
Table
6

and
Table
7
, respecti
vely. The results are qualitatively similar to those for the single equation models of the
freight rate and the laycan period
--

although estimation of the two equations as a system of simultaneous
equations allows interaction between the dependent variab
les and yield more efficient estimates of
parameters due to higher degrees of freedom.

Estimated coefficients of route dummy variables in the f
reight
rate
equation reveal that only 2 of the 4
route dummy variables are statistically significant at the 1%

level


Continent to Far East (route C9_03)
and Far East to Continent (route C11_03). The freight rate in route
C9_03 is
26.75% higher than
for

Capesize
4TC capsize vessels, while
the
freight rate in route C11_03 is 32.13% lower than
that for
Capesize
4T
C. Th
ese

result
s

are
similar to those observed in
the
single equation model
s

and signif
y

the
argument that freight rate
s

in the back
-
haul route from the Far East to the Europe
are

significantly lower
than front
haul routes from Europe to the F
ar East. The
coefficient
s
of route dummy variable for routes
C8_03 (transatlantic round voyage) and C10_03 (transpacific round voyage) are not significantly different
from zero at the 5% level.


There is a significant between the freight rate and vessel size and age. A

positive relationship exists
between freight rate and vessel size, while a negative and quadratic relationship exists between the freight
rate and vessel age, similar to what was observed in the single equation model. The positive coefficient
for the layc
an period suggests that the freight (charter) rates increases as the laycan period increases. The
coefficient of the laycan period indicates that for every one day increase in the laycan period, the freight
rate increases by 0.47%. The estimated coefficien
t of freight market volatility is not significant.

The estimated coefficients of the route dummy variables in the laycan equation are all negative, but
only the coefficient for route C9_03 (Continent to Far East) is statistically significant, indicating a

relatively shorter laycan period for capsize fixtures in this route compared to other routes. The coefficient
of freight rate, d
fr
i,t

, is positive and significant, suggesting that the laycan period increases when the
12


freight rate increases. The positive
and significant coefficient of the Baltic 4TC Rate suggest that
charterers tend to hire vessels earlier when freight rates are high. The estimated coefficient of freight
market volatility is negative and significant. Furthermore, the coefficients of vessel

size and age are both
negative and significant in the laycan equation, suggesting that the laycan period for larger and older
Capesize vessels is shorter than for smaller and newer vessels. This is to be expected as noted by the fact
that newer and more m
odern vessels are fixed earlier than older vessels.


The estimation results of the Panamax freight rate and laycan period simultaneous equations also
reveal similar results to those of the single equation models. Once again, freight rates differ among
Pana
max routes. Vessel age and size are significant determinants of freight rates, Larger panamax vessels
command higher freight rates than smaller Panamax vessels and freight rates decrease non
-
linearly in
relation to vessel age. The laycan period is also a s
ignificant determinant of the Panamax freight rate; the
freight rate is higher when the vessel is hired early as opposed to being hired later.

As for the single equation model, the results for the determinants of the Panamax laycan period for
the simult
aneous equation model

reveal that the
laycan period
varies among
routes
.

and these differences
are consistent with those observed in single equation model of laycan period. Furthermore, the laycan
period for
P
anamax vessels
is
positively
related to the
fr
eight
rate, the
Baltic 4TC freight rate, and age of
the vessel.
Among these three determinants of the laycan period
, only the
coefficient
sign
for vessel age
is
not consistent with what was
found for
Capesize
laycan period
.
The positive coefficient for ve
ssel age
suggest
that older
Panamax
vessel
s

have a longer laycan period
than newer
panamax
vessels
.

The

negative and significant coefficient of freight market volatility once again confirms that there is
an inverse relationship between market volatility and laycan period
for

charter contracts.
This result is
consistent with real option valuation theory in t
he sense that as volatility increases the value of the option
to wait increases, i.e., charterers tend to delay fixing vessels. On the other hand, as freight rates (or the
value of the underlying asset) increase, the value of the option to wait decreases a
nd charterers tend to
enter the market earlier to hire vessels.


6.

