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ECONOMIC SPILLOVERS BETWEEN

RELATED DERIVATIVES MARKETS:

THE CASE OF COMMODITY AND FREIGHT MARKETS

MANOLIS G. KAVUSSANOS

*

Athens University of Economics and Business

Department of Accounting and Finance

Athens, Greece, Email: mkavus@aueb.gr

ILIAS D. VISVIKIS

ALBA Graduate Business School

Athens, Greece, Email: ivisviki@alba.edu.gr

DIMITRIS N. DIMITRAKOPOULOS

Athens University of Economics and Business

Department of Accounting and Finance

Athens, Greece, Email: jim@aueb.gr

DECEMBER 2011

ABSTRACT

Extant studies in the literature investigate volatility spillover effects between spot markets of the same asset

class or between derivatives and their underlying spot markets. This paper considers, for the first time,

economic spillovers between two different and important for the world economy derivatives markets; namely, the freight forward market and the commodity futures markets of commodities transported by

ocean-going vessels. An economic relationship linking the two markets is proposed and tested empirically.

Moreover, the difficulty of derivatives pricing of the non-storable freight service may be overcome by

considering the link with the derivatives of the commodity transported proposed in this paper. Return and

volatility spillover effects of high significance are uncovered, for the first time, between the two distinct but

interrelated derivatives markets.

Keywords: Futures and Forward Markets, Causality, Price Discovery, Volatility Spillovers, Shipping and

Commodity Markets.

JEL Classification: G13, G14, C32

Corresponding Author: Manolis G. Kavussanos, Athens University of Economics and Business, 76 Patission Str., 10434, Athens, Greece, Tel: +30 210 8203167, Fax: +30 210 8203196, Email: mkavus@aueb.gr. 2

1. INTRODUCTION

Cross-market information transmission is a research area that has received a lot of attention from both academia and practitioners alike. Following Working (1970), economic shocks in one market can impact with various degrees of severity other markets. In perfectly efficient markets, new information is simultaneously incorporated into the prices of the markets, in such a way so that prices adjust to new equilibrium levels without any time delay (see Chan et al., 1991). However, transactions costs, information asymmetries, supply-demand imbalances and other market microstructure issues may create information spillover (lead-lag) relationships between markets (see Wahab and Lashgari, 1993; Fleming, et al., 1996, among others). The importance of modelling such relationships is linked with the nature of trading dynamics between markets. Investors and fund managers can utilize such information spillovers to create investment strategies and portfolios, respectively. Regulators and policy decision-makers are interested in these relationships in order to better monitor and supervise the markets. Economic agents participating in these markets can utilize these relationships in investment and hedging decisions, as the difference in reaction times between markets can be exploited in derivatives trades.

Cross-market linkages and spillover effects broadly fall into three categories. The first constitutes

a linkage between spot markets that are fundamentally linked through supply and demand functions 1 . The second refers to information flows between derivatives markets and their underlying spot markets 2 , and the third one, which, surprisingly enough, has received the least attention, concerns return and volatility spillovers between different derivatives markets 3 . This paper investigates the information (spillovers) relationships between freight and commodity (grain and oil) derivatives markets and analyses the magnitude and direction of these spillovers. More than 95% of the world's commodity trade is transported by ocean-going vessels. The

international market for freight services possesses some special features that set it apart from other

commodity markets, due to its high level of volatility, cyclical nature, the seasonality influences of

the commodities transported, and its non-storable nature, amongst others. The latter characteristic alone differentiates the freight market from all other storable commodity markets, as the theory of storage and the cost-of-carry no-arbitrage relationships cannot be applied for the pricing of 1

See Kao and Wan (2009) on energy markets; Yu et al. (2007) on spot grain commodities and freight prices; and

Haigh and Bryant (2001) on barge, ocean freight prices and soybeans prices, among others. 2

See Coppola (2008) on futures and spot commodity markets; Kavussanos and Visvikis (2004) on forward and spot

freight markets, among others. 3

See Chng (2009) on natural gas, palladium and gasoline Japanese futures markets; Chulia and Torro (2008) on stock

(the DJ Euro Stoxx 50 index futures) and bond (the Euro Bund futures) derivatives markets; Fung, et al. (2010) on US

and Chinese aluminum and copper futures markets; and Kavussanos et al. (2010) on freight forwards and commodity

futures markets, among others. 3 derivatives contracts for freight. As such, there is an increasing need for more sources of information, that may be utilized by economic agents participating in these markets for the pricing and trading of such commodity contracts 4, 5 . Economic theory and intuition suggests that bi-directional linkages between freight derivatives markets - Forward Freight Agreements (FFA) - and commodity futures markets for commodities carried by ships may exist. On one hand, the final CIF (Cost, Insurance and Freight) prices of commodities transported by sea should reflect the global demand and supply conditions for these commodities. The CIF prices incorporate the FOB (Free on Board) prices, insurance and freight.

As a consequence, freight rates and the information incorporated in freight markets affect the final

demand for the commodity through the pricing channel, the degree of the impact depending on the contribution of freight to the CIF prices and on the elasticity of demand with respect to freight rates. It is also known that FFA prices are related to the underlying freight rates. On the other hand, what happens in commodity markets affects the demand for freight services, as the latter depends directly on the former. Moreover, commodity futures prices are linked to the underlying commodity price. Commodity and freight rate markets then should be linked somehow and market participants monitor both markets when taking investment decisions. The nature of this economic relationship between the two derivatives markets is addressed in this paper and is consistent with the presented empirical findings.

Given that derivatives contracts exist on these two sets of markets, the information available in the

commodity derivatives market is expected to also reveal itself in the freight derivatives market and

vice versa. Moreover, due to the forward looking behavior of derivatives markets, possible spillover effects between spot markets may make themselves evident first in the corresponding derivatives markets. Market participants, active in the freight (commodity) derivatives markets, can benefit by the existence of such spillover effects, as they can exploit the information incorporated in the commodity (freight) derivatives prices for investment and hedging purposes.

4 Goss and Avsar (1999) argue that a major difference between non-storable and storable commodities (when both

spot and derivatives markets exist) is that the magnitude of the forward premium (contango) in the case of storable

commodities is limited by the "marginal net cost of storage", whereas for non-storable commodities no restriction

exists. Keynes (1930) mentions that in the case of backwardation, no such restriction exists, both for storable and non-

storable commodities.

