Speculative Activity and Returns Volatility of Chinese ... · In particular, commodity index...

Speculative Activity and Returns Volatility of Chinese Major

Agricultural Commodity Futures*

Martin T. Bohl1, Pierre L. Siklos 2 and Claudia Wellenreuther 3

November 16, 2016

* An earlier version of the paper was presented at the 2016 Econometric Research in Finance

(ERFIN) Conference in Warsaw. The authors are grateful for the comments by the participants.

1 Corresponding author: Martin T. Bohl, Department of Economics, Westfälische Wilhelms-University Münster, Am Stadtgraben 9, 48143 Münster, Germany, phone: +49 251 83 25005, fax: +49 251 83 22846, e-mail: [email protected]

2 Pierre L. Siklos, Lazaridis School of Business & Economics, Wilfrid Laurier University, 75 University Avenue West, Waterloo, ON, N2L 3C5, Canada, e-mail: [email protected]

3 Claudia Wellenreuther, Department of Economics, Westfälische Wilhelms-University Münster, Am Stadtgraben 9, 48143 Münster, Germany, phone: +49 251 83 25003, fax: +49 251 83 22846, e-mail: [email protected]

Speculative Activity and Returns Volatility of Chinese Major

Agricultural Commodity Futures

Abstract

We empirically investigate whether speculative activity in Chinese futures markets for

agricultural commodities destabilizes futures returns. To capture speculative activity a

speculation and a hedging ratio is used. Applying GARCH models we first analyse the

influence of both ratios on the conditional volatility of three heavily traded Chinese futures

contracts, namely soybeans, soybean meal, and sugar. Additionally, VAR models in

conjunction with Granger causality tests, impulse-response analyses and variance

decompositions are used to get insight into the lead-lag relationship between speculative

activity and returns volatility.

Keywords: Speculation Ratio, Returns Volatility, Chinese Futures Markets, Agricultural

Commodities

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1. Introduction

Since the mid-2000s, commodity markets have witnessed turbulent times, including both

dramatic price peaks in 2007-2008, and again in 2010-2011, and a surge in returns volatility.

Furthermore, a sharp rise in the popularity of commodity investing has triggered a large inflow of

investment capital into commodity futures markets. This phenomenon, known as the

“financialization” of commodity markets, has encouraged a heated public and an extensive

academic debate (e.g., see Cheng and Xiong 2014).

In particular, commodity index traders, who represent a new player in commodity futures

markets, have become the centre of public attention. Hedge fund manager Michael W. Masters is a

leading supporter of the claim that the spikes in commodity futures prices in 2007-2008 were

mainly driven by long-only index investment. Masters argues that the index investment created

massive buying pressure, which thus led to a bubble in commodity prices with prices far away from

their fundamental values (Master 2008, Master and White 2008). Nevertheless, the academic

literature that has generated thorough empirical analyses has failed to find compelling evidence for

the Masters hypothesis (Irwin et al. 2009, Stoll and Whaley 2009, Gilbert and Morgan 2010).

Discussing several empirical findings on the influence of index traders, Irwin and Sanders (2012)

conclude that index trading is unrelated to the recent price peaks.

While the academic debate about the effects of long-only index investment seems to be settled,

the role of traditional speculators on commodity futures markets, the so called long-short investors,

still remains an empirical issue. Our research builds upon this debate and aims to investigate

whether long-short speculators contribute to the observed price changes. Studies by Till (2009) and

Sanders et al. (2010) come to the conclusion that long-short speculators are not to blame for the

excessive price impact in 2007-2008 because the rise in speculation was only a response to a rise

in hedging demand. Brunetti et al. (2011) use Granger causality tests to analyse the relationship

between changes in the net positions of hedge funds in three commodities, namely corn, crude oil

and natural gas, and volatility. The authors find that such funds actually stabilize prices by

decreasing volatility. Miffre and Brooks (2013) also investigate the influence of long-short

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speculators and conclude that speculators have no significant impact on volatility or cross-market

correlation.

Only a few studies investigate the influence of futures speculation on spot returns volatility.

Bohl et al. (2012) analyse how expected and unexpected speculative volume and open interest of

six heavily traded futures contracts impact on conditional spot returns volatility. After applying

their tests to two sub periods, which differ by the size of the market shares of speculators, they

conclude that the financialization of commodity futures markets does not increase volatility of spot

returns. Furthermore, Kim (2015) shows that especially during the recent period, when

commodities have become financial assets attracting diverse types of speculators, speculation in

futures markets can even contribute to reducing spot returns volatility.