Conclusions

The purpose of this paper has been to investigate vessel and voyage determinants (e.g., vessel age,
size, and the fixture route) of individual shipping freight (charter) rates and laycan periods for
dry bulk
charter contracts

as well as to investigate
the
relationship between these rates and the laycan periods for
these contracts. Data for the investigation were obtained from Clarkson

s Research Studies website and
comprise information on dry bulk trip
-
charter fixtures for the period January 2006 to April 2
009 for
Capesize and Panamax vessels.


The investigation reveals several important findings: First, freight (charter) rates are positively related
to the length of the laycan period and the size of the vessel (dwt). Second, a simultaneous relationship

exists between freight (charter) rates and lengths of laycan periods. Third, dry bulk freight rates and
laycan periods vary across shipping routes. Fourth, the laycan periods of freight contracts vary directly
with freight (charter) rates and indirectly w
ith freight rate volatility. This is expected, since higher freight
rates generally reflect a lower availability of dry bulk tonnage


therefore, vessel charterers anticipating
13


shortages will seek to enter the charter market earlier, i.e., to fix their tra
nsportation requirements well in
advance. When the volatility and uncertainty of freight rates increase, charterers are expected to delay
their hiring of vessels as long as it is feasible to wait for the market to stabilize and avoid paying a
premium to fi
x a vessel. Finally, laycan periods for trip
-
charter contracts in the dry bulk market are
negatively related to the age of vessels, i.e., newer ships have longer laycans than older ones.


Appendix

In the capsize market, route
C8_03 is a transatlantic rou
te with delivery and redelivery in Europe
(Gibraltar to Hamburg range). Route C9
_03

is a trip from Europe to the Far East with delivery in Europe
or Mediterranean and redelivery in the Far East. Route C10
_03

is a transpacific round trip with delivery
and r
edelivery in the Far East for trips to Australia, North Pacific, or even South Africa or India. Route
C11
_03

is a trip from Far East to the Continent Europe via South Africa or Australia.
The
Baltic
Exchange also reports the average of these four routes as

the
Average 4TC, which is used for Forward
Freight Trading. T
he four main Baltic routes represent 95% of

the trip
-
charter fixtures, which shows how
concentrated the capsize market is. It should be noted that there are other active Capesize routes such as
Bolivar to Rotterdam (C7) and Richards Bay to Rotterdam (C4), amongst others, where vessels are hired
on a voyage charter basis. These fixtures are not considered in our sample of trip
-
charter contracts.

In the Panamax sector, the Baltic Exchange compiles
and reports more or less the same four main routes,
which again cover a large proportion of activities and fixtures. These routes are numbered as: P1A
_03
,
transatlantic route with delivery and redelivery in Europe; P2A
_03
, Europe to the Far East; P3A
_03
,
t
ranspacific round trip with delivery and redelivery in the Far East for trips to Australia, North Pacific, or
even South Africa or India; and P4
_03
, Far East to the Continent. The equally weighted average of these
four routes is reported as the
average
Pan
amax 4TC. The
average
Panamax 4TC rate is also believed to
convey the general earnings of Panamax vessels
that are
actively used for FFA trading.



14




R
eferences

Adland, R. and
Cullinane
, K. (2005) “
A Time
-
Varying Risk Premium in the Term Structure of Bulk

Shipping Freight Rates.

Journal of Transport Economics & Policy
, Vol. 39, pp. 191
-
208.

Adland, R. and Cullinane, K. (2006) “
The non
-
linear dynamics of spot freight rates in tanker markets.”
Transportation Research: Part E
, Vol. 42, No 3, pp. 211
-
224.

Ali
zadeh
, A. H. ,

and Nomikos
, K. N.

(2009): “Shipping Derivatives and Risk Management,”

Palgrave
Macmillan,London

Batchelor, R., Alizadeh, A. H. and Visvikis, I. (2007) “
Forecasting spot and forward prices in the
international freight market”
International J
ournal of Forecasting
, Vol. 23 Issue 1, p101
-
114

Beenstock, M. and Vergottis, A . R. (1989) “An econometric model of the world tanker market”
Journal
of Transport Economics and Policy,
23

(2), 263
-

280.

Beenstock, M. and Vergottis, A. (1993) “
Econometric M
odelling of World Shipping”
London,
Chapmann &Hall.

Bera, A. K. and Jarque, C. M. (1980) Efficient tests for normality, heteroskedasticity, and serial
independence of regression residuals,
Economic Letters
, 6, 255
-
259.