5 Prokopczuk (2011) employs alternative affine continuous-time models of the spot price dynamics in order to derive

closed-form valuations for freight futures contracts. 4 FFAs are Over-The-Counter (OTC) forward contracts between a buyer and a seller, to settle a

freight rate, for a specified quantity of cargo (in a voyage chartering agreement) or number of days

(in a time-charter agreement), for a specific type of vessel, for one or a combination of the major trade routes of the dry-bulk, tanker and containership sectors of the shipping industry. Charterers

that wish to fix a vessel in a future time period to cover cargo transportation requirements protect

themselves against freight rate increases by buying FFAs. Shipowners wishing to hire their vessels in a future time period can hedge themselves against freight rate decreases by selling FFAs. The trading routes, which serve as the underlying assets of dry-bulk FFA contracts, are based on the Baltic Capesize Index (BCI), the Baltic Panamax Index (BPI), the Baltic Supramax Index (BSI) and the Baltic Handysize Index (BHSI) 6 . The Baltic Exchange indices comprise the most important routes in each segment of the industry and are designed to reflect freight rates across spot voyage and time-charter routes. Similarly, commodity futures contracts are agreements to buy or sell a certain amount of a commodity, of certain specifications in a future time period. Commodity futures contracts reflect the future price of the commodities transported by vessels in routes that are the underlying assets of the FFA contracts. Additionally, commodity futures constitute the main hedging instruments for shippers who are involved in seaborne transportation. The type of vessel carrying each commodity depends on the economics of the industry that creates the demand for the commodity and the regions where the industries using the raw materials are established, relative to the raw material producing countries. For instance, Capesize vessels typically carry commodity parcel sizes of approximately 150,000 - 170,000 metric tons of iron ore because these are the typical commodity sizes of iron ore required by the steel mills using iron-ore as raw material in the production of steel. A typical route that such vessels operate is between the iron-ore producing Brazil and Northern Europe, where the steel mills are established. The size of the vessel used of course

requires that the ports are deep enough, with enough storage facilities and sufficient handling gear

to accommodate these vessels. This paper contributes to the literature in a number of ways: First, it puts forward an economic framework, where the derivative market of the commodity transported is linked to that of the freight market of the vessel transporting it, which shows that commodity futures informationally lead the freight derivatives markets. Following that and since it has been found in the literature that the derivatives markets under investigation informationally lead their corresponding 6

More details on the various freight market indices, their construction process and the use of FFAs can be found in

Kavussanos and Visvikis (2006, 2011).

5 underlying spot (physical) markets, the main findings here should apply in the spot freight and commodity markets as well 7 . This economic framework further contributes to the pricing of FFAs, which are not so precisely priced given the non-storable nature of their underlying "commodity"; namely the freight service (see Kavussanos and Visvikis, 2004) 8 . Second, by utilizing a data set large enough to include a global financial crisis, the paper investigates the statistical properties of the variables of interest, as well as, their long-run interrelationships during crises periods. Structural breaks that may arise in either case in such adverse market conditions may have a significant effect on the spillover patterns between the examined variables. Investigating and incorporating the influence of such structural breaks in an information spillover framework is important to investors and traders engaging in these derivatives markets, as derivatives contracts can serve the role of "break discovery" to the underlying spot markets (see Lien et al. 2003). Lien and Yang (2010) argue that the breaks in futures markets always take place before those in the physical markets. Therefore, locating structural breaks in derivatives markets can serve as an indication of such breaks taking place in the spot markets. Third, since the US commodity futures markets close at a different time interval than the time of the announcement of FFA prices in the UK, it is possible that non-synchronicity may influence the results 9 . In order to ensure that the spillover inferences are not biased by a non-synchronous trading problem among the markets, a "time-matched" data set is created, in which the prices of all

commodity futures contracts are retrieved in the US, on a daily basis, at the exact publication time

of the freight derivatives prices in London, and thus, overcoming the possibility of non- synchronicity in the data. 7 Kavussanos and Visvikis (2004), show that FFA markets are broadly unbiased and that the FFA market

informationally leads the underlying (physical) spot market for freight rates. As such, FFAs can be utilized as price

discovery vehicles for spot freight markets. Wheat, corn, soybean and coal futures, which correspond to the

underlying commodities transported in the shipping routes of the dry-bulk FFA contracts, are also shown in the

literature to fulfill their price discovery role in relation to their underlying spot markets; see for instance, McKenzie

and Holt (2002) for US corn futures and Yang and Leatham (1999) for US wheat commodity futures markets, among

others. 8

Tomek and Gray (1970) argue that futures prices of storable commodities provide more reliable forecasts (and thus

can assimilate more information) than those for non-storable commodities, as the futures prices for non-storable

commodities serve as a source of price stability, while the futures prices for storable commodities serve as a measure

of inventory allocation. 9

In any given day, the Baltic Exchange FFA prices are announced at 17:30 London time, while Chicago Mercantile

Exchange (CME) closing futures prices are published at 19:15 London time (13:15 in Chicago). 6 Fourth, to the best of our knowledge, with the exception of Kavussanos et al. (2010), this is the first paper to empirically examine cross-market return and volatility information spillovers between freight and commodity derivatives markets. Moreover, it extends the Kavussanos et al. (2010) study by combining the sub-segments of dry-bulk vessels (Capesize, Panamax and Supramax), which carry various types of commodities under different types of freight contracts (route-specific and time-charter contracts - see section 3 for more details), allowing comparisons between the various dry-bulk shipping sectors and the different freight contracts, respectively. Fifth, this paper investigates and reveals that commodity and freight derivatives markets are

interrelated, standing in a long-run equilibrium (cointegration) relationship between them. Finally,

results can help improve the understanding of the information transmission mechanisms between freight and commodity derivatives markets (and consequently between their underlying spot markets) and assist market participants into more effective trading, investment and hedging decisions. The remainder of this paper is structured as follows. The next section presents the economic framework, linking freight and commodity derivatives markets, and the methodology used. Section three analyses the data and outlines some preliminary results. Section four presents the

return and volatility spillover results. The fifth section provides a critical discussion of the results.

Finally, section six concludes the paper.

2. METHODOLOGY

2.1. Economic Framework

The final CIF price of commodities transported by sea, which incorporate the FOB price, insurance and freight, should reflect the global demand and supply conditions for these commodities. Consider the demand for a commodity carried by bulk carrier ships, say for wheat. This demand depends on the CIF spot price of the commodity at time t, S CIF,t , as this is the price that the final consumer pays. One could decompose this S CIF,t price into the FOB price of the commodity that has been negotiated during the last period in the market (S

FOB,t-1

), the current freight rate (S FR,t ) and the insurance required for the transportation of the commodity (INS).