The literature review indicates that most of the studies show either no effect or even a

stabilizing effect of speculation on returns volatility. However, the studies above only investigate

futures markets in the US and are all based on Commitments of Traders (COT) reports, provided

by the US Commodity Futures Trading Commission (CFTC). The original COT report, which

separates solely traders into commercial (hedgers) and non-commercial traders (speculators), has

been put into question many times from diverse perspectives (Peck 1982, Ederington and Lee

2002). To deal with these concerns, the CFTC published two variations to the COT reports, the

Disaggregated Commitments of Traders (DCOT) report, which further disaggregates the

commercial and non-commercial trader categories and the Supplemental Commitments of Traders

(SCOT).1 Nevertheless, CFTC data are publicly available only at a weekly level and therefore

unsuited for analyses, which aim to examine the short-run dynamics of commodity prices. To

investigate effects of speculative activity on return volatility, empirical analyses should be based

on data at the daily frequency. Furthermore, the CFTC publishes only data for specific futures

contracts traded on markets in the US. Hence, to investigate other markets apart from the US,

different methods to separate hedging from speculative activity must be applied.

1 For more details about the CFTC database see Stoll and Whaley (2009) as well as Irwin and Sanders (2012).

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Therefore, we use two ratios, namely ones proposed by Garcia et al. (1986) and Lucia and

Pardo (2010), that combine trading volume and open interest data to measure the relative

dominance of speculative activity and hedging activity on a market. The extant literature on

commodity futures markets has generally accepted that volume contains information about

speculative activity while open interest reflects hedging activity (Rutledge 1979, Leuthold 1983,

Bessembinder and Seguin 1993).

Anecdotal evidence suggests that trading behaviour in Chinese financial markets is highly

speculative. For example, the Wall Street Journal even compares China’s stock markets to casinos,

which are driven by fast money flows (Wall Street Journal, August 22, 2001). Due to strengthening

stock market regulation, drawn by the collapse in Chinese stock markets in 2015, futures markets

for commodities have also become very attractive to speculators lately. Recently, the Financial

Times states: “In the past month near mania has gripped China’s commodity futures markets with

day traders and yield-hungry wealth managers pouring into a lightly regulated sector, often with

astonishing results.” (Financial Times, April 27, 2016). In a similar vein, a report published by

Citigroup Research describes Chinese investors as perhaps prone to being the most speculative in

the world. Furthermore, the report points out that speculative trading volume on Chinese

commodity futures markets has exploded in the last years and has created high returns volatility

(Liao et al. 2016). Using market activity data enables us to examine the speculative content in

China’s futures markets.

Another argument in favour of examining Chinese commodity markets, is that Chinese futures

markets for commodities have grown rapidly in recent years. A loosening of regulations also

permits foreign investors to participate in Chinese futures markets and trading volume has increased

substantially. According to trading volume, commodity futures markets in China already belong to

the most active ones in the world. For instance, Dalian Commodity Exchange (DCE) soybeans

futures market is the second largest soybeans futures market in the world, right behind the one of

the Chicago Board of Trade (CBOT). Therefore Chinese futures markets gain more and more global

importance and Chinese prices have begun affecting global prices for commodities (Wang et al.

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2016, Wang and Ke 2005). Additional to the increased trading volume, Chinese futures markets

have also seen turbulent events in the last decade, including price spikes and high returns volatility.

Keeping in mind all these facts it is quite surprising that studies on futures markets in China

are rare. Due to its global importance and the mentioned characteristics, it is important to investigate

speculation in Chinese futures markets. To measure speculative activity we use the speculation ratio

as well as the hedging ratio. The empirical analysis includes GARCH models and Granger causality

tests to examine both contemporaneous and lead-lag relationships between speculation activity and

returns volatility in three heavily traded agricultural commodities, namely soybeans, soybean meal

and sugar.

In contrast to the available literature we find a positive influence of the speculation ratio on

returns volatility and a negative influence of the hedging ratio on conditional volatility for all

commodities examined. These empirical results indicate that a rise in speculative activity leads to

an increase in returns volatility and a rise in hedging activity stabilizes the returns. Moreover, we

show that for soybeans, soybean meal and cotton, the speculation ratio and the hedging ratio

Granger causes conditional returns volatility and vice versa. This relationship is positive in the case

of the speculation ratio and negative in the case of the hedging ratio. These results imply that the

amount of speculative activity in relation to hedging activity contains information about changes in

futures returns volatility.

The remainder of this paper is structured as follows: In Section 2 we introduce the speculation

measures and their computation. After presenting the econometric methods in section 3 and the

data in section 4, we discuss the empirical results in section 5. Section 6 summarizes our findings

and concludes.

