Trading volume and volatility in the shipping forward freight market

Transportation Research Part E 49 (2013) 250–265

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Trading volume and volatility in the shipping forward freight market Amir H. Alizadeh ⇑ Faculty of Finance, Cass Business School, 106 Bunhill Row, London EC1Y 8TZ, United Kingdom

a r t i c l e

i n f o

Article history: Received 24 January 2012 Received in revised form 9 July 2012 Accepted 10 July 2012

Keywords: Shipping Forward freight agreement Volatility Trading volume Causality

a b s t r a c t This paper investigates the price volatility and trading volume relationship in the forward freight agreement (FFA) market for dry bulk ships over the period 2007–2011. It is found that FFA price changes have a positive impact on trading volume, suggesting a momentum effect as higher capital gains encourage more transactions. There is also evidence of a contemporaneous and positive relation between trading volume and volatility, which is in line with evidence from financial markets and the Mixture of Distribution Hypothesis. However, increases in price volatility lead to lower future trading activities in the FFA market. Ó 2012 Elsevier Ltd. All rights reserved.

1. Introduction Shipping is an integral part of the global transportation and logistics network and has always been considered as one of the most volatile industries, where agents are exposed to substantial financial and business risks. The risk in the shipping industry predominantly emanates from fluctuations in freight rates which in turn affect the cash flows of ship-owners, operators and charterers. As a result, market participants have developed different methods to manage freight market risk including using long term contracts such as period-charter, contract of affreightment, and bareboat charter. Although these physical contracts can effectively protect ship-owners and charterers from adverse freight fluctuations and risk, they have their own drawbacks including long term physical commitment, low trading activity in long term contracts, and counterparty risk, among others. Despite the need for a more efficient risk management mechanism in shipping markets, it was not until the mid 1980s that a derivative market for freight rate was introduced.1 From the early 1990s a new market for trading the forward value of shipping freight rate was emerged as a response to the deficiencies of the Baltic International Freight Futures contract, BIFFEX, as a hedging instrument. In particular, the participant in the dry shipping market wanted a hedging tool which would provide a more precise match to their exposure in the physical market and, hence, a more accurate hedging mechanism. A forward freight agreement could be a more effective instrument in mitigating the risk involved in dry bulk freight market due to its nature and structure. A forward freight agreement (FFA) is defined as an agreement between two counterparties to settle a freight rate or hire rate for a specified quantity of cargo or type of vessel for one of the major shipping routes (or a basket of routes) in the dry-bulk or the tanker markets at a certain date in the future. The underlying asset of the FFA contracts can be any of the routes which constitute the indices ⇑ Tel.: +44 207040 0199; fax: +44 207040 8186. E-mail address: [email protected] To allow shipping market participants to trade freight derivatives, the Baltic Exchange started to publish the first daily freight index in January 1985. The Baltic Freight Index (BFI) initially constructed from freight rates in 13 shipping routes covering a variety of cargoes ranging from 14,000 metric tonnes (mt) of fertilizer up to 120,000 mt of coal and was developed as a settlement mechanism for the then newly established BIFFEX futures contract. However, soon it was realized that the BIFFEX futures contract, which was based on an underlying index (BFI) calculated as average of freight rates over a number of routes, was not an effective instrument to manage freight rate risk in individual shipping routes (see Kavussanos and Nomikos, 2000). 1

1366-5545/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.tre.2012.08.001

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Fig. 1. Estimates of annual FFA trading volume in the dry bulk market.

produced by the Baltic Exchange, or by other reputable providers of underlying market information, such as Platts. FFAs are settled in cash on the difference between the contract price and an appropriate settlement price which is normally average of spot rates of the underlying route over a period of time (e.g. one calendar month).2 Over the last 20 years the forward freight market grew rapidly and despite the economic decline in the second half of 2008, and consequent slow down in international shipping, trade in freight derivatives instruments is still developing and plays a significant role in risk management process in international shipping markets. The growth of transactions in the FFA market is also evidenced in Fig. 1, which presents the estimated number of freight contracts traded each year in lots.3 According to market sources, in February 2008 the total value of trades in the market was about $150bn. The development of the forward freight market also attracted a lot of interest from non-shipping industry as traders from investment banks and hedge funds realized the potential benefit of these new instruments for speculation and diversification.4 However, the trading volume in the FFA market declined following the effects of credit crunch and slowdown in sea transportation and shipping markets activities since 2008. The trading volume in dry bulk FFAs has stabilized to around 1 million lots per year in total as illustrated in Fig. 1. There have been a number of studies on the dynamic behavior, hedging effectiveness, forecasting and other risk management issues related to the shipping derivative contracts. Kavussanos and Visvikis (2006) provide a thorough survey of the literature on shipping freight derivatives. For instance, Kavussanos and Visvikis (2004) investigate the market interactions in returns and volatilities between spot and forward freight rates in the Panamax sector, while Kavussanos et al. (2004b) examine whether FFA is an unbiasedness predictor of futures spot rate in 4 routes of the Panamax market. Kavussanos et al. (2004a) study the effect of development of the Panamax FFA market on volatility of the Panamax spot freight rate and argue that increased activities in the FFA market has had a stabilising effect on the spot rates. Batchelor et al. (2005) investigate the relationship between bid-offer spread and volatility of the FFA prices and find that increase in bid-offer spread is an indication of agents’ expectations on future market uncertainty and increase in volatility of FFA prices. In another study, Batchelor et al. (2007) argue that using the FFA prices along with spot prices in multivariate dynamic models can improve the forecasting performance of both spot and forward freight rates, while Koekebakker and Adland (2004) investigate the forward freight rate dynamics using a term structure model. Angelidis and Skiadopoulos (2008) investigate the suitability of different time-varying volatility models for the FFA prices in terms of estimating the Value-at-Risk. Other studies such as Tvedt (1998) and Koekebakker et al. (2007) focus on pricing options on forward freight rates. Despite this plethora of studies on different aspects of forward freight agreements such as the dynamic behavior, risk management benefits, and predictive power of FFAs, there has been no study to investigate the relationship between price volatility and trading activity of forward freight rates. Hence, this paper attempts to fill this gap in the literature by investigating, for the first time, the relationship between trading volume and price movements in the FFA market. The analysis of dynamic relation between price and trading activity in the FFA market is important both in economic and econometric terms. First, the interrelationship between price and trading activity, and price volatility and trading volume, is essential in understanding how the FFA market functions and how the participants behave or make decisions according to the arrival of information. From the econometric point of view, establishing the correct relationship between price, volatility and volume is essential in modeling and forecasting these variables as well as setting up risk management functions and trading strategies. 2

See Alizadeh and Nomikos (2009) for detail definition FFA contracts and description of their settlements. One lot is defined as either one day of hire or 1000 metric tonne of cargo. For instance, an FFA contract for 60 days hire a considered as 60 lots, whereas one FFA contract for transportation of 150,000 metric tonnes of cargo is considered 150 lots. 4 ‘‘Freight Futures surge as Funds seek Refuge’’, Financial Times, February 24, 2008. 3