Mathematically:

S

CIF,t

= S

FOB,t-1

+ S FR,t + INS (1) 7 The insurance part in the above equation is relatively steady over time. However, both S

FOB,t-1

and S FR,t are quite volatile and depend on the demand and supply conditions of the commodity and freight markets, respectively. For instance, the spot freight rate for the transportation of the relevant commodity depends on the demand and supply conditions of the freight market for this commodity; that is, on the number of cargoes of the commodity being available for transportation

at any point in time on a particular route (but also on other types of cargo, such as wheat, corn and

coal that are transported by similar types of vessels), as well as on the number of vessels available

to transport the commodity at that point in time. As shown by Kavussanos, et al. (2004), the unbiasedness hypothesis holds in the freight market, and as such, the FFA price (F FR,t ) can substitute the spot freight rate, S FR,t , yielding: S

CIF,t

= S

FOB,t-1

+ F FR,t + INS (2)

Similarly, the spot price of the commodity (S

CIF,t ) is determined by the demand and supply of cargoes for this commodity. The commodity futures (CIF) price, F CIF,t , in turn, is determined through the following cost of carry relationship: F

CIF,t

= S CIF,t + C (3) where, C refers to the relatively steady over time cost of carrying the commodity forward in time and incorporates storage, insurance and financial costs. Substituting Equation (3) into (2) yields: F

CIF,t

= S

FOB,t-1

+ F FR,t + INS + C (4) From equation (4), it is evident that the derivative market of the commodity transported is linked to that of the freight market; that is, the futures price for a commodity today (F CIF,t ) is related to the one-period lag of the spot price of the physical commodity (S

FOB,t-1

), the current price of the freight derivatives market (F FR,t ), the insurance to transport the commodity (INS) and the cost of carry components (C). This economic relationship can be used to assist the pricing of the non-

storable derivatives contracts for freight. Following the above, this paper empirically investigates

the dynamic interrelationship between the commodity and the related freight derivatives markets, for a number of commodities carried by sea. 8

2.2. Stationarity and Cointegration

To determine the order of integration of each price series the standard unit root tests of Dickey and

Fuller (ADF, 1981), Phillips and Perron (PP, 1988) and Kwiatkowski et al. (KPSS, 1992) and the

later test of Lee and Strazicich (2003, 2004) (LS henceforth) that accounts for structural breaks are

used 10 . A drawback of the standard unit root tests (like the ADF and PP) is that structural breaks may affect their outcome, as economic variables may be better described as stationary processes around a breaking level, rather than integrated ones (see Perron 1997). Thus, standard unit root

testing procedures may erroneously fail to reject the null hypothesis that a series is integrated of

higher order. In order to identify and account for structural breaks in the unit root testing of economic variables, during a highly volatile environment that includes a financial crisis, the LS

unit root test is also employed. This test allows for two endogenous structural breaks in the levels

of the series 11 . The LS test is superior to other similar tests in that it offsets the loss of power of

tests that search for one structural break by including structural breaks both under the null and the

alternative hypotheses (with the rejection of the null to indicate trend stationarity) 12 . Critical values for the one- and two-break cases are tabulated in Lee and Strazicich (2004) and (2003), respectively 13 . Given a set of two non-stationary series, Johansen (1988) tests are used next to determine whether the series stand in a long-run relationship between them; that is that they are cointegrated. The following Vector Error Correction Model (VECM) is estimated: ǻX t = 1p 1i ī i ǻX t-i + ȆX t-1 + İ t ; İ t | t-1 ~ distr(0, H t ) (5) where X t is the 2x1 vector (FFA t , FUT t )' of log-FFA and log-commodity futures prices, respectively, ǻ denotes the first difference operator, İ t is a 2x1 vector of residuals (İ S,t , İ F,t )' that follow an as-yet-unspecified conditional distribution with mean zero and time-varying covariance matrix, H t . Johansen and Juselius (1990) show that the coefficient matrix Ȇ contains the essential information about the relationship between FFA t and FUT t . Specifically, the VECM specification contains information on both the short- and long-run adjustment to changes in X t , via the 10

The KPSS test addresses the lack of power of the ADF and PP tests, in rejecting the null hypothesis of a unit root

when it is false, by having stationarity as the null hypothesis. 11

Mehl (2000) argues that by adding more than two breaks the time-series are closer to becoming a random-walk

process, and therefore, unit root tests with multiple structural breaks are less relevant. 12

The assumption of no breaks under the null hypothesis may lead to size distortions in the presence of a unit root

with breaks (see Lee and Strazicich, 2003). 13

There is another version of the LS test, which allows for two shifts in the level and trend of the series, but it is not

used due to sample size considerations. 9 estimated parameters ī i and Ȇ, respectively. If Ȇ has a reduced rank, that is rank(Ȇ) = 1, then there is a single cointegrating relationship between FFA t and FUT t , which is given by any row of matrix Ȇ and the expression ȆX t-1 is the error-correction term. In this case, Ȇ can be factored into

two separate matrices Į and ȕ, both of dimensions 2x1, where 1 represents the rank of Ȇ, such as

Ȇ = Įȕ', where ȕ' represents the vector of cointegrating parameters and Į is the vector of error-

correction coefficients measuring the speed of convergence to the long-run equilibrium 14 . As global economic and financial shocks may cause shifts in the cointegration relationship

between economic variables, it is also important to account for the existence of structural breaks in

integrated systems of variables. A shortfall of Johansen's (1988) standard cointegration test is that

it is prone to Type II error when breaks exist in the cointegrating system (i.e. it fails to reject the

null of no cointegration when in fact there is cointegration with breaks; see Villanueva, 2007). This in turn, may lead to misspecification of the long-run properties of a dynamic system, inadequate estimation and incorrect inferences. In that respect, the residual-based cointegration test of Gregory and Hansen (1996a, b) is used, under two models that allow, respectively, the alternative hypothesis for one endogenous intercept shift ("level shift" or Model C as Gregory and Hansen name the model) and a shift in both intercept and slope ("regime shift" or Model C/S) of the cointegration vector at some unknown date: y t = Į 1 + a 2 D t + ȕ 1 x t + İ t (6a) y t = Į 1 + a 2 D t + ȕ 1 x t + ȕ 2 D t x t + İ t (6b) where, the break dummy D t = 1 for t = t * +1, ..., T and D t = 0 for t = 1, ..., t * , t * is an endogenously determined break date of a sample of size T, Į 1 and (Į 1 + a 2 ) are the intercepts before and after the break at t * , and ȕ 1 and (ȕ 1 + ȕ 2 ) are the cointegrating slope coefficients before and after the break.