Brown, G. G., Graves, G. W.; Ronen, D. (1987) “Scheduling ocean transportation of crude oil.”
Management Science
, Vol. 33, No 3, pp. 335
-
346.

Davidson, R. and Mackinnon, J. (1993)
Estimation and Inference in Econometrics
.

Oxford, Oxford
University Press.

Dikos, G., Markos, H. S., Papadatos, M. P., and Papakonstantinou, V., (2006) “Niver Lines: A System
-
Dynamics Approach to Tanker Freight Modeling”
Interfaces
, Vol. 36, No. 4, pp. 326

341.

Glen, D. R. and Martin, B. T.
(1998) “
Conditional modelling of tanke
r market risk using route specific
freight rates”
Maritime Policy & Management
, Vo. 25, No 2, pp. 117
-
128.

Gujarati, D. N. (2002) “Baisc Econometric” McGraw Hill Higher Education.

Hausman
, J.A. (1978) “Specification Tests in Econometrics”,
Econometrica
,
Vol. 46 Issue 6, p1251
-
1271.

Hawdon, D. (1978) “Tanker freight rates in the short and long run”
Applied Economics,
10
, 203
-

217.

Jarque, C.M and Bera, A.K. (1980) “Efficient test for normality, homoscedasticity and serial dependence
of regression residuals
,”
Economics Letters,

Vol.

6, pp. 255
-
259.

Kavussanos, M.G. (1996)

“Comparisons of volatility in the dry
-
cargo ship sector: Spot versus time
charters, and small versus larger vessels,”
Journal of Transport Economics and policy
, January, pp.
67
-
82.

Kavussan
os, M.G. and Alizadeh, A.H. (2001) “
Seasonality patterns in the dry bulk shipping spot
and time
-
charter freight rates,”

Transportation Research Part E
, Vol. 37, pp. 443
-
467.

Kavussano
s, M. G. and Alizadeh, A. (2002
): The Expectations Hypothesis of the Ter
m Structure and Risk
Premia in Dry Bulk Shipping Freight Markets; An EGARCH
-
M Approach’,
Journal of Transport
Economics and Policy
, Vol. 36, Part 2, 267
-
304.

Koopmans, T. C. (1939)
Tanker Freight Rates and Tankship Building
,

Haarlem, Holland.

Laulajainen
, R.
(2007)

Dry

bulk

shipping market inefficiency, the wide perspective

Journal of
Transport Geography,
Vol. 15 Issue 3, pp. 217
-
224.

15


Ljung, G.M. and Box, G.E.P. (1978) “On a measure of lack of fit in time series models,”
Biometrika,

Vol.

65, pp. 297
-
303.

Lyridis D.V., Zacharioudakis P.,

Mitrou P,

Mylonas A., (2004) “Forecasting Tanker Market Using
Artificial Neural Networks”,
Maritime Economics & Logistics
, Vol. 6,

Number 2, June 2004 , pp.
93
-
108.

Newey, W. K. and West, K. D. (1987) “A simple positive de
finite heteroskedasticity and autocorrelation
consistent covariance matrix,”
Econometrica,
Vol

55, pp. 703
-
708.

Randers, J. and Göluke, U. (2007) “Forecasting turning points in shipping freight rates: lessons from 30
years of practical effort.”
System Dyna
mics Review
, Vol. 23, No. 2/3, pp. 253

284

Stopford, M. (2009)


Maritime Economics
” Routledge, London.

Strandenes, S.

P.

(1984) “Price determination in the time
-
charter and second
-
hand markets,” Working
paper No 06, Centre for Applied Research, Norwegian
School of Economics and Business
Administration
.


Strandenes, S. P. (1999) “
Is there potential for a two
-
tier tanker market?


Maritime Policy and
Management
, VOL. 26, NO. 3, pp. 249
-
264

Tamvakis
,

M. N. and Thanopoulou H. A. (2000) “Does quality pay? The case of the dry bulk market”,
Transportation Research Part E: Logistics and Transportation Review
, Volume 36, Issue 4, Pages
297
-
307.

Tamvakis,

M. N.
(
1995
)
, An investigation into the existence of a two
-
tier spot freight market for crude oil
tankers.
Maritime Management and Policy,
Vol.

22
, No

1
,

pp.

81
-

90.