2. Measures Construction

To analyse the character of trading activity on a specific trading day, we compute two ratios,

both of which combine daily figures of volume and open interest. Daily trading volume captures

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all trades of a particular contract, which are executed during a specified day. Open interest describes

all positions of that contract that are still open at the end of that trading day, meaning that the

position has neither been equalized by an opposite futures position nor been fulfilled by the physical

delivery of the commodity or by cash settlement. The first ratio used in this study captures

speculative activity and is defined as daily trading volume (𝑇𝑇𝑇𝑇𝑡𝑡) divided by end-of-day open

interest (𝑂𝑂𝑂𝑂𝑡𝑡):

𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑡𝑡𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 =

𝑇𝑇𝑇𝑇𝑡𝑡𝑂𝑂𝑂𝑂𝑡𝑡

(1)

The speculation ratio measures the relative dominance of speculative activity in the contract

analysed in comparison to the hedging activity. A high (low) speculation ratio indicates high (low)

speculative activity with respect to hedging activity. Therefore, a rise in the speculation ratio

reflects a rise in the dominance of speculators in the market.

The idea behind the speculation ratio lies in the assumption that hedgers hold their positions

for longer periods due to their underlying positions, whereas speculators mainly try to avoid holding

their positions over night. Based on their different trading behaviours, speculators and hedgers

influence the amount of trading volume and open interest in a different way. Speculators mostly

impact on trading volume instead of open interest because they buy and sell contracts during the

day and close their positions before trading ends. Thus outstanding contracts at the end of a trading

day are mainly hold by hedgers (Rutledge 1979, Leuthold 1983, Bessembinder and Seguin 1993).

Obviously, the ability of the ratio to measure the dominance of speculative activity depends on the

assumption that hedgers and speculators sit on their trading position for different time periods.

There is empirical evidence that seems to approve the assumption that hedgers tend to hold their

position for longer periods than speculators (Ederington and Lee 2002, Wiley and Daigler 1998).

We use a second ratio to provide supportive results of the first one. The second ratio is also

based on the different trading behaviour of speculators and hedgers, but relates daily trading volume

to open interest in a different way. The ratio gauges the relative importance of hedging activity

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instead of speculative activity on a specific trading day and is defined as the daily change in open

interest ( ΔOIt = OIt – OIt−1) divided by daily trading volume:

Ratio𝑡𝑡𝐻𝐻𝑆𝑆𝐻𝐻𝐻𝐻𝑆𝑆 =

∆OIt𝑇𝑇𝑇𝑇𝑅𝑅

(2)

The change in open interest during period t is a measure of net positions being opened or closed

each day and held overnight and is used to capture the hedging activity. The hedging ratio can take

any value in the range of [1 and -1], where a high hedging ratio, with a value close to one, indicates

low speculative activity in the contract examined. The correlation between the two ratios used in

this study should be negative.

Based on the speculation ratio (1) we are able to investigate the role of short term speculators

on commodity futures markets. In a few studies, short term speculation in US futures markets is

explored by using the speculation ratio. For agricultural commodities Streeter and Tomek (1992)

find a positive influence of the speculation ratio on returns volatility for soybeans. Robles et al.

(2009) investigate speculative activity in four agricultural future markets and find a Granger

causality relationship between the speculation ratio and prices for wheat and rice. More recently

Chan et al. (2015) examine the role of speculators on oil futures markets by using the speculation

ratio to proxy speculative activity and conclude that the oil futures market is dominated by

uniformed speculators in the post-financialization period. 2 Only Lucia et al. (2015) apply both the

speculation (1) and hedging ratios (2) to explore the relative importance of speculative activity

versus hedging activity in the European carbon futures market. The authors show the different

dynamics of speculative behaviour during three phases of the European Union Emission Trading

Scheme.

2 The speculation ratio has not only be used to investigate commodity markets. Chatrath et al. (1996), for instance, apply the speculation ratio to examine the influence of speculation on the volatility of exchange rates.

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3. Econometric Methodology

To analyse the impact of speculative activity, proxied by the speculation and the hedging ratio,

on returns volatility, a generalized autoregressive conditional heteroscedasticity (GARCH) model

(Bollerslev 1986), is used. Our AR(1)-GARCH(1,1) model reads as follows:

𝑟𝑟𝑡𝑡 = 𝑅𝑅 + 𝑏𝑏1𝑟𝑟𝑡𝑡−1 + 𝜀𝜀𝑡𝑡 (3)

𝜎𝜎𝑡𝑡2 = 𝛼𝛼0 + 𝛼𝛼1𝜀𝜀𝑡𝑡−12 + 𝛽𝛽1𝜎𝜎𝑡𝑡−12 + 𝛾𝛾𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑡𝑡 (4)

where 𝑟𝑟𝑡𝑡 = (𝑙𝑙𝑙𝑙(𝑃𝑃𝑅𝑅) − 𝑙𝑙𝑙𝑙(𝑃𝑃𝑅𝑅−1)) ∗ 100 is the return on day t, 𝜎𝜎𝑡𝑡2 is the conditional volatility on day t

and 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑡𝑡𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆,𝐻𝐻𝑆𝑆𝐻𝐻𝐻𝐻𝑆𝑆 describes the speculation ratio on day t in the first run and hedging ratio on

day t in the second run.3 The mean equation (3) models the returns as a first-order autoregressive

(AR) process. The relationship between conditional volatility and speculative activity has been

modelled by modifying the volatility equation (4). The parameter 𝛼𝛼1 captures the ARCH effect,

which measures the reaction of conditional volatility to new information (shocks), whereas 𝛽𝛽1

describes the GARCH effect, which displays the duration of a shock to die out.