Then the Phillips-Perron Z

t statistic, which is an ADF-type test that uses a corrected covariance matrix is estimated for the residuals of the equations 15 . The process is repeated until the minimum value of the statistic ( ௧כ ) is found, which corresponds to the break date. The critical values are provided by Gregory and Hansen (1996a, b). However, it should be noted that if the standard cointegration models (without breaks) reject the null of no-cointegration then there is no need to 14

Since rank(Ȇ) equals the number of characteristic roots (or eigenvalues) which are different from zero, the number

of distinct cointegrating vectors can be obtained by the Ȝ trace and Ȝ max statistics of Johansen (1988). Critical values are provided by Osterwald-Lenum (1992). 15

Monte Carlo experiments indicate that from the three recursive tests of GH (ADF and the Phillips-Perron Z

t and Z a ) the Z t test performs better than the ADF and Z a tests (see Gregory and Hansen, 1996a, b) and therefore, only the Z t results are reported in the ensuing analysis. 10 test for cointegration with breaks. In contrast, if the standard cointegration models cannot reject the null, then the Gregory and Hansen test is used, which tests for a shift in the cointegration vector at some point in time (see Gregory and Hansen, 1996a). Finally, since this test does not provide consistent standard-errors for parameter hypothesis testing, the Fully-Modified OLS (FM- OLS) estimator, proposed by Philips and Hansen (1990) is used. The latter estimates a heteroskedasticity and autocorrelation consistent covariance matrix in order to extract the parameters of the cointegration Error-Correction Terms (ECTs).

2.3. Return and Volatility Spillovers

To investigate for return spillovers between the various derivatives markets, pairs of FFA and commodity futures, corresponding to the major commodities transported by the specific vessels, are constructed. The following VECM is estimated in each case: FFA t = 1 1p i a FFA,i FFA t-i + 1 1p i b FFA,i FUT t-i + q FFA z t-1 + İ

1,t

(7a)

İ

i,t | t-1 ~ distr(0, H t ) FUT t = 1 1p i a FUT,i FFA t-i + 1 1p i b FUT,i FUT t-i + q FUT z t-1 + İ

2,t

(7b) where, FFA t-i and FUT t-i are the logarithmic first-differences of FFA and commodity futures prices, respectively, z t-1 (= FFA t-1 - FUT t-1 ) is the lagged ECT, which represents the long-run relationship between the two derivatives markets, İ i,t are stochastic error-terms that follow an as- yet-unspecified conditional distribution, with mean zero and time-varying covariance matrix H t and a FFA,i , b FFA,i , a FUT,i and b FUT are short-run coefficients.

If some non-zero b

FFA,i

(a FUT,i ) coefficients, i = 1, 2, ..., p-1, are statistically significant in Equation 7a (7b) then a unidirectional causality exists from commodity futures (FFA) to FFA (commodity futures), and it is argued that FUT t (FFA t ) Granger causes FFA t (FUT t ). A two-way feedback relationship between FFA t and FUT t prices exist if both b

FFA,i

and a FUT,i coefficients are significant. These hypotheses are tested by employing a Granger (1988) Wald test on the joint significance of the lagged estimated coefficients of ǻFFA t-i or ǻFUT t-i . When heteroskedasticity is

evident in the residuals of the error-correction equations, the t-statistics are adjusted by White's

(1980) heteroskedasticity correction. Finally, when no cointegration is established between FFA 11 and commodity futures price series, a bivariate Vector Autoregressive (VAR) model is estimated instead of a VECM, excluding the z t-1 term from Equations (7a) and (7b). Impulse response functions are further estimated to provide a more detailed insight on the spillover relationships, by measuring the reaction of FFA and commodity futures prices in response to one standard error shocks in the equations of the VAR and VECM models, estimated as Seemingly Unrelated Regressions (SUR) systems. Generalised Impulse Responses (GIR) are estimated to overcome the issues induced by the orthogonalization of the underlying shocks through the Cholesky decomposition of the covariance matrix of Equation (5) (see Pesaran and

Shin, 1998).

The conditional second moments of FFA and commodity futures prices are estimated using the following bivariate Generalised Autoregressive Conditional Heteroskedasticity (GARCH) model with the Baba et al. (1987) augmented positive definite parameterisation in order to capture higher moment dependencies (volatility spillovers): H t = A'A + B'H t-1 B + C'İ t-1 İ t-1 'C + S1'u 1,t-1 u 1,t-1 'S1 + S2'u 2,t-1 u 2,t-1 'S2 + E'(z t-1 ) 2

E (8)

where, A is a (2x2) lower triangular matrix of coefficients, B and C are (2x2) diagonal coefficient matrices, S1 and S2 are the matrices of the spillover effect parameters, u 1,t-1 and u 2,t-1 are matrices of lagged square error-terms and E is a diagonal matrix containing the coefficients of the squared error-correction term, ec 11 , ec 22
. In this setting, u 1,t-1 is the volatility spillover effect from the FFA market to the commodity futures market and u

2,t-1

is the volatility spillover effect from the commodity futures market to the FFA market. The element of S1 (S2), s1 21
(s2 12 ), measures the spillovers of the FFA (commodity futures) volatility equation to the volatility of the commodity futures (FFA) equation. By incorporating the lagged squared ECT in the conditional variances and covariance, the model is capable to highlight the potential relationship between disequilibrium (measured by the ECT) and risk (measured by the conditional variance) (see Lee, 1994). Once again, when no cointegration is discovered between FFA and commodity futures price series, a BEKK VAR-GARCH model is estimated, as in Equation (9), but without including the lagged squared ECT, (z t-1 ) 2 . The following bivariate Exponential-GARCH (EGARCH) model of Nelson (1991) is used, when asymmetries are observed in the conditional variances; that is, positive returns are followed by higher volatility than negative returns: 12 H t = exp [A'A + B'H t-1 B + C'İ t-1 İ t-1 'C + D' İ t-1 İ t-1 'D + S1'u 1,t-1 u 1,t-1 'S1 + S2'u 2,t-1 u 2,t-1 'S2] (9) where, the coefficients are as previously defined and the diagonal (2x2) D matrix measures the asymmetry effects of shocks on volatility (d ii ): ,1 ,1 ,1 ,1ii ii t i ii t ii t ii t dE (10) When the EGARCH model fails to eliminate the asymmetries in the data, the asymmetric GJR-

GARCH model of Glosten

et al. (1993) is used instead that allows positive and negative innovations to returns to have different impact on the conditional variance. In a bivariate BEKK- VECM setting, the conditional variance according to the GJR model is defined as: H t = A'A + B'H t-1 B + C'İ t-1 İ t-1 'C + D'ȗ t-1 ȗ t-1 'D + S1'u 1,t-1 u 1,t-1 'S1 + S2'u 2,t-1 u 2,t-1 'S2 (11) where, D is a 2x2 diagonal matrix of the coefficients of asymmetry, ȗ t-1 = İ t-1 G t-1 , and G t-1 is a 2x1 vector of indicator variables which take the value of 1 if İ t-1 < 0 and 0 otherwise. If the coefficient of the indicator variable (G t-1 ) is positive and significant, this indicates that lagged negative innovations have a larger effect on returns than positive ones, and thus, nonlinear dependencies in the volatility of the returns exist.