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There are basically two questions that this paper attempts to address. First, the paper investigates whether trading volume contains useful information for predicting fluctuations in FFA prices and vice versa. In this respect, the information content of trading volume and FFA price changes are examined in a multivariate setting to assess the ability of one variable to predict the direction and magnitude of the other variable. Second, the paper investigates whether there is a relation between the volatility of FFA prices and market activity as measured by trading volume. Examination of those issues is of considerable interest to academics and practitioners alike since the price–volume relationship has important implications for areas such as modeling and forecasting FFA prices and their volatilities as well as for the purposes of developing trading and risk management strategies in the shipping forward freight markets. Investigating the trading volume and volatility relation in the market for forward freight agreements is also interesting due to the unique features of this market. For instance, this is a derivative market for non-storable sea transportation service. In addition, the dry bulk FFA market is a relatively new derivative market which is global in nature with participants from different parts of the world. Also, the unique underlying characteristics of the FFA market including: the lack of price transparency in the market, illiquidity and thin trading, and transition from OTC to cleared contract trading over the life of the FFA market, are important issues which make this market interesting to investigate. This study contributes to the literature in several ways. First, since almost all the previous studies examine the price–volume relationship in financial markets, by investigating this relation in a market where derivative for sea transportation service is traded, the paper examines the validity of the existing theories in a different setting. Second, the level of trading activity in the dry FFA market is relatively low compared to other commodity and financial markets studied in the literature. In this respect, this study provides empirical evidence on the price–volume relationship in a relatively new market with special characteristics including thin trading and no centralized trade execution system. Third, the FFA market is not as transparent as are commodity and financial markets since most of the transactions are based on private negotiations between buyers and the sellers, through an FFA broker; this feature of the market makes the investigation of the price–volume and volume–volatility relations even more interesting. Fourth, the analysis sheds new light on issues such as the behavior of FFA prices and the information content of variables such as market activity. Finally, this paper provides a further dimension to the literature by comparing the price–volume and volume–volatility relations in the FFA market for different size vessels as well and FFA contract maturities in the dry bulk sector. The structure of the paper is as follows. Section 2 reviews previous studies on the price–volatility relation and highlights their conclusions. Section 3 presents the methodology used in this paper to investigate the price–volume relation in the market for freight derivatives. The data and their properties are discussed in Section 4, while Section 5 presents the empirical results and discussion. Implications of findings and conclusions are the subject of the last section.

2. Review of literature on price–volume relationship Several attempts have been made in the literature to determine the true underlying relationship between price and trading volume in different markets, both at a theoretical and at an empirical level. The general consensus is that there is a positive relationship between trading volume and price changes in financial markets. Perhaps the most important study, which brings together the results of several earlier studies in different markets, is the one by Karpoff (1987). He classifies earlier studies into two groups; first, those that examine the relationship between absolute price changes and trading volume, and, second, those that examine the relationship between price changes per se and trading volume, and finds that the majority of them report a positive relationship between price changes (per se or absolute) and trading volume. He also argues that, although many researchers use linear monotonic models to investigate the price change–volume relationship, there might be some form of asymmetry in this relationship. He finally points to the theories which are suggested to explain the positive price variability and volume relationship. These are the Mixture of Distribution Hypothesis (MDH) of Clark (1973) and the Sequential Information Flow (SIF) of Copeland (1976). The MDH of Clark (1973) is based on the assumption that both price changes and volume follow a joint probability distribution. Consequently, price changes and trading volume should be positively correlated because they jointly depend on a common underlying variable, which is normally interpreted as the random flow of information to the market. This means that both price changes and trading volume simultaneously respond to the new information and they are contemporaneously correlated. Additional evidence in support of the MDH is also provided by Epps and Epps (1976) who suggest that price changes follow a mixture of distributions, with transaction volume being the mixing variable. The SIF hypothesis proposed by Copeland (1976) and discussed further in Jennings et al. (1981), assumes that information is disseminated in the market sequentially and randomly. Therefore, informed traders who obtain the information first, take positions and adjust their portfolios accordingly, which results in shifts in supply and demand and a series of transitory equilibria. Once the information is fully absorbed by all traders, informed and uninformed, then equilibrium is restored. This sequential dissemination of information initiates transactions at different price levels during the day, the number of which increases with the rate of information flow to the market. Consequently, both trading volume and movement in prices increase as the rate of arrival of information to the market increases which implies the existence of a positive relationship between the two variables. It can be noted that both the MDH and the SIF attempt to justify the existence of a positive relationship between price changes and trading volume. However, they differ in that the MDH assumes that dissemination of information is symmetrical and all traders view changes in supply and demand simultaneously, which results in an immediate restoration

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of equilibrium, whereas in the SIF hypothesis, it is assumed that information is disseminated asymmetrically and equilibrium is restored gradually. Therefore, under the latter hypothesis, the trading volume affects subsequent price changes and volatility. An alternative theory, based on the information content of trading volume, is proposed by Blume et al. (1994). Based on the assumption that trading volume is a proxy for the quality and precision of information in the market and consequently contains information about price movements, they suggest that trading volume plays an important role in the price formation process. As a result, they propose that technical trading based on both the information in price movements and trading volume may produce superior results, which implies that there must be some form of inefficiency in the price determination process. Empirical studies by Crouch (1970), Cornell (1981), Grammatikos and Saunders (1986), Harris (1986), Chatrath et al. (1996), and Malliaris and Urrutia (1998) provide further evidence in support of this argument and report a positive contemporaneous relation between absolute returns and aggregate volume in different markets. Other studies investigate the relationship between trading volume and price volatility. For instance, Grammatikos and Saunders (1986) for futures markets and Harris (1986) for US equities, report the existence of a positive relationship between trading volume and volatility of returns. Other studies such as Lamoureux and Lastrapes (1990) on stocks, Najand and Yung (1991) on treasury bond futures, Bessembinder and Seguin (1993) on various financial and commodity futures markets, Foster (1995) on oil futures, and Chen et al. (2001) on stock indices, recognize the fact that many asset returns are characterized by time-varying distributions and hence utilize Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models [Engle (1982) and Bollerslev (1986)] to capture the time variability in the conditional second moments of price returns. In general, the evidence in the literature points to positive price–volume and volume–volatility relationships. Recent studies have also examined the price–volatility relationship in a dynamic framework using GARCH-type models, where trading volume is used as a proxy for the rate of information flow to the market. For instance, Lamoureux and Lastrapes (1990) examine the volume–volatility relationship for a number of stocks in the US. They use contemporaneous trading volume as an explanatory variable in the variance equation and find that the inclusion of volume eliminates the persistence in the volatility. However, they also suggest that adding contemporaneous volume into the variance equation might cause ‘simultaneity bias’ since volume is endogenous to the system. Therefore, they also use the lagged volume in variance equation which is found to be insignificant in most cases. Najand and Yung (1991) perform similar analysis using Treasury bond futures and find that lagged volume explains volatility better than contemporaneous trading volume. Foster (1995) investigates the temporal price–volatility relationship in the oil futures market considering the simultaneity problem. In fact, using a GARCH model he estimates time-varying variances and incorporates the volatility along with volume in a simultaneous equation model. His results indicate that not only lagged volume is positively related to volatility, but also there is a positive contemporaneous relationship between trading volume and price volatility. Finally, Chen et al. (2001) report that the persistence in volatility is not eliminated when lagged or contemporaneous trading volume level is incorporated in the GARCH model, a result contrasting the findings of Lamoureux and Lastrapes (1990). In other studies the focus has been on the causal interaction between volume and returns per se. For instance, Gallant et al. (1992) examine the causal relationship between S&P 500 stock index returns and trading volume in the New York Stock Exchange (NYSE) and find evidence for returns leading trading volume. Gervais et al. (2001) also report that periods of high trading volume tend to be followed by periods of positive excess returns whereas periods of low volume tend to be followed by negative excess returns using data from stocks traded at the NYSE; this suggests that there is a positive relationship between returns and trading volume and that volume precedes returns. Furthermore, Hiemstra and Jones (1994) study the causal dynamic relationship between Dow Jones Industrial Average index returns and aggregate trading volume using non-linear causality tests and report bilateral non-linear causality (feedback) between returns and trading volume. Similar conclusions are drawn by Silvapulle and Choi (1999) for the Korean Stock Exchange. Saatcioglu and Starks (1998) investigate emerging stock markets in Latin America. Consistent with the findings in the US, they report that volume leads returns in those markets, while Lee and Rui (2002) find little supportive evidence on the predictive power of trading volume for stock returns in four Chinese stock exchanges. Finally Chen et al. (2001) also report mixed results on the causality between index price changes and volume in nine of the world’s largest stock markets. Wang (1994) and Llorente et al. (2002) argue that volume and return dynamics depend on the motivation behind the trade. For instance, Wang (1994) discusses two different hypotheses, namely Liquidity Driven Trade (LDT) and information driven trade (IDT) hypotheses. Under the LDT hypothesis, a reversal in consecutive returns is likely if the trading by informed traders is driven by changes of investment opportunities outside the market. In this case, trading volume will contribute positively to the subsequent volatility. Under the IDT hypothesis, it is argued that the momentum in consecutive returns is a consequence of the informed investors’ trade due to better private information. This is because when a subset of informed investors sells (buys) because they have unfavorable (favorable) private information; the asset price decreases (increases), reflecting the negative (positive) private information about its payoff. Since this information is usually only partially incorporated into the price at the beginning, the negative (positive) return in the current period will be followed by another negative (positive) return in the next period. Thus this trading volume leads to lower subsequent volatility since these two period returns tend to be of the same sign, which means that high trading volume will be followed by a low volatility; that is, trading volume and subsequent volatility are negatively related. Llorente et al. (2002) also show that ‘‘hedging trades’’, which are liquidity-driven trades, generate negatively auto-correlated returns, while ‘‘speculative trades’’, which are information-driven trades, generate positively auto-correlated returns.