Tinbergen, J. (1934)

“ Scheepsruimte en vrachten”
De Nederlandsche Con
junctuur
, March, pp. 23
-
35.
Tsolakis, S.D. (2005). ‘Econometric analysis of bulk shipping markets implications for
investment strategies and financial decision
-
making’. Unpublished PhD Thesis. Department
of Econometrics, Erasmus School of Economics, Erasmus

University Rotterdam, The
Netherlands.

Veenstra, A. W., Franses, P. H. (1997) “
A co
-
integration approach to forecasting freight rates in the dry
bulk shipping sector”
Transportation Research Part A: Policy & Practice
, Vol. 31A, Issue 6, pp.
447
-
459.

White, H. (1980): “A heteroscedasticity
-
consistent covariance matrix estimator and a direct test for
heteroscedasticity,”
Econometrica
, Vol. 48, pp 55
-
68.

Zannetos,Z.S. (1966)
The Theory of Oil Tankship Rates
,

Massachusetts: MIT Press.


16




Table
1
: Components of the Baltic Average 4TC
Rates (
Indices
)

for Capsize and Panamax
Dry Bulk
Ships

Capesize

Route

Size (dwt)

Description

Weighting

C8_03

172,000

Delivery Gibraltar to Hamburg range for Transatlantic round voyage

25%

C9_03

172,000

Delivery Continent Europe to Mediterranean for a trip to the Far East

25%

C10_03

172,000

Delivery China to Japan range for a Transpacific round voyage

25%

C11_03

172,000

Delivery China to Japan range for a trip to European Continent and
Mediterranean

25%

Panamax


Size (dwt)

Description

Weighting

P1A_03

74,000

Delivery Gibraltar to Hamburg range for Transatlantic round voyage

25%

P2A_03

74,000

Delivery Skaw to Gibraltar range for trip to the Far East via US Gulf

25%

P3A_03

74,000

Delivery Japan to South Korea for a Transpacific round voyage

25%

P4_03

74,000

Delivery Far East for a Trip to Europe (Skaw to Cape Passero range) via
North Pacific or Australia

25%



Route C8_03 to C11_03 are based on a standard Baltic Capesize vessel of the following specification: 172,000
mt dwt, not over 10 years of age, 190,000 cbm grain, max LOA 289m, max beam 45m, draft 17.75m, 14.5 knots
laden, 15 knots ballast on 56 mts fuel oi
l, no diesel at sea



Routes P1A_03, P2A_03, P3A_03 and P4_03 are based on a standard Baltic Panamax vessel of the following
specifications: 74,000 mt dwt , not aged over 7 years with 89,000 cbm grain, max LOA 225 m, draft 13.95 m,
capable of about 14 knots

on 32 mts fuel oil laden, 28 mts fuel oil ballast and no diesel at sea.



17


Table
2
: Descriptive
S
tatistics for
R
outes and
L
aycan
P
eriods for
Dry Bulk Ships




Sample period: 1
st

January 2003 to 31 July 2009.
































Route


Number of Fixtures


Laycan Period (days)