The conditional Student-

t distribution is used as the density (likelihood) function of the error-term t and the number of degrees of freedom v is treated as another parameter to be estimated. Baillie and Bollerslev (1995) show that for v < 4, the Student- t distribution has an undefined or infinite kurtosis. In such cases the Quasi-Maximum Likelihood Estimation (QMLE) of Bollerslev and Wooldridge (1992), which estimates robust standard-errors and yields an asymptotically consistent normal covariance matrix, is used. The most parsimonious specification for each model is estimated by excluding insignificant terms. Finally, the Broyden, Fletcher, Goldfab, Shanno (BFGS) algorithm (see Broyden, 1967) is used, which maximize the log-likelihood function, in order to estimate the parameters of the GARCH models. 13

3. DATA AND PRELEMINARY STATISTICS

The dataset used in the paper consists of daily Baltic Forward Assessments (BFAs) obtained from Reuters and commodity futures price series from Bloomberg, Datastream International and Tick Data. The period investigated extends from May 2006 to October 2009, yielding a total of 868 daily observations. Nearby (prompt) BFAs and commodity futures are used in order to include contracts with the highest liquidity among the contracts of different durations. These are rolled

over to the next nearest contract before the nearby contract expires to circumvent the issues of thin

market and expiration effects. When a market is not open on a given day, for a national holiday or any other event, the corresponding returns in all other markets are removed from the sample. BFAs are mid bid and offer FFA market prices based on average FFA prices reported by a panel of dry-bulk FFA brokers (namely, the panelists) appointed by the Baltic Exchange. Every business day, the panelists submit their expert estimation of mid FFA market prices for the trading shipping routes defined by the Baltic Exchange. BFAs are regarded as the most representative FFA data, as they include information from the most active FFA brokers. The used BFA (henceforth FFA) data consists of freight derivatives contracts on C4 (Richards Bay in South Africa to Rotterdam) and C7 (Bolivar in Columbia to Rotterdam) routes of the Capesize index, on P2A (Skaw-Gibraltar range to a trip in the Far East) route of the Panamax index, and on time-charter baskets. The choice of routes for which FFA prices are taken is determined by the availability of corresponding

futures prices on commodities carried on the particular route. The baskets of time-charter rates are

constructed by the Baltic Exchange's dry-bulk time-charter routes of the Capesize, Panamax, Supramax and Handysize indices. The composition of each FFA time-charter basket is detailed in

Table 1.

The weights reported next to the route of each vessel size illustrate the most dominant cargoes transported in each case. As can be seen, iron ore and coal are the major commodities transported in the routes of the Capesize basket. The Panamax basket includes a wider range of commodities

transported (for example, grains, coal, iron ore, bauxite and sulphur). Finally, the Supramax basket

includes an even more diverse list of commodities transported (grains, fertilizers, steel, petcock and scrap). Four commodity futures contracts (coal, corn, wheat and soybeans) are employed in the paper. These commodities reflect the major commodities transported in the underlying routes of the aforementioned FFA contracts. Coal futures refers to the Richards Bay All Publication Index-4 (API4) coal futures, which trade in the European Energy Exchange (EEX) and are regarded as the underlying commodities of the Capesize trades. Corn, wheat and soybeans grain futures contracts trade at the Chicago Mercantile Exchange (CME) and are regarded as the 14 underlying commodities of the Panamax and Supramax trades. All commodity futures are quoted in free on board prices. The inclusion criteria of the aforementioned commodity futures in the ensuing analysis are data availability, trading activity (liquidity) and the importance (weight) of each commodity in the cargoes of each FFA basket constituent routes. Consequently, sulfur, iron ore and minor commodity futures contracts (salt, clinker, and pet coke) do not trade in an organized exchange, and thus, were not included in the analysis. Due to the different commodities transported by the investigated vessel markets, the use of single commodity futures may not proxy sufficiently the actual composition of the cargoes transported in some cases. To account for the different commodities transported by the vessels of the underlying BFA baskets and routes, synthetic equally weighted commodity futures baskets are also constructed. The synthetic futures baskets comprise the major commodities transported by each vessel type. Specifically, the synthetic basket 1 consisting of wheat, corn, soybean and API4 coal futures is used as a proxy for the cargoes of the Panamax trades. The synthetic basket 2 proxies the cargoes of the Supramax trades and includes wheat, corn, soybean futures. As there is no

futures contract for iron ore, a synthetic commodity futures basket could not be constructed for the

cargoes of Capesize trades, and therefore, Capesize FFA trades are compared only with coal futures contracts. Moreover, the non-synchronous trading times between the investigated markets may induce serial correlation (predictability) in the residuals of the used models. In any given day, the European Energy Exchange - EEX closing futures prices are published at 15:30 London time (16:30 in Leipzig, Germany), the Baltic Exchange BFA prices are announced at 17:30 London time, while CME closing futures prices are published at 19:15 London time (13:15 in Chicago). Thus, the