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Overall the results from these studies indicate the existence of a positive relationship between price volatility and trading volume in different financial markets. Additionally, there is evidence that a causal relationship exists between trading volume and price changes although the direction of causality seems to differ depending on the period and the market under investigation. However, these studies examined the information content of volume in financial markets. The unique characteristics of the shipping forward freight markets, especially the fact that the underlying asset is a non-storable sea transportation service makes investigation of the same issue in this market extremely interesting. Moreover, the results can have important implication for trading and risk management activities in this market which is believed to be a developing derivative market. 3. Methodology A number of different methodologies are employed in this paper to investigate the nature of the interaction between price change and trading volume, and volatility and trading volume in the dry bulk forward freight agreement market. The first step in the analysis is to investigate the causal relationship between trading volume and FFA price change in different markets using a Vector Autoregressive (VAR) model. Next, the relationship between trading volume and FFA price volatility is examined across the three different dry bulk sizes using an Exponential GARCH model proposed by Nelson (1991). Finally, the impact price change and volatility on FFA trading volume is examined using a VaR framework. The causal relationship between price changes and trading volume is investigated by estimating a Vector Autoregressive (VAR) model and application of Granger-causality tests. The VAR model is defined as

r t ¼ a1 þ

p p X X b1;j r tj þ k1;j DV tj þ e1;t j¼1

j¼1

ð1Þ

p p X X DV t ¼ a2 þ b2;j r tj þ k2;j DV tj þ e2;t j¼1

j¼1

where, rt and DVt are the weekly percentage change in FFA price and trading volume, respectively, while p is the order of the VAR model, which can be determined using Schwarz (1978) Bayesian Information Criterion (SBIC).5 The VAR model defined in Eq. (1) can be used to test causal relationship between price change and trading volume in the FFA market.6 In this respect, trading volume Granger causes price changes if the coefficients k1,j for j = 1, 2, . . . , p in the first equation are jointly significant, i.e. the null hypothesis of k1;j ¼ 0 for j = 1, 2, . . . , p is rejected. Similarly, price changes Granger cause trading volume if the null hypothesis of b2,j = 0 for j = 1, 2, . . . , p is rejected. The relation between price volatility and trading volume is investigated using the GARCH type models. These types of models have been used extensively in the literature to explore the price volatility and volume relationship in financial markets (see e.g. Lamoureux and Lastrapes (1990), Najand and Yung (1991), Foster (1995), and Chen et al. (2001), among others). In this study we use Nelson’s (1991) Exponential GARCH model, which allows for asymmetric impact shocks with different size and sign on the conditional volatility. The lagged change in trading volume is included as an exogenous parameter in the equation of the conditional variance, in what is termed an EGARCH-X model.

r t ¼ a0 þ

q X

ai rti þ et et  GEDð0; r2t ; mÞ

i¼1

ð2Þ

r2t ¼ expðb0 þ b1 g 1;t1 þ b2 g 2;t1 þ b3 ln r2t1 þ cDV t Þ pffiffiffiffiffi pffiffiffiffiffi pffiffiffiffiffi g 1;t ¼ ðet = rt Þ; g 2;t ¼ ½ðjet j= rt Þ  Eðjet j= rt Þ

pffiffiffiffiffi where r2t is the conditional variance and g 1;t1 and g 2;t1 are the standardized residuals, et = rt , and the difference between pffiffiffiffiffi pffiffiffiffiffi jet j= rt and the expected value of jet j= rt , respectively. In the above EGARCH-X framework, the coefficient of changes in trading volume in the conditional variance equation, c, measures the impact of trading activity on the conditional volatility of FFA price. It is also assumed that the disturbance terms follow a Generalized Error Distribution (GED) as in Nelson (1991), which allows for deviations from normality depending on the coefficient of degrees of freedom, m. Under the GED assumpP tion, the log of the likelihood function for t observations is represented by LðHÞ ¼ Tt¼1 IðHÞ where H represents the set of parameters of the average and conditional variance to be estimated with

IðHÞt ¼ ln

 

m

w

 0:5



m

et rt w

     1 1 lnð2Þ  ln C  lnðr2t Þ  1þ

m

m

5 We use changes in trading volume, DVt, as unexpected trading activity. The literature suggest decomposing trading activity into expected and unexpected components (Bessembinder et al., 1996) and only the unexpected component should be used to analyze the impact on price change and volatility. This is because information on expected component of trading activity should have already been incorporated in the asset price. 6 A stationary variable, xt, is said by Granger to cause another stationary variable, yt, if the present value of yt can be predicted more accurately by using past values of xt than by not doing so, considering also other relevant information including past values of yt. If both xt and yt Granger-cause each other then there is a two-way feedback relationship between the two variables.

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where w ¼ expðð1=mÞ lnð2Þ þ 0:5 lnðð1=mÞ  0:5 lnðð3=mÞÞÞ and C() is the Gamma function. The estimate of the coefficient of degrees of freedom, m, indicates the excess kurtosis of the distribution of error terms; that is, when m is equal to 2, et is normally distributed, and when m less than 2 the distribution shows excess kurtosis and fat-tails. The EGARCH-X specification is preferred in this study for a number of reasons. First, in the EGARCH model, the innovations are allowed to have an asymmetric impact on future volatility depending on their sign and magnitude, as reflected by the coefficients of g1,t1 and g2,t1 terms. For instance, if the coefficient of g1,t1, b1, is negative, then negative shocks will increase the ex-ante variance proportionately more than positive shocks, and vice versa. Similarly, if the coefficient of g2,t1, b2, is positive then large shocks will have a relatively larger impact on the ex-ante variance compared to smaller shocks. This feature of EGARCH models is particularly important in this case since preliminary evidence of the data indicates that the response of volatility to shocks is asymmetric and is affected by the magnitude and sign of the shock, thus providing evidence that an EGARCH specification is appropriate. Second, the EGARCH specification also relaxes the non-negativity restrictions on the conditional variance parameters, required by standard GARCH models for the variance to be positive at all times. Estimation of parameter values is achieved by maximizing the conditional GED log-likelihood function. Once the correct EGARCH-X model is estimated, the estimated volatility of FFA prices can be extracted and used as an endogenous variable along with trading volume in a modified VAR(1) model to examine the relationship between the two variables in the following form