Capesize


No

Percentage


Mean

Median

Min

Max

SD

1

Trans At lant ic Round Voyage


366

12.0%


8.6

8.0

0.0

39.0

7.0

2

Continent to Far East


492

16.2%


9.0

8.0

0.0

35.0

7.2

3

Trans Pacific Round Voyage


1642

54.0%


6.7

6.0

0.0

77.0

6.3

4

Far East to Continent


391

12.9%


7.7

7.0

0.0

31.0

6.5

5

Other routes


148

4.9%


8.2

7.0

0.0

42.0

7.7


Total Fixtures


3039



7.5

6.0

0.0

77.0

6.7


Panamax










1

Trans Atlantic Round Voyage


1397

15.4%


3.7

3.0

0.0

40.0

4.4

2

Continent to Far East


1033

11.4%


4.1

3.0

0.0

38.0

4.5

3

Trans Pacific Round Voyage


3174

35.0%


4.2

3.0

0.0

61.0

4.2

4

Far East to Continent


503

5.5%


4.8

4.0

0.0

34.0

4.5

5

Mediterranean to Far East


104

1.1%


5.5

5.0

0.0

27.0

5.2

6

PG
-

Indian Ocean to Far East


865

9.5%


5.7

4.0

0.0

32.0

5.5

7

Far East

to PG
-
Indian Ocean


376

4.1%


3.7

3.0

0.0

30.0

3.9

8

Continent to PG
-
Indian Ocean


218

2.4%


3.3

3.0

0.0

17.0

3.6

9

PG
-

Indian Ocean to Continent


208

2.3%


5.2

4.0

0.0

30.0

5.5

10

Other routes


1198

13.2
%



4.8

3.0

0.0

47.0

5.3


Total Fixtures


9076



4.4

3.0

0.0

61.0

4.6

18



Table
3
: Descriptive
S
tatistics of
E
xplanatory
V
ariables


Age

Size

Dwt

Laycan

period

Freight

Rate

Baltic

Ave 4TC

Diff FR
and Baltic


years

000 mt

days

$/day

$/day

%

Capesize







Mean

9.9

165.1

7.5

66,964

72,451

-
0.03
07

Median

9.0

170.2

6.0

55,000

63,235

-
0.01
7

Maximum

36.0

301.8

77.0

303,000

233,988

1.058

Minimum

0.0

101.7

0.0

1,000

2,320

-
1.95
7

Std. Dev.

7.1

14.9

6.7

47,611

46,378

0.227

Skewness

0.6

-
0.1

1.4

1.5

1.3

-
0.3
8

Kurtosis

2.5

6.6

8.7

5.2

4.2

6.3
9

JB test

215.9

1652

5175

1747

1039

4554

P
-
value

0.000

0.000

0.000

0.000

0.000

0.000

Panamax







Mean

9.6

72.3

4.4

32,001

32,506

0.0003

Median

8.0

73.6

3.0

26,500

27,642

0.000

Maximum

33.0

79.9

61.0

125,000

94,977

2.394

Minimum

0.0

50.3

0.0

1,000

3,537

-
2.29
5

Std. Dev.

7.0

3.9

4.6

20,877

20,189

0.373

Skewness

0.85

-
1.05

2.07

1.22

1.02

0.096

Kurtosis

2.83

3.48

12.09

4.14

3.35

6.42

JB test

1100

1745

37726

2758

1636

1488.
8

Probability

0.000

0.000

0.000

0.000

0.000

0.000



Sample period: 1
st

January 2003 to 31 March 2009, consist of 3039 Cap
si
ze Trip
-
charter fixtures, and 9079 of
Panamax trip
-
charter fixtures.



JB is the Bera and Jarque (1980)
test for normality which follows
. The 5% critical value for this test is 5.991.









19


Table
4
:

Determinants of Capsize and Panamax Trip
-
Charter Freight Rates



Capesize



Panamax



Coeff

P
-
val




Coeff

P
-
val

α
0

Constant

-
0.7242

0.000


α
0

Constant

-
0.4435

0.000

α
1

Laycan
LC
i
,t

0.0024

0.000


α
1

Laycan
LC
i
,t

0.0035

0.000

α
2

Size
SZ
i

0.0039

0.000


α
2

Size
SZ
i

0.0060

0.000

α
3

Age
AG
i

0.0084

0.001


α
3

Age
AG
i

0.0066

0.000

α
4


AG
2
i

-
0.0008

0.000


α
4


AG
2
i

-
0.0006

0.000

α
5

Volatility
VOL
t

-
0.0233

0.154


α
5

Volatility
VOL
t

0.0020

0.87
8


Route





Route




1

Trans Atlantic Round Voyage

0.0298

0.319



1

Trans Atlantic Round Voyage

0.0368

0.000


2

Continent to Far East

0.2685

0.000



2

Continent to Far East

0.1494

0.000


3

Trans Pacific Round Voyage

-
0.0301

0.263



3

Trans Pacific Round Voyage

-
0.0910

0.000


4

Far East to Continent

-
0.3230

0.000



4

Far

East to Continent

-
0.1709

0.000







5

Mediterranean to Far East

0.2474

0.000







6

PG


Indian Ocean to Far East

0.0809

0.000







7

Far East to PG
-
Indian Ocean

-
0.0757

0.000







8

Continent to PG
-
Indian Ocean

0.1900

0.000







9

PG


Indian Ocean to Continent

-
0.1504

0.000


0.5807





0.346


BG test

37.054

0.000




111.44

0.000

White test

208.10

0.000




801.53

0.000

JB test

2.15*10
5

0.000




1.4*10
4

0.000



BG test is the Breusch and Godfrey LM test for 10
th

order serial correlation in residuals
, which follows Chi
-
squared distribution with 10 degrees of freedom.