Baltic Exchange market on day

t, by announcing the prices of the day before the CME market on day t, CME commodity futures prices may be able to assimilate more information than BFA forward prices, and thus, may exhibit an informational lead (predictability element) in comparison

with BFA prices. In the literature, instead of using daily close-to-close prices, daily open-to-close

prices from daily close-to-open prices are separated (see Lin et al., 1994, among others). Others researchers lead one day ahead the market data for the market that closes before the other markets in their samples (see Kao and Wan, 2009, among others). Others use overlapping (rolling) multiday returns (see Forbes and Rigobon, 2002), while others revert to lower-frequency (e.g. weekly) data sets (see Chulia and Torro, 2008, among others). However, all the above solutions are not suitable when returns are autocorrelated or when testing for information spillovers (predictability). Thus, in order to ensure that the spillover inferences are not biased by a non- 15 synchronous trading problem among the markets a "time-matched" data set is created by intra-day data purchased from Tick Data, in which the prices of all commodity futures contracts at CME are retrieved on a daily basis at 17:30 (London time) to match the exact publication time of the BFA prices in London 16 . Table 2 provides summary statistics of the logarithmic first-differences of FFA and commodity

futures price series. Sample means are statistically zero in all cases. The most volatile series, based

on the standard deviation values, are the FFA baskets which exhibit higher (approximately double) values, compared to the standard deviations of the commodity futures series. The standard deviation of the FFA routes range somewhere between the standard deviations of FFA baskets and commodity futures series. Skewness values indicate that all series, besides route C4 and the corn

and wheat futures, exhibit statistically significant (positive or negative) skewness. All series have

significant excess kurtosis, with FFA excess kurtosis values being substantially higher than that of

commodity futures prices. In turn, Jarque-Bera (1980) tests indicate departures from normality for all price series examined. The discrepancy between the standard deviation and kurtosis values among the FFAs and commodity futures highlight the difference in terms of the distributional attributes of these markets. The Ljung-Box Q-statistic (Ljung and Box, 1978) based on up to 12 lags of the sample autocorrelation function indicates strong serial dependence in all FFA price series, but no serial correlation in the commodity futures series. The Ljung-Box

Q-statistic,

applied on the squared series, and the ARCH test (Engle, 1982) indicate existence of heteroscedasticity and ARCH effects, respectively in all commodity futures series and only in route-specific FFA series. No ARCH effects are evidenced in the case of FFA baskets, where their variances appear to be homoskedastic. Table 3 reports unit root tests for the FFA and commodity futures price series. The Augmented Dickey-Fuller (ADF, 1981) and Phillips and Perron (PP, 1988) conventional tests, applied on the log-levels and log-first differences of all price series, reveal that all variables are log-first difference stationary, all having a unit root on the log-levels representation. Support for the log- first difference stationary assumption is also provided by the Kwiatkowski, et al. (KPSS, 1992)

test. Regarding the unit root testing in the presence of breaks, Lee and Strazicich (2003, 2004) test

results indicate that the hypothesis of a unit root with break(s) cannot be rejected for all of the employed logarithmic price series. Thus, all series considered are non-stationary on the log-level representation. As different markets have different speeds of adjustment and reaction to breaks 16

Unfortunately, this procedure cannot be applied to EEX coal data as they are reported before the BFA prices on any

given day and thus the closing prices of each day are used in the ensuing analysis. 16 induced by shocks into the economic system, and since the break points are estimated endogenously in the two Lee and Strazicich models, it is expected that the break points may differ

across the markets. However, it seems that in the freight derivatives market a break, in most of the

series, occurs unanimously between the two models around May-June 2008, when the freight markets collapsed. The P2A route is the only FFA series that exhibits two structural breaks (the B 1t and B 2t dummy variables are significant at 10% and 5% significance levels, respectively). The first break occurs nearby the end of 2007 (together with the statistical significant breaks in the CTC and STC baskets), when the sub-prime crisis was initiated in the US. The second break occurs in the middle of 2008 (together with a significant break in route C4), when freight prices had reached an all-time high record level, due to market expectations for a buoyant future demand for seaborne transportation services and subsequently fell to times their previous values in the space of just a few months. In contrast, all commodity futures series (including the two

synthetically constructed baskets), besides the coal series, exhibit two significant break points, the

first occurring between the end of 2007 to early 2008, when the sub-prime crisis showed in its full extent, and the second occurring between the end of 2008 to early 2009, when commodity market prices started to pick up. Johansen's cointegration tests, reported in Table 4, show that in three out of the fifteen FFA and commodity futures pairs a cointegrating (long-run equilibrium) relationship exists. These are the Capesize (CTC basket, C4 and C7 routes) series with the coal futures (API4) series. The Schwartz Bayesian Information Criterion (SBIC, Schwarz, 1978), used to determine the lag length of the VAR models, indicates two lags in all cases. As mentioned earlier, the Johansen's test can be misleading in the presence of structural breaks, as it may incorrectly accept the null of no cointegration, when in fact cointegration with a structural break exists. For this purpose the Gregory and Hansen (1996) test for cointegration is employed in the cases where cointegration with the Johansen test is not found. The former tests the null of no cointegration against the alternative of cointegration with a possible break. The test reveals that cointegration, in the

presence of structural change, exists in five additional pairs of FFA with commodity futures series.

These are the PTC basket, the STC basket and route P2A with soybean futures, the PTC basket

with the synthetic 1 futures basket and the STC basket with the synthetic 2 basket. In all cases, the

time of the break is situated around December 2007, which coincides with the start of the global

financial crisis. Moreover, the coefficients of the cointegrating vector are statistically significant in

all cases according to the t-statistics. On the other hand, none of the wheat and corn commodity futures markets are found to be cointegrated with the freight derivatives markets by either the Johansen (1988) or the Gregory and Hansen (1996) tests. 17 Overall, cointegration results seem to be related with the type and importance of commodity cargoes transported by each type of vessel. For instance, all FFA prices are cointegrated with coal and soybean futures, which constitute major commodities in the Capesize and Panamax/Supramax trades, respectively. The same holds between the time-charter FFA baskets and the synthetic commodity baskets, being aggregate " indices" of major time-charter freight rates and commodity

prices, respectively. On the other hand, after examining the structural stability of the systems, it

can be robustly stated that there is no evidence of cointegration between wheat and corn futures and their respective FFAs. This result can be due to the lesser importance of these commodities in the relevant physical trades. The extant literature on whether cointegration between interrelated futures markets exists is contentious. Among the studies that find cointegration between futures markets is that of Liu (2005) on commodity (corn, hog and soybean) futures, while studies that report no evidence of cointegration between futures markets include those of Low et al. (1999) on commodity futures and Chulia and Torro (2008) on stock and bond futures markets, among others. The next section examines the informational spillovers between the investigated FFA and commodity futures markets.