r2t ¼ a1 þ

p p X X b1;j r2tj þ k1;j DV tj þ e1;t j¼1

DV t ¼ a2 þ

j¼1

p p p X X X b2;j r2tj þ k2;j DV tj þ cj rtj þ e2;t j¼1

j¼1

ð3Þ

j¼i

In addition to the lagged endogenous variables the VAR model includes lagged FFA price changes series as an explanatory variable in the equation for changes in trading volume. This setting allows us to test whether lagged volatility and price change determine the trading activity in the market. Again the VAR model can be estimated using the Generalized Method of Moments (GMM) approach proposed by Hansen (1982). The GMM has the advantage of yielding heteroskedasticity and autocorrelation consistent estimates (as proposed by Newey and West, 1987) in the process. 4. Description and properties of the data Although FFAs can be traded for every shipping route for which the assessment is published by the Baltic Exchange, there seems to be a tendency for trades to concentrate on certain routes; namely, the equally weighted average of the 4 trip-charter routes of the Capesize market known as the Baltic Capesize Index (BCI) 4TC average, which is calculated as the equally weighted average of routes C8_03 to C11_03 of the BCI, presented in Table 1; the equally weighted average of the Baltic Panamax 4 trip-charter routes known as the Baltic Panamax Index (BPI) 4TC average; and, the weighted average of 6 Trip-charter routes of the Baltic Supramax Index (BSI), with the weights presented in Table 1. Market participants use basket routes, such as the average of the 4 TC routes of BCI and BPI, in order to hedge their average monthly earnings; hence calculating the settlement rate as the monthly average is preferred, since it provides a better fit to the requirements of the traders in the physical market and more closely matches the monthly earnings of the vessel. Since July 2007, information on the volume of FFA trade has also been reported by the Baltic Exchange. The information on FFA trades is provided to the Baltic Exchange on a weekly basis by international FFA brokers, Clearing Houses and Exchanges, is aggregated and reported to the market.7 The dataset in this study consists of weekly FFA prices and total trading volume for three different types of dry bulk carriers, namely Capesize, Panamax and Supramax, over the period August 2007– August 2011. The data is from the Baltic Exchange. Weekly observation for 1st, 2nd, and 3rd nearest quarter average trip-charter FFA contracts for Capesize, Panama and Supramax vessels are chosen because these are considered the most liquid contracts in the dry FFA market. All FFA prices are quoted in US dollars per day and represent the average of 4 trip-charter routes for Capesize and Panamax, and average 6 trip-charter routes for Supramax vessels. The trading volume series represent the total – cleared and OTC – trading activities for all maturity contracts; i.e. they are not specific trading activities for 1st, 2nd, and 3rd nearest quarter FFAs. This is because trading activities for individual routes or contract maturities are not yet reported by market sources. However, since most of the liquidity is concentrated on average trip-charter contracts, especially for the three nearest quarterly contracts, the analysis is performed on the assumption that trading volume mainly reflects market activity in these quarterly FFA contacts. In the dry sector, FFA trading volume, defined as lots traded, is estimated for the Capesize, Panamax and Supramax markets. The trades are also classified as to whether they are cleared or not. In the dry FFA market a lot is defined as either one trip-charter day or 1000 tonnes of cargo on voyage charter contracts. In each case a single transaction, although having a buyer and a seller, is counted only as one in estimation of trading volume. For instance, a single Panamax average 4TC 7 Weekly reported FFA trading volumes are based on figures collected on Fridays at the end of the business day, and published every Monday, and when this conflicts with a UK public holiday, publishing takes place on the next working day.

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Table 1 Definitions of routes used to report the Baltic average trip charter rates for different size dry bulk carriers. Source: The Baltic Exchange. Route number

Route description

Capesize 172,000 mt dwt C8_03 Delivery Gibraltar–Hamburg for a Trans-Atlantic round voyage, redelivery Gibraltar–Hamburg range. Duration 30–45 days C9_03 Delivery ARA–Mediterranean for a trip to the Far East, redelivery China–Japan range. Duration 65 days C10_03 Delivery China–Japan for a Pacific round voyage, redelivery China–Japan range. Duration 30–40 days C11_03 Delivery China–Japan for a trip to ARA or the Mediterranean. Duration 65 days Panamax 74,000 mt dwt P1A_03 Delivery Skaw–Gibraltar range for a Trans-Atlantic round voyage (including ECSA), redelivery Skaw–Gibraltar range. Duuration 45–60 days P2A_03 Delivery Skaw–Gibraltar for a trip to the Far East, redelivery Taiwan–Japan range. Duration 60–65 days P3A_03 Delivery Japan–South Korea for a trans-Pacific round voyage, either via Australia of NOPAC, redelivery Japan–South Korea range. Duration 35–50 days P4_03 Delivery Japan–South Korea for a trip to the Continent (via US West Coast–British Columbia range), redelivery Skaw–Gibraltar range. Duration 50–60 days Supramax 52,000 mt dwt S1A Delivery Antwerp–Skaw for a trip to Far East, redelivery Singapore–Japan range including China. Duration 60–65 days S1B Delivery Canakkale for a trip to the Far East, redelivery Singapore–Japan range including China. Duration 50–55 days S2 Delivery Japan–S. Korea for a Pacific round voyage, redelivery Japan–S. Korea. Duration 35–40 days S3 Delivery Japan–South Korea for a trip to the Continent, redelivery Gibraltar–Skaw range. Duration 60–65 days S4A Delivery US Gulf for a trip to the Continent, redelivery Skaw–Passero. Duration 30 days S4B Delivery Skaw–Passero for a trip to US Gulf. Duration 30 days

Weighting (%) 25 25 25 25 25 25 25 25

12.5 12.5 25 25 12.5 12.5

This table presents the definitions of selected the Baltic routes for calculation of average trip-charter contracts as of January 2008. See the Baltic Exchange for detailed definitions. TC stands for a trip-charter route; dwt stands for dead-weight tons; ARA stands for Amsterdam–Rotterdam–Antwerp range. ECSA stands for East Coast South America; NOPAC stands for North Pacific.

FFA contract for 1st quarter of 2010 is considered as 90 lots corresponding to the number of days of hire in the first quarter of the year; whereas a single Jan 2010 Capesize route C4 (150,000 mt of coal from Richards Bay to Rotterdam) FFA contract is considered as 150 lots.8 Fig. 2 presents the number of lots traded in the dry FFA market over the period June 2007–August 2011 on a quarterly basis from the data provided by the Baltic Exchange. It can be seen that following the fall in the market in 2008, the trading volume in FFA contracts also declined in all dry bulk sectors, with the most notable drop in Panamax FFA trading. In addition, following the credit market squeeze in mid 2008, the FFA market went through a transformation where participants switched from trading OTC contracts to cleared contracts. This was mainly the result of increased sensitivity to credit risk exposure of the OTC FFA contracts which participants tried to avoid using clearing facilities offered by clearing houses such as London Clearing House (LHC.Clearnet), Singapore Exchange (SGX) and Imarex NOS (Nowegian Option Clearing House). Figs. 3–5 illustrate the change in the pattern of trading FFA contracts from mainly OTC prior to 2008 to cleared contracts after 2008, for Capesize, Panamax and Supramax markets, respectively. Summary statistics for weekly price changes of FFAs for 1st, 2nd and 3rd quarter along with trading volume and changes in trading volume for different size dry bulk carriers are presented in Table 2.9 It can be seen that mean return on FFA prices, as measured by the average of weekly changes in log FFA prices, are all negative and seem to be larger, in absolute terms, for larger vessels (0.010 and 0.009 for Capesize, 0.007 and 0.006 for Panamax, and 0.006 and 0.005 for Supramax vessels). This is mainly because of the decline in FFA rates across all shipping sectors over the sample period as well as greater sensitivity of freight rates for larger ships to market decline compared to smaller ships. There also seems to be a positive relationship between standard deviation of returns and vessel size; that is, FFA prices for larger vessels show higher volatility than smaller ones, a fact that is in line with the literature on freight market volatility (e.g. Kavussanos, 1997, and Kavussanos and Alizadeh, 2002). In addition, for each vessel type the volatility of FFA prices declines as the maturity of the contract increases; a characteristic of the forward freight market known as ‘‘volatility term structure’’. For instance, the estimated weekly volatility of 1st, 2nd and 3rd quarter FFA for Capesize vessels are 0.154, 0.125 and 0.109, respectively. Furthermore, the coefficient of skewness indicates that all the FFA return series are negatively skewed, with the exception of 1st quarter Capesize FFAs, while the coefficients of kurtosis reveal that all FFA return series show excess kurtosis. As a consequence, Jarque and Bera (1980) tests indicate significant departures from normality for all the FFA return series, substantiating the use of GED in EGARCH-X models. The Ljung and Box (1978) Q-statistic on the first 8 lags of the sample autocorrelation function is significant at the 1% level in all FFA return series indicating that serial correlation is present in the return series. Turning next to the descriptive statistics of weekly trading volume and changes in trading volume, also presented in Table 2, it is observed that the average weekly trading over the sample period is 11173.49, 14126.97, and 3407.87 lots 8 Defining traded volume in terms of lots may underestimate the significance of trades in the larger size vessels since, for instance, 1 day of trip-charter hire in the Capesize sector reflects twice as much volume of cargo transported than for instance one Panamax FFA lot. 9 Weekly price (trading volume) changes are constructed as the difference in the logarithms of FFA prices (trading volume) between successive weeks. In the case of no trading activities in one week, for instance end of the year, the price of the following week is considered.