White Test is the White (1980) the F
-
test for heteroscedasticity.

This is an LM test which follows Chi
-
squared distribution with 4

degrees of freedo
m
.



JB the Jarque and Bera test for normality of residuals, which follows a Chi
-
squared distribution with 2

degrees of freedom
.



Standard Errors are corrected for hetreoscedasticity and Se
rial Correlation using Newey and
West (1987) method.

20




Table
5
:

Determinants of the Laycan Period of Trip
-
Charters Contracts



Capesize



Panamax



Coeff

P
-
val




Coeff

P
-
val

β
0

Constant

11.0116

0.000


β
0

Constant

-
0.9773

0.659

β
1

Diff Freight Rate d
fr
i
,t

3.3320

0.000


β
1

Diff Freight Rate d
fr
i
,t

2.1310

0.000

β
2

Baltic 4TC Rate


bfi
t

0.4027

0.046


β
2

Baltic 4TC Rate


bfi
t

0.4602

0.000

β
3

Size
SZ
i

-
0.0273

0.007


β
3

Size
SZ
i

0.0205

0.456

β
4

Age
AG
i

-
0.0849

0.000


β
4

Age
AG
i

0.0139

0.364

β
5

V
olatility
V
OL
t

-
3.2349

0.000


β
5

V
olatility
V
OL
t

-
1.0252

0.000











Route





Route




1

Trans Atlantic Round Voyage

0.1386

0.846



1

Trans Atlantic Round Voyage

-
1.2121

0.000


2

Continent to Far East

-
0.3820

0.614



2

Continent to Far East

-
1.1709

0.000


3

Trans Pacific Round Voyage

-
1.3851

0.033



3

Trans Pacific Round Voyage

-
0.5197

0.005


4

Far East to Continent

0.1803

0.816



4

Far East to Continent

0.1840

0.480







5

Mediterranean to Far East

0.2333

0.662







6

PG
-

Indian Ocean to Far East

0.7599

0.003







7

Far East to PG
-
Indian Ocean

-
1.0153

0.000







8

Continent to PG
-
Indian Ocean

-
1.9339

0.000







9

PG
-

Indian Ocean to Continent

0.6261

0.126


0.0577





0.0341


BG test

44.27

0.000




65.15

0.000

White test

26.53

0.055




63.28

0.000

JB test

6283

0.000




4.17*10
4

0.000



BG
test is the Breusch and Godfrey LM test for 10
th

order serial correlation in residuals
, which follows Chi
-
squared distribution with 10 degrees of freedom.



White Test is the White (1980) the F
-
test for heteroscedasticity.

This is an LM test which follows C
hi
-
squared distribution with 4

degrees of freedom
.



JB the Jarque and Bera (1980) test for normality of residuals, which follows a Chi
-
squared distribution with 2

degrees of freedom
.



Standard Errors are corrected for hetreoscedasticity and

Serial Correlation using Newey and
West (1987) method.