4. SPILLOVER EMPIRICAL RESULTS

4.1. Spillovers under Cointegrated Relationships

Table 5 presents the return and volatility spillover results for the pairs of FFA and commodity futures prices that stand in a long-run (cointegrating) relationship. As it can be seen, for the Capesize trades (CTC-Coal, C4-Coal, and C7-Coal) and the STC-Soybean pair an asymmetric VECM-EGARCH process is found to provide a better fit to the data, while for the PTC-Soybean pair a VECM-GJR-GARCH process best fits the data, all eliminating any asymmetries, as shown

by the Engle and Ng (1993) test statistics (sign bias, negative size bias, positive size bias and the

joint test of sign and size bias) presented in panel C of the same table. In contrast, for the remaining Panamax (PTC-Synthetic 1 and P2A-Soybean) and Supramax (STC-Synthetic 2) trades, symmetric VECM-GARCH models are estimated, as there is no evidence of asymmetries

in the conditional variance. The diagnostic tests reported in panel C indicate that the standardised

residuals of the employed models in all cases are free from serial correlation (with the exception of the C4, C7 and STC equations, where the Newey-West autocorrelation correction is used) and heteroskedasticity. Furthermore, the Engle's (1982) heteroskedasticity test also indicates no

ARCH effects.

18 Panels A and B, of the same table, present the maximum-likelihood mean (return) and variance

(volatility) parameters estimates of the estimated models, respectively. More specifically, in panel

A, in all cases, the ECT coefficients (

q j ) are statistically significant. In all Panamax and Supramax

pairs, ECT coefficients attain opposite signs; that is, the negative FFA coefficients and the positive

commodity futures coefficients are in accordance with convergence towards a long-run

equilibrium. Thus, in response to a positive forecast error, the FFA prices will decrease, while the

commodity futures prices will increase in values in order to restore the long-run equilibrium. In contrast, in the three Capesize pairs, the ECT coefficients are positive (and significant in most cases) in both FFA and commodity futures equations. This finding may be partially explained by the existence of a structural break in the cointegrating system, following the Gregory and Hansen (1996b) earlier results. According to the short-run dynamics of the models, as shown by the statistically significant b FFA,1 coefficient in the FFA equations and the statistically insignificant a FUT,1 and a

FUT,2

coefficients in (most of) the commodity futures equations, and in accordance to the statistical significance of the Granger causality (Wald) tests, it seems that commodity futures returns have a unidirectional positive impact on FFA returns in all investigated Panamax and Supramax cases. Thus, market information is discovered first in commodity futures markets (soybeans and the two synthetic commodity baskets) and then it appears in the FFA markets. This is expected, as the demand for freight is created in commodity markets, resulting in freight markets lagging behind commodity

markets in their reaction to news in these markets. These results can also be partially justified by

the fact that commodity futures markets are more liquid with lower transactions costs compared to

FFA markets. Fleming

et al. (1996) argue that the market with the lowest overall trading costs (and higher liquidity) will react more quickly to new information and thus, provide the price discovery function.

In contrast to

a-priori expectations, in Capesize markets there is no spillover relationship in either direction; that is, neither coal futures nor Capesize FFAs, on the time-charter basket (CTC) or on individual routes (C4 and C7), are capable of transmitting any new information to the other market and in that way act as price discovery vehicles. This may be due to the fact that transportation costs for coal represent a higher percentage of the final price of the commodity in comparison to grain commodities, as in some instances they may account for 70% of the CIF price of coal, while grains account for about 30% of the CIF price of grains. This in turn, may erode the signaling power of coal futures markets for the respective FFA markets, as the latter seem to have a dominant role in the formation of the coal futures price. On the other hand, and according to the 19 empirical results, Capesize FFA markets do not spill information to coal futures markets as well. This finding is in accordance with the results in all other FFA markets, where FFAs lag behind commodity futures in terms of information assimilation. Panel B presents the parameter estimates of the conditional variance of the models, where the lagged disequilibrium squared error-term is also included as explanatory variable in the conditional variances of the model. The statistical significance of the lagged error-terms ( kk c) and lagged variance-terms ( kk b) of the variance equations indicate that volatility is time-varying in all cases. Besides the PTC-Soybean pair, in all other cases the coefficients of the squared lagged

ECTs (

ec i ) are statistically significant and negative in most FFA and commodity futures equations. Thus, the ECT of the previous period has important predictive power for the conditional variances of cointegrated series and should be included in the volatility models. Finally, out of the eight examined pairs, in three pairs (C4-Coal, PTC-Synthetic 1 and STC-Synthetic 2) there is a unidirectional volatility spillover from the commodity futures to the FFA markets and in two pairs (C7-Coal and STC-Soybean) there is a bi-directional causal relationship. The magnitude of the s i coefficient shows that this bi-directional causal relationship runs stronger from the commodity futures to the FFAs. Finally, only in two FFA basket cases (CTC-Coal and PTC-Soybean) there seems to be no volatility spillover between the markets and in one case (P2A-Soybean) FFAs seem to spillover volatility to the commodity futures. Overall, in most cases, commodity futures informationally lead the freight derivatives market in both returns and volatilities.

4.2. Spillovers under Non-Cointegrated Relationships

The return and volatility spillover results for the pairs of FFA and commodity futures prices that do not stand in a cointegrating relationship are shown in Table 6. Results indicate that for the PTC-Corn, P2A-Synthetic 1 and STC-Corn cases a VAR-GJR-GARCH process effectively captures the asymmetries present in the data, while for the P2A-Corn pair a VAR-EGARCH process best fits the data. These results can be seen by the Engle and Ng (1993) tests, which are presented in panel C of the same table. On the other hand, for the remaining three pairs, a symmetric VAR-GARCH is fitted to the data successfully. Results of the Ljung-Box (1978) statistics for 12 th -order serial correlation of levels and squared levels of standardized residuals and Engle's (1982) test, presented in panel C, indicate absence of any serial correlation and heteroskedasticity, respectively. 20 Panel A of table 6 presents the maximum-likelihood mean parameter estimates of the estimated models. As can be seen, in six out of the seven pairs, where cointegration is not found even after accounting for the possibility of a structural break in cointegrated systems, a unidirectional positive spillover effect exists from the commodity futures returns to the FFA returns. This is documented by the positive and statistically significant b FFA,1 and b

FFA,2

coefficients in the FFA equations and the statistically insignificant a FUT,1 and a