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Fig. 2. Quarterly trading volume in dry FFA by vessel size. Source: The Baltic Exchange.

Fig. 3. Weekly cleared and OTC trading volume of Capesize FFA contracts.

Fig. 4. Weekly cleared and OTC trading volume of Panamax FFA contracts.

for Capesize, Panamax, and Supramax, respectively. Comparing the mean trading volume across the sub-sectors indicates a higher average trading activity in the Panamax sector than the Capesize and Supramax sectors. This is because of more liquidity in the Panamax FFA market and the fact that Panamax fleet is larger than the other two sectors. Furthermore, the frequency distribution of trading volume exhibit both positive skewness and significant kurtosis, while ARCH test and Ljung and Box (1978) Q-statistic indicate significant ARCH and autocorrelation in trading volume series. The average change in trading volume of FFA contracts seems to be close to zero for Capesize market and negative for Panamax and Supramax sectors. The estimated weekly standard deviation of changes in trading volume is 0.444, 0.361 and 0.542 for Capesize, Panamax and Supramax contracts which reveals an inverse relation to the average trading volume. Moreover, changes in

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Fig. 5. Weekly cleared and OTC trading volume of Supramax FFA contracts.

Table 2 Descriptive statistics of return on FFA prices and trading volume for different size dry bulk carriers. Mean

SD

Skewness

Kurtosis

Auto-correlation

ARCH

JB

Normity p-Val.

LBQ(8)

p-Val.

LBQ2(8)

p-Val.

PP stat

PP test p-Val.

Capesize DQ1 DQ2 DQ3 Volume DVolume

0.010 0.010 0.009 11173.490 0.000

0.156 0.125 0.109 5370.571 0.444

0.214 0.259 0.661 1.428 0.519

6.761 7.205 7.715 5.467 4.159

124.786 156.283 208.791 124.017 21.100

0.000 0.000 0.000 0.000 0.000

41.681 43.213 38.874 131.300 23.355

0.000 0.000 0.000 0.000 0.000

94.120 74.021 53.532 104.810 9.649

0.000 0.000 0.000 0.000 0.291

16.706 16.989 17.158 8.489 81.837

0.000 0.000 0.000 0.000 0.000

Panamax DQ1 DQ2 DQ3 Volume DVolume

0.007 0.007 0.006 14126.970 0.005

0.121 0.102 0.089 9870.804 0.361

0.409 0.810 1.136 1.697 0.266

6.989 6.909 8.098 5.621 3.817

144.408 155.932 271.298 160.090 8.275

0.000 0.000 0.000 0.000 0.016

36.503 42.033 18.909 538.94 20.871

0.000 0.000 0.015 0.000 0.007

61.074 34.195 29.997 283.95 5.104

0.000 0.000 0.000 0.000 0.746

17.075 17.754 16.706 4.770 33.112

0.000 0.000 0.000 0.000 0.000

Supramax DQ1 DQ2 DQ3 Volume DVolume

0.006 0.005 0.005 3407.866 0.002

0.096 0.081 0.075 2280.372 0.542

0.651 1.343 1.790 1.759 0.214

7.408 8.808 11.279 6.381 3.381

184.007 356.583 708.440 207.288 2.864

0.000 0.000 0.000 0.000 0.239

28.548 23.490 12.948 362.340 42.979

0.000 0.000 0.114 0.000 0.000

45.303 42.342 45.661 229.92 13.628

0.000 0.000 0.000 0.000 0.092

15.728 15.946 15.306 7.437 47.080

0.000 0.000 0.000 0.000 0.000

Sample: weekly observation from 9 July 2007 to 21 August 2011. JB is the Jarque and Bera (1980) test for Normality which follows a chi-squared distribution with 2 degrees of freedom. LBQ(8) is the Ljung and Box (1978) statistics for 8 order Autocorrelation in the series which follows a chi-squared distribution with 8 degrees of freedom. LBQ2(8) is the Ljung and Box (1978) statistics for 8 order Autocorrelation in the squared values of the series which is used as an ARCH test and follows a chisquared distribution with 8 degrees of freedom. PP is Phillips and Perron (1988) test for unit root.

Capesize and Panamax FFA trading volumes show deviations from normal distribution according to the estimated coefficient of skewness and kurtosis, and Jarque and Bera (1980) test; whereas changes in Supramax FFA trading activity seem to be normally distributed according to the Jarque and Bera (1980) test. Finally, the Ljung and Box (1978) test for autocorrelation indicate that changes in FFA trading activities across all vessel types show significant autocorrelation, but no ARCH effects according to ARCH test results. Finally, Phillips and Perron (1988) unit root test for all FFA price returns with different maturities, trading volume and changes in trading volume for different vessel sizes are also presented in Table 1. These results indicate that FFA price returns, trading volume and changes in trading volume are all stationary series across different vessel sizes and FFA maturities. 5. Estimation results Having discussed the stochastic properties of forward freight agreements and trading volume, the next step is to examine the relationship between FFA price changes and trading volume using Eq. (1). The results are presented in Tables 3–5 for Capesize, Panamax and Supramax sectors, respectively. The lag length for VAR models is selected using Schwarz (1978) Bayesian Information Criterion (SBIC) and models are estimated using Hansen (1982) GMM estimation method with heteroscedasticity and autocorrelation robust standard errors. Starting with the results of the Capesize market, Table 3, estimated

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A.H. Alizadeh / Transportation Research Part E 49 (2013) 250–265 Table 3 Estimation results of VAR models and Granger-causality tests for Capesize FFA returns and trading volume. Pp j¼1 b1;j r tj þ j¼1 k1;j DV tj þ e1;t Pp P DV t ¼ a2 þ j¼1 b2;j rtj þ pj¼1 k2;j DV tj þ e2;t r t ¼ a1 þ

Pp

1st Quarter FFA

ai i = 1,2 bi,1, i = 1, 2 ki,1, i = 1, 2

2nd Quarter FFA

3rd Quarter FFA

rt

DV t

rt

DVt

rt

DVt

0.012 (0.913) 0.150 (1.524) 0.010 (0.429)

0.002 (0.111) 0.390** (2.509) 0.281*** (3.505)

0.011 (1.157) 0.172 (1.621) 0.009 (0.458)

0.002 (0.118) 0.427** (2.182) 0.280*** (3.537)