21



Table
6
:
Determinants of Freight Rates and Laycan Periods of

Capeszie Ship Trip
-
Charter contracts: A
Si
multaneous

Equation Estimation




d
fr
i,t

Equation


LC
i,t

Equation


Variables


Coeff

p
-
val



Variables


Coeff

p
-
val

α
0

Constant


-
0.7532

0.000


β
0

Constant


11.92
1

0.000

α
1

Laycan
LC
i
,t


0.0047

0.000


β
1

Diff Freight Rate d
fr
i,t


6.4483

0.000

α
2

Size
SZ
i


0.0039

0.000


β
2

Baltic 4TC Rate
bfi
t


0.4878

0.021

α
3

Age
AG
i


0.0084

0.000


β
3

Size
SZ
i


-
0.0386

0.000

α
4



AG
i
2


-
0.0008

0.000


β
4

Age
AG
i


-
0.0518

0.012

α
5

Volatility

VOL
t


-
0.0150

0.238


β
5

Volatility
VOL
t


-
3.1427

0.000


Routes






Routes





1,1

Trans
Atlantic Round Voyage


0.0293

0.083



2,1

Trans Atlantic Round Voyage


-
0.0010

0.999


1,2

Continent to Far East


0.2675

0.000



2,2

Continent to Far East


-
1.2539

0.048


1,3

Trans Pacific Round Voyage


-
0.0266

0.074



2,3

Trans Pacific Round Voyage


-
1.3085

0.019


1,4

Far East to Continent


-
0.3213

0.000



2,4

Far East to Continent


1.1686

0.076



0.5777






0.0507


BG test


58.93

0.000


62.65

0.000

White test


71.94

0.000


35.10

0.000

JB test


20.9*10
4

0.000


6223

0.000



BG test is the Breusch and Godfrey LM test for 10
th

order serial correlation in residuals
, which follows Chi
-
squared distribution with 10 degrees of freedom.



White Test is the White (1980) the F
-
test for heteroscedasticity.

This is an LM test which follow
s Chi
-
squared distribution with 4

degrees of freedom
.



JB the Jarque and Bera test for normality of residuals, which follows a Chi
-
squared distribution with 2

degrees of freedom
.



Standard Errors are corrected for hetreoscedasticity and

Serial Correlation us
ing Newey and
West (1987) method.




22


Table
7
:
Determinants of Freight Rates and Laycan Periods of

Panamax Ship Trip
-
Charter Contracts: A System

of
S
imultaneous

Equations



d
fr
i
,t

Equation


LC
i
,t

Equation


Variables


Coeff

p
-
val



Variables


Coeff

p
-
val

α
0

Constant


-
0.4487

0.000


β
0

Constant


-
0.2105

0.902

α
1

Laycan
LC
i
,t


0.0067

0.000


β
1

Diff Freight Rate d
fr
i
,t


4.0890

0.000

α
2

Size
SZ
i


0.0058

0.000


β
2

Baltic 4TC Rate


bfi
t


0.4508

0.000

α
3

Age
AG
i


0.0064

0.000


β
3

Size
SZ
i


0.0093

0.653

α
4


AG
i
2


-
0.0006

0.000


β
4

Age
AG
i


0.0329

0.005

α
5

Volatility
VOL
t


0.0074

0.346


β
5

V
olatility
V
OL
t


-
1.0504

0.000


Routes






Routes





1,1

Trans Atlantic Round Voyage


0.0404

0.000



2,1

Trans
Atlantic Round Voyage


-
1.2722

0.000


1,2

Continent to Far East


0.1522

0.000



2,2

Continent to Far East


-
1.4568

0.000


1,3

Trans Pacific Round Voyage


-
0.0887

0.000



2,3

Trans Pacific Round Voyage


-
0.3438

0.028


1,4

Far East to Continent


-
0.1703

0.000



2,4

Far East to Continent


0.5248

0.034


1,5

Mediterranean to Far East


0.2450

0.000



2,5

Mediterranean to Far East


-
0.2508

0.595


1,
6

PG
-

Indian Ocean to Far East


0.0778

0.000



2,
6

PG
-

Indian Ocean to Far East


0.6009

0.003


1,
7

Far East
to PG
-
Indian Ocean


-
0.0720

0.000



2,
7

Far East to PG
-
Indian Ocean


-
0.8621

0.001


1,
8

Continent to PG
-
Indian Ocean


0.1950

0.000



2,
8

Continent to PG
-
Indian Ocean


-
2.2912

0.000


1,
9

PG
-

Indian Ocean to Continent


-
0.1514

0.000



2,
9

PG
-

Indian
Ocean to Continent


0.9270

0.007



0.3412



0.0278


BG test


167.18

0.000


122.35

0.000

White test


799.29

0.000


60.479

0.000

JB test


13495

0.000


41000

0.000



BG test is the Breusch and Godfrey LM test for 10
th

order serial correlation in residuals
, which follows Chi
-
squared distribution with 10 degrees of freedom.



White Test is the White (1980) the F
-
test for heteroscedasticity.

This is an LM test which follows Chi
-
squared distribution with 4

degrees of freedo
m
.



JB the Jarque and Bera (1980) test for normality of residuals, which follows a Chi
-
squared distribution with 2

degrees of freedom
.



Standard Errors are corrected for hetreoscedasticity and

Serial Correlation using Newey and
West (1987) method.