FUT,2

coefficients in the commodity futures

equations, as well as by the statistical significance of the Granger causality tests. These spillover

findings in the returns are in accordance with earlier results coming from cointegrated markets, where market information is discovered first in commodity futures markets and then it appears in FFA markets. Thus, so far it seems that regardless of the existence of a long-run cointegrating relationship, commodity futures informationally lead FFA returns. In contrast, in the case of the P2A-Corn pair an inverse unidirectional relationship is found; that is, there is a statistically significant spillover relationship from the P2A FFA market to the corn commodity futures market, which is in contrast to the original expectations. It seems that FFA

trades discover prices prior to corn futures trades, as new information is revealed and incorporated

first in the FFA returns, before it is spilled over to corn returns. These results can be partially explained by the fact that corn physical trades affect to a larger extent the demand of smaller vessel types (e.g. Supramax vessels) which are used for their transportation than larger ones (e.g. Panamax vessels) that carry mostly iron ore and wheat cargoes. It follows that any price discovery function between the relevant freight and commodity derivatives markets is more clearly evidenced in the smaller Supramax vessels than in the larger Panamax ones. Panel B of Table 6 presents the parameter estimates of the conditional variance models. The statistical significance of the lagged error-terms ( kk c) and lagged variance-terms ( kk b) of the

variance equations indicates once again that volatility is time-varying in all cases. In three out of

the seven pairs examined (PTC-Corn, PTC-Wheat and STC-Wheat), there is a unidirectional volatility spillover from the commodity futures to the FFA markets. A bi-directional causal relationship is found in two pairs (P2A-Wheat and P2A-Synthetic 1) which, according to the magnitude of the s i coefficient, runs stronger from the commodity futures to the FFAs. It seems that time-charter baskets of freight rates and the individual P2A route of the Panamax trades are able to receive successfully new information from commodity futures markets. Overall, in most cases, commodity futures informationally lead the freight derivatives market in both returns and 21
volatilities. In only two cases (P2A-Corn and STC-Corn), there is no evidence of volatility spillovers in either direction.

4.3. Impulse Response Analysis

By analysing the Generalised Impulse Responses (GIR) functions of a SUR-VAR (when cointegration is not found) or of a SUR-VECM (when cointegration is found) an insight into the dynamics of the causal relationship between FFA and commodity futures markets can be obtained. Impulse responses measure the reaction of FFA and commodity futures prices in response to one unit standard error shocks in the equations of the models. Figure 1 presents the time paths of the FFA and commodity futures price innovations for a ten days-ahead horizon, first in the FFA returns (upper graphs) and second in the commodity futures returns (lower graphs). Only the responses of the FFA Capesize time-charter basket with coal futures (CTC-API4), of the FFA Panamax time-charter basket with the synthetic 1 basket (PTC-SYN1), and of the FFA Supramax time-charter basket with soybeans futures (STC-SOY) are shown in order to conserve space 17 . In the FFA Capesize basket (CTC) with the API4 coal futures it can be seen that the adjustment time varies between the two price series, taking approximately 4-5 days to revert back to the

original state after the shock for the FFA CTC prices (solid line in the upper graph). Adjustment in

commodity futures prices (dashed line in the lower graph) takes place in half the period, as it takes

around 3 days for the FFA CTC prices to adjust. An overshooting is observed in the FFA CTC prices, while coal futures prices exhibit a lower impact. The most important finding, however, is that when the FFA CTC prices are affected by a shock (in the upper graph), coal futures prices remain almost unaffected. The same also holds true when the coal futures prices are affected by one standard error shock (in the lower graph) with FFA CTC prices not responding. These findings are in accordance with earlier results of no spillover relationships from either market. Consider next the case of the FFA Panamax basket (PTC) with the commodity futures synthetic basket (SYN1). When the FFA PTC prices are subjected to a shock (in the upper graph), the commodity futures prices react to the new " news" in the market that originate from the shock and respond accordingly in order to incorporate the news into prices. When the commodity futures prices are subjected to a shock (in the lower graph) the FFA PTC prices do not seem to have the information assimilation capacity to respond to the shock. The same findings also hold in the case of the FFA Supramax (STC) basket with the soybeans commodity futures. These findings are in accordance with earlier Granger causality test results. 17 The responses for the remaining markets are available from the authors upon request. 22
Overall, it seems that commodity futures returns respond to new information coming from the FFA market and arrive at a long-run equilibrium level more rapidly than their corresponding FFA prices, but not the other way around. Thus, it seems that investors, which collect and analyse new market information on a daily basis, are not indifferent about transacting in these derivatives markets, and as such, new information is revealed first in the commodity futures market, before it is spilled over in the FFA market.

5. DISCUSSION

Overall, results indicate that commodity futures, in general, lead FFAs both in returns and volatilities. As far as returns are concerned, this is confirmed for the Panamax and Supramax markets, where only unidirectional spillovers from commodity futures returns to freight FFA returns were found. In contrast, it seems that there is no relationship in returns between Capesize and coal derivatives markets. Regarding volatility spillovers, again it seems, in most cases, that there is a unidirectional relationship from commodity futures to the time-charter baskets FFAs and a bi-directional relationship between commodity futures and single-route FFAs, with the latter relationship running stronger from the commodity futures to the FFA market. Finally, the synthetic commodity futures baskets, which aim to replicate the structure of the FFA contracts in the Panamax and Supramax trades, also seem to be able to spill new information to the FFA markets, both in returns and volatilities. The above empirical findings can be explained as follows: First, commodity futures, which trade in well-organised markets, are more liquid than FFAs, which trade OTC, with their average daily contract trading volume being roughly triple that of FFA contracts. As such, commodity futures are regarded as more efficient markets, subjected to less market frictions and mispricing.

McMillan and Ülkü (2009) argue that as the volume of trades increase in futures markets, the price

discovery function is strengthened, as the futures markets become more informationally efficient.

Chordia,

et al. (2008) argue that higher liquidity attracts arbitrage trading, leading to diminishing return predictability and eventually to a higher market efficiency. Chung and Hrazdil (2010) also report that increased liquidity enhances market efficiency, after controlling for the effects of market capitalization, trading volume and trading frequency. Second, less trading costs result to higher trading and to a more efficient market (see Chordia, et al., 2011). The examined commodity futures have less transactions costs in comparison to the FFA trades, as in a FFA trade the brokerage fee is typically 0.25% of the value of the contract, in addition to the fees of the clearing-house if the trade is cleared. 23
Third, as FFA contracts are risk management instruments for exposures reciprocating from operations only in the maritime industry, and since they represent an " unconventional" family of commodity derivatives markets, it is not expected to attract as much trading interest from institutional investors as the mainstream commodity futures markets examined in the paper. In the literature, institutional investors are well-informed and rational, while individual investors are treated as uniformed, exhibiting disposition effects and overconfidence (see Dhar and Zhu, 2006; and Kim and Nofsinger, 2007, amo

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