0.009 (1.338) 0.142 (1.280) 0.005 (0.234) 0.242** (1.727) 0.016 (0.787)

0.003 (0.155) 0.455* (1.841) 0.348*** (3.635) 0.240 (0.777) 0.215*** (4.038)

0.014

0.089

0.021

0.085

0.077

0.117

bi,2, i = 1, 2 ki,2, i = 1, 2 R2 LogL SBIC WH test Q(8) test Granger causality rt Causes DVt

DVt Causes rt

24.709 0.0836 59.397 [0.000] 50.923 [0.000]

21.340 0.3592 52.618 [0.000] 63.334 [0.000] 4.294 [0.038] 0.172 [0.678]

63.716 0.3580 141.50 [0.000] 42.472 [0.011] 3.300 [0.069]

0.210 [0.647]

3.170 [0.205] 0.896 [0.639]

The lag length, p, is chosen on the basis of the SBIC, Schwarz (1978). Models are estimated by non-linear GMM of Hansen (1982), where standard errors are corrected for heteroscedasticity and autocorrelation using the Newey–West (1987) method. Granger causality tests are Wald statistics distributed as v2 (r), where r is the number of the restricted parameters. This is equal to the number of lags, p, included in the model Granger (1969). LogL and SBIC are the system log-likelihood and Schwartz Bayesian Information Criterion for lag length selection, respectively. Figures in () and () are t-statistics and p-values, respectively. Q(8) test is the Portmanteau test for autocorrelation in VAR residual with lag order of 8. The test follows a chi-squared distribution with 8 degrees of freedom. WH test is White (1980) test for heteroskedasticity with cross terms. * Significance at the 10% levels. ** Significance at the 5% levels. *** Significance at the 1% levels.

coefficients of lagged FFA returns and changes in volume are not significant in the FFA equation for all maturities, with the exception of 2 period lagged returns in model for 3rd quarter FFA, b1,2 which suggest that changes FFA prices are random and in line with the Efficient Market Hypothesis. Positive and significant coefficients of lagged FFA price change, b2,1, in volume equations indicate that FFA price changes positively affect trading volume. Moreover, Granger-causality tests reveal that while lagged FFA price changes, rtj, can explain changes in trading volume for 1st and 2nd quarter FFAs, lagged changes in trading volume, DVtj, does not have any explanatory power over FFA price movements. This can be considered as a confirmation that FFA markets are informationaly efficient in the sense that publicly available information such as price and volume cannot be used to predict FFA price movements. Also, higher reported values of adjusted R-squared, R2 , for volume equations compared to those of FFA return equations suggest higher predictability of trading volume in comparison to FFA price changes. The results of estimated VAR models for 1st, 2nd and 3rd quarter FFA prices changes and trading volume for Panamax sector are reported in Table 4. Once again, it can be seen that while coefficients of lagged FFA price changes are positive and significant in all volume equations, coefficient of lagged changes in volume are not significant in any of the FFA return equations. This is also confirmed by Granger-causality test, which indicate that there is a unidirectional causality in the sense that FFA price changes can explain trading activity. The fact that FFA price changes can explain changes in trading volume suggests that there might be a momentum effect in FFA markets where price increases tend to increase trading activities, while price fall tend to lower trading activities. The results of the estimated VAR models for 1st, 2nd and 3rd quarter FFA prices changes and trading volume for Supramax sector, reported in Table 5, reveals somehow different outcome. In particular, this time lagged changes in FFA prices are not significant in any of the trading volume equations. At the same time, significant and positive coefficients of one period lagged trading volume in FFA return equations suggest that changes trading volume can explain FFA price changes in this market. Granger-causality tests, although only significant at the 10% level, also confirms that information on changes in trading volume can be used to determine the direction of FFA price movements. This is in contrast to what was observed in the

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Table 4 Estimation results of VAR models and Granger-causality tests for Panamax FFA returns and trading volume. Pp j¼1 b1;j r tj þ j¼1 k1;j DV tj þ e1;t Pp P DV t ¼ a2 þ j¼1 b2;j rtj þ pj¼1 k2;j DV tj þ e2;t r t ¼ a1 þ

Pp

1st Quarter FFA

ai, i = 1, 2 bi,1, i = 1, 2 ki,1, i = 1, 2 R2 LogL SBIC WH test Q(8) test

3rd Quarter FFA

DVt

rt

DV t

rt

DVt

0.008 (0.870) 0.185* (1.737) 0.020 (1.035) 0.029

0.003 (0.151) 0.706*** (4.493) 0.248*** (3.367) 0.105

0.008 (1.070) 0.235** (2.214) 0.009 (0.604) 0.047

0.003 (0.176) 0.658*** (3.082) 0.242*** (3.361) 0.083

0.007 (1.142) 0.162* (1.712) 0.012 (0.834) 0.019

0.003 (0.137) 0.798*** (2.874) 0.240*** (3.309) 0.088

79.212 0.6077 23.207 [0.080] 46.237 [0.016]

Granger causality rt Causes DVt

DVt Causes rt

2nd Quarter FFA

rt

114.15 0.9436 15.001 [0.451] 45.596 [0.019] 12.960 [0.000]

0.786 [0.375]

139.48 1.1872 15.506 [0.416] 35.288 [0.161] 7.827 [0.005]

0.240 [0.625]

8.835 [0.003] 0.540 [0.462]

The lag length, p, is chosen on the basis of the SBIC, Schwarz (1978). Models are estimated by non-linear GMM of Hansen (1982), where standard errors are corrected for heteroscedasticity and autocorrelation using the Newey–West (1987) method. Granger causality tests are Wald statistics distributed as v2 (r), where r is the number of the restricted parameters. This is equal to the number of lags, p, included in the model. LogL and SBIC are the system log-likelihood and Schwartz Bayesian Information Criterion for lag length selection, respectively. Figures in () and (] are t-statistics and p-values, respectively. Q(8) test is the Portmanteau test for autocorrelation in VAR residual with lag order of 8. The test follows a chi-squared distribution with 28 degrees of freedom. WH test is White (1980) test for heteroskedasticity with cross terms. * Significance at the 10% levels. ** Significance at the 5% levels. *** Significance at the 1% levels.

Capesize and Panamax sectors. One possible explanation for such a difference in FFA price change and trading volume relationship in the Supramax sector compared to the other two markets could be relatively low trading activity in the Supramax market in comparison to the Capesize and Panamax markets. In fact, trading volume in Supramax FFAs has been almost a quarter of that in Capesize and Panamax markets. Turning to examination of the relationship between FFA price volatility and trading volume, the results of estimation of EGARCH(1,1)-X models of Eq. (2) are reported in Table 6.10 The lag length for each model is chosen to eliminate any autocorrelation and minimize the Schawrtz Bayesian Information Criterion (SBIC). The estimated coefficients of lagged dependent variable (AR terms) in the mean equation are all significant at the 10% level. Diagnostic tests indicate that these models are well specified. For instance, Ljung and Box (1978) test for 8 order autocorrelation, and Engle’s (1982) ARCH(8) test for 8 order ARCH effects in standardized residuals indicate that all the residuals are free from any autocorrelation and ARCH effects, confirming the appropriateness of the EGARCH-X specification in modeling FFA price volatilities. Several observations merit attention. First, the estimated coefficients of GED distribution parameters are below 2 and significant indicating excess kurtosis in distributions of error terms of all models. The estimated coefficients of b2, measuring the asymmetric impact of shocks with different signs on price volatility are negative but significant only in 6 out of 9 models; namely 1st, 2nd and 3rd quarter Capesize FFAs, 1st quarter Panamax FFA, and 1st and 2nd quarter Supramax FFAs. This implies that negative shocks – unexpected changes in FFA prices – have relatively greater impact on the conditional variance of FFA prices than positive shocks Estimated coefficients of b1, measuring the asymmetric impact of shocks with different size, are positive and significant across all models, with the exception of 2nd quarter Capesize FFA. The positive sign of estimated b2 coefficients suggest that larger than average shocks tend to have a proportionally greater impact on volatility compare to smaller than average shocks. These asymmetric impact of shocks with different sign and size on volatility are consistent with findings reported in the financial markets literature (see for example Cheung and Ng (1992) and Koutmos (1998)). Moreover, estimated coefficients of lagged variance b3, range between 0.931 and 0.992 indicating a high level persistence in volatility of 10 In order to test the presence of any structural change in the relationship between variables due to the 2008 credit crunch and market crash, a binary dummy variable (taking value of 0 before and 1 after August 2008) was used in all models. The results, which are not reported here, indicate that the estimated coefficients of the dummy variable are not significant in both VAR and EGARCH-X volatility models.

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Pp

1st Quarter FFA

ai, i = 1, 2 bi,1, i = 1, 2 ki,1, i = 1, 2 bi,2, i = 1, 2 ki,2, i = 1, 2 R2 LogL SBIC WH test Q(8) test

3rd Quarter FFA

DV t

rt

DVt

rt

DV t

0.005 (0.818) 0.069 (0.708) 0.032** (2.241) 0.194 (1.387) 0.017 (1.282) 0.055

0.002 (0.067) 0.483 (1.160) 0.495*** (7.594) 0.102 (0.343) 0.245** (3.454) 0.194

0.005 (1.036) 0.090 (0.938) 0.025* (1.859) 0.190 (1.255) 0.017 (1.417) 0.054

0.001 (0.050) 0.479 (1.064) 0.493*** (7.416) 0.211 (0.648) 0.241*** (3.378) 0.193

0.006 (0.811) 0.066 (0.657) 0.027*** (2.472)

0.005 (0.217) 0.253 (0.601) 0.403*** (7.285)

0.019

0.159

60.647 0.3283 92.096 [0.000] 46.056 [0.004]

Granger causality rt Causes DVt

DVt Causes rt

2nd Quarter FFA

rt

94.837 0.6587 78.955 [0.001] 40.830 [0.017] 1.886 [0.390]

5.983 [0.050]

98.866 0.7967 25.528 [0.043] 55.446 [0.002] 1.472 [0.479]

5.484 [0.064]

0.050 [0.824] 2.828 [0.093]

The lag length, p, is chosen on the basis of the SBIC, Schwarz (1978). Models are estimated by non-linear GMM of Hansen (1982), where standard errors are corrected for heteroscedasticity and autocorrelation using the Newey–West (1987) method. Granger causality tests are Wald statistics distributed as v2 (r), where r is the number of the restricted parameters. This is equal to the number of lags, p, included in the model. LogL and SBIC are the system log-likelihood and Schwartz Bayesian Information Criterion for lag length selection, respectively. Figures in () and (] are t-statistics and p-values, respectively. Q(8) test is the Portmanteau test for autocorrelation in VAR residual with lag order of 8. The test follows a chi-squared distribution with 28 degrees of freedom. WH test is White (1980) test for heteroskedasticity with cross terms. * Significance at the 10% levels. ** Significance at the 5% levels. *** Significance at the 1% levels.

FFA prices. The adjusted R squared values, ranging from 0.029 for 3rd quarter Supramax FFA to 0.085 for 1st quarter Capesize FFA, seem to be higher for models for Capesize FFAs compared to Panamax and Supramax FFAs. More importantly, the estimated coefficients of changes in trading volume in the variance equation, c, are all positive and significant indicating that as trading activity in FFA market increases there is a proportionate increase in the volatility of FFA prices.11 This result is in line to what is reported in the literature on the relationship between volume and volatility in financial markets; for instance, studies such as Grammatikos and Saunders (1986), Najand and Yung (1991), Foster (1995), and Kalotychou and Staikouras (2006) rport a positive relationship between volume and volatility. In the next step, Eq. (3) is estimated and the dynamic relationship between price volatility and trading volume is examined. To this extent, the FFA price volatilities extracted from Eq. (2) and used as endogenous variables along with trading volume in a bivariate VAR setting. The models are estimated using Hansen’s (1982) GMM method and standard errors are corrected for presence of heteroscedasticty and autocorrelation using Newey and West (1987) method. The equation for trading volume also includes lagged changes in FFA prices as an explanatory variable. The results are reported in Table 7. The estimated coefficients of lagged FFA price change, c1, in trading volume equation are positive and significant across all vessel sizes and contract maturities, with the exception of 2nd and 3rd quarter Supramax. This implies that positive price changes tend to increase trading activity while price drops tend to decrease trading activity. Thus it can be argued that there might be a momentum effect that derives the trading activity in the FFA market as positive gains tend to encourage trading and losses tend to lower trading activity. Estimated coefficients of lagged variance in the trading volume equations, b2,1, are all negative and statistically significant across all vessel sizes and contract maturities, which suggests that as market volatility increase, trading volume tend to 11 The EGARCH-X models were also estimated with lagged changes in trading volume as an explanatory variable in the variance equation. However, all estimated coefficients for changes in trading volume lagged found to be insignificant and rejecting SIF hypothesis. The results, not reported here, are available from the author.

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Table 6 Estimation results of time-varying volatility models with trading volume as explanatory variable. Pq

a r þ et et  GEDð0; r2t ; mÞ i¼1 i ti r ¼ expðb0 þ b1 g1;t1 þ b2 g 2;t1 þ b3 ln r2t1 þ cDV t Þ pffiffiffiffiffi pffiffiffiffiffi pffiffiffiffiffi g 1;t ¼ ðet = rt Þ; g 2;t ¼ ½ðjet j= rt Þ  Eðjet j= rt Þ r t ¼ a0 þ 2 t

Mean equation

CSZ Q1

a0

0.016* (1.749) 0.126 (1.570) 0.163** (2.284)

a1 a2 a3 a4 Variance equation b0 b1 b2 b3

c m

CSZ Q2 0.022*** (3.451) 0.189*** (2.576) 0.167** (2.306)

0.163*** (2.640)

0.205*** (3.350)

0.318* (1.905) 0.275** (2.498) 0.120* (1.862) 0.975*** (38.252) 0.944*** (4.284) 1.877*** (5.298)

0.146 (1.355) 0.146 (1.360) 0.215*** (4.163) 0.992*** (117.66) 0.864*** (4.709) 2.229*** (4.478)

CSZ Q3

PMX Q1

PMX Q2

PMX Q3

SMX Q1

SMX Q2

SMX Q3

0.019*** (4.195) 0.191*** (3.053) 0.155** (2.203)

0.005 (0.927) 0.157** (1.960) 0.125* (1.708)

0.005 (1.180) 0.246*** (3.311) 0.127* (1.818)

0.003 (0.996) 0.224*** (3.142)

0.005 (0.956) 0.192*** (2.567)

0.005 (1.287) 0.242*** (3.109)

0.003 (0.930) 0.212*** (2.788)

0.122* (1.919)

0.115* (1.729)

0.470* (1.893) 0.253** (2.181) 0.111* (1.713) 0.946*** (24.972) 0.767*** (3.523) 1.589*** (6.240)

0.576** (2.125) 0.358** (2.482) 0.107* (1.649) 0.947*** (23.697) 0.888*** (4.091) 1.846*** (5.989)

0.127** (2.354) 0.162*** (2.860) 0.148*** (2.266) 0.247*** (4.177) 0.987*** (86.525) 1.094*** (4.584) 1.970*** (6.146)

0.386* (1.950) 0.294** (2.352) 0.128** (2.307) 0.968*** (32.039) 0.956*** (4.893) 1.856* (5.762)

0.503* (1.950) 0.328*** (2.583) 0.097 (1.426) 0.950*** (23.594) 0.677*** (3.168) 1.413*** (6.421)

0.551* (1.895) 0.326** (2.539) 0.082 (1.219) 0.942*** (20.883) 0.535** (2.222) 1.257*** (7.767)

0.751* (1.952) 0.464** (2.481) 0.049 (0.551) 0.931*** (18.888) 0.722*** (2.906) 1.465*** (6.886)

Diagnostics R2 LL SBIC LB Q(8) ARCH(8)

0.085 150.160 1.205 7.143 [0.210] 11.490 [0.042]

0.078 204.199 1.733 6.467 [0.263] 8.955 [0.111]

0.093 234.966 2.033 4.756 [0.446] 7.581 [0.181]

0.017 192.559 1.629 2.713 [0.844] 11.190 [0.083]

0.072 225.770 1.949 5.471 [0.485] 2.164 [0.904]

0.013 254.094 2.238 4.251 [0.751] 1.900 [0.965]

0.027 226.057 1.972 5.909 [0.433] 1.132 [0.980]

0.014 268.005 2.381 5.339 [0.502] 1.347 [0.969]

0.029 296.243 2.643 5.616 [0.585] 3.017 [0.883]

Figures in (.) and [.] are t-statistics and exact probability values, respectively. Significance at the 10% levels. Significance at the 5% levels. *** Significance at the 1% levels. *

**

decrease, and vice versa. On the first instance this may appear surprising as higher market volatility should encourage more hedging activities by participants; however, because the FFA market is a developing derivatives market where it is believed that hedgers are not as active as speculators, and informed trading exceeds noise trading, it could be that higher volatility levels tend to have a negative impact on trading activity. Finally, estimated coefficients of lagged trading volume in the variance equation are all negative but only significant in the Supramax market. The negative sign of the coefficient of lagged changes in volume is consistent with the literature (see for example Wang, 2004Wang, 2004; Yang et al., 2005), in that lagged changes in trading volume tend to negatively affect the volatility while contemporaneous changes in trading volume tend to positively affect the price volatility. Overall, the results point to three main findings. First, there is evidence of a unidirectional casual effect from price change to trading volume in the FFA market, suggesting that there might be a momentum effect in FFA trading activity as price increases tend to lead to more transactions and trading activities. Second, there is a positive contemporaneous relationship between volatility and trading activity, which is in line with the MDH of Clark (1973), signifying that both price changes and trading volume simultaneously respond to the flow of new information. This also suggests that information dissemination in the FFA market is efficient and participants incorporate information on market movements in their trading strategies. Finally, across all forward freight markets FFA price volatility seem to negatively affect trading activities. This could be explained partly by the move towards clearing FFA contracts after the market crash and credit crunch in 2008, which requires high margins and higher cost of transactions which consequently reduced FFA trading volume. In terms of implication of the findings, the results suggest that there has been a significant decline in FFA trading in this market after the 2008 credit crises, with a clear shift to clear trades. Also, the results of the analysis reveal that FFA trading volume declines as market volatility increases, which suggests that the cost of hedging can increase due to drop in liquidity.12 In addition, the results seem to provide evidence in relation to the type of trading activities in the FFA market according to 12

Batchelor et al. (2007) show that increase in volatility lowers liquidity and widens the bid-ask spread, which in turn makes hedging more expensive.

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A.H. Alizadeh / Transportation Research Part E 49 (2013) 250–265 Table 7 Estimation results of the VAR model for FFA volatility and trading volume. P

P

r2t ¼ a1 þ pj¼1 b1;j r2tj þ pj¼1 k1;j DV tj þ e1;t P P P DV t ¼ a2 þ pj¼1 b2;j r2tj þ pj¼1 k2;j DV tj þ pj¼1 cj rtj þ e2;t 1st quarter

Capesize ai, i = 1, 2 bi,1, i = 1, 2 ki,1, i = 1, 2

2nd quarter

DV t

r2t

DVt

r2t

DV t

0.021*** (3.924) 0.838*** (19.665) 0.011 (1.425)

0.207*** (4.019) 1.577*** (4.428) 0.234*** (2.649) 0.449*** (5.291) 0.145

0.010*** (2.772) 0.879*** (24.127) 0.010 (1.626)

0.128** (2.697) 1.304*** (3.164) 0.265*** (3.122) 0.760*** (4.552) 0.098

0.011*** (2.773) 0.847*** (17.747) 0.009 (1.202)

0.125*** (2.974) 1.448*** (3.544) 0.256*** (2.994) 0.806*** (4.043) 0.099

0.069* (1.651) 0.641* (1.775) 0.268*** (3.575) 0.792*** (5.359) 0.111

0.010*** (3.159) 0.873*** (22.073) 0.006 (1.496)

0.147*** (2.798) 1.696*** (2.735) 0.225*** (3.009) 0.916*** (6.235) 0.104

0.009*** (2.673) 0.875*** (19.309) 0.003 (0.884)

0.289*** (4.293) 3.505*** (4.587) 0.328*** (5.863) 0.568** (1.977) 0.180

0.016*** (4.595) 0.744*** (14.836) 0.010*** (3.235)

0.268*** (4.311) 4.020*** (5.083) 0.333*** (5.884) 0.535 (1.562) 0.179

0.012*** (4.557) 0.790*** (19.652) 0.008** (2.604)

c1 R-bar square

2nd quarter

r2t

0.665

0.762

0.691

Panamax

ai, i = 1, 2 bi,1, i = 1, 2 ki,1, i = 1, 2

0.004** (2.125) 0.948*** (46.093) 0.004 (1.224)

c1 R-bar square Supramax ai, i = 1, 2 bi,1, i = 1, 2 ki,1, i = 1, 2

0.913 0.016*** (4.488) 0.801*** (19.982) 0.010*** (3.277)

c1 R2

0.643

0.751

0.594

0.779

0.663

0.149*** (3.122) 1.944*** (3.219) 0.221*** (3.002) 1.067*** (5.206) 0.110 0.208*** (3.812) 3.398*** (4.754) 0.353*** (6.424) 0.169 (0.404) 0.178

The lag length, p, is chosen on the basis of the SBIC, Schwarz (1978). Models are estimated by non-linear GMM of Hansen (1982), where standard errors are corrected for heteroscedasticity and autocorrelation using the Newey–West (1987) method. Figures in () are t-statistics. * Significance at the 10% levels. ** Significance at the 5% levels. *** Significance at the 1% levels.

Wang (1994) and Llorente et al. (2002). This is because increase in FFA trading activity due to increase in FFA prices, in the form of a momentum effect in trading, combined with a reduction in trade due to higher price volatility, suggest that a larger proportion of trades in this market are information driven trades (IDT Hypothesis) and for speculative rather than hedging objectives. 6. Conclusions The aim of this paper was to examine the price–volume and volume–volatility relationships in the dry bulk FFA market. A variety of econometric techniques are employed to investigate the lead-lag relationships between FFA price changes and level of trading activity for three different vessel classes. In addition, the volume–volatility relationship is examined using asymmetric conditional heteroscedasticity models. The results indicate the existence of a significant positive relationship between price change and trading activity in the FFA market for dry bulk vessels, which is consistent with the literature for financial markets. Causality tests between the two variables indicate that FFA price changes Granger-cause trading volume in the FFA market for Capesize and Panamax vessels, while trading volume seem to Granger-cause prices in the Supramax market. For former markets such a pattern implies a momentum effect as higher returns encourage more transactions thus leading to an increase in trading volume. However, there is no evidence of causality from volume to price changes. Results from the asymmetric conditional volatility models indicate the asymmetric response of FFA price volatility to shocks in the market and there is a positive relationship between trading volume and price volatility. The main contributions of the paper are as follows. First, it is observed that a momentum effect drives the FFA market and trading activities as price increases tend to lead to more transactions and trading activities. Second, the positive contemporaneous relationship between volatility and trading activity in the FFA market, in line with the MDH of Clark (1973), implies

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