The influence of speculative activity is captured by the parameter 𝛾𝛾 . In the case of the

speculation ratio a positive sign of the parameter 𝛾𝛾 implies a destabilizing effect of speculative

activity on returns volatility, whereas a negative sign would imply a stabilizing effect. Regarding

the hedging ratio a negative influence indicates that speculation drives volatility, while a positive

influence points out that speculation stabilizes the market. Furthermore, the GARCH (1,1) model

has a number of restrictions to ensure a positive conditional variance, i.e., 𝛼𝛼0 > 0, 𝛼𝛼1 ≥ 0, 𝛽𝛽1 ≥ 0

and 𝛼𝛼1 + 𝛽𝛽1 ≤ 1.

The previously introduced GARCH model only measures the possible influence of speculative

activity on conditional volatility and not vice versa. Since not only speculation can drive returns

volatility, but high volatility also can attract speculators attention and thus lead to speculative

activity, we are also interested in the lead-lag relationship between the two variables. To investigate

3 We apply a GARCH model of order p = 1 and q = 1, since a number of researchers have frequently demonstrated the suitability of GARCH (1,1) models to represent the majority of financial time series (Bera and Higgins 1993).

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the dynamic relationship (lead-lag) of returns volatility and the speculative activity, we use a vector

autoregressive (VAR) model that is expressed by the two following equations:

𝜎𝜎𝑡𝑡2 = 𝑅𝑅0 + �𝛼𝛼1,𝑡𝑡𝜎𝜎𝑡𝑡−𝑖𝑖2𝑘𝑘

𝑖𝑖=1

+ �𝛽𝛽1,𝑡𝑡𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑡𝑡−𝑖𝑖

𝑘𝑘

𝑖𝑖=1

+ 𝜖𝜖𝑡𝑡 (5)

𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑡𝑡 = 𝑅𝑅0 +�𝛼𝛼1,𝑡𝑡𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑡𝑡−𝑖𝑖

𝑘𝑘

𝑖𝑖=1

+ �𝛽𝛽1,𝑡𝑡𝜎𝜎𝑡𝑡−𝑖𝑖2𝑘𝑘

𝑖𝑖=1

+ 𝑢𝑢𝑡𝑡 (6)

The VAR equations show that the endogenous variables as conditional volatility (𝜎𝜎𝑡𝑡2), that is

estimated from a simple GARCH(1,1) model, and speculation ratio (𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑡𝑡𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆) in the first run and

hedging ratio (𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑡𝑡𝐻𝐻𝑆𝑆𝐻𝐻𝐻𝐻𝑆𝑆) in the second run are dependent on their own lagged values and on the

lagged values of the respective other variable. Optimal lag length (k) for each variable of the VAR

model is determined by minimizing the (Schwarz 1978) information criterion. Parameters ϵt and

ut represent the residuals of the regression, which are assumed to be mutually independent and

individually i.i.d. with zero mean and constant variance.

Based on the VAR model (5) and (6), we perform three further analyses. They are: Granger

causality testing, variance decompositions and impulse response function estimation. Granger

causality tests (Granger 1969) are applied to gain information about the lead-lag relationship

between returns volatility and the speculation ratio and returns volatility and the hedging ratio

respectively. The test will help to answer the question, whether speculative activity causes

conditional volatility in a forecasting sense and/or vice-versa. To test for Granger causality we

estimate a standard F-test and test the null hypothesis, which states that the speculative activity

(conditional volatility) does not Granger causes conditional volatility (speculative activity). The

hypothesis is rejected when coefficients of the lagged values are jointly significantly different from

zero (𝛽𝛽1 ≠ 𝛽𝛽2 ≠ … ≠ 𝛽𝛽𝑘𝑘 ≠ 0).

Next analysis, based on our VAR model, we obtain the variance decompositions. These

measure the percentage of the forecast error of a variable that is explained by another variable. It

indicates the conditional impact that one variable has upon another variable within the VAR system.

Therefore the variance decomposition mirrors the economic significance of these impacts as the

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percentages of the forecast error for a variable sum to one (Fung and Patterson 1999). To find out

whether the causal relationships are positive or negative we compute impulse response functions,

which explain the impact of an exogenous shock in one variable on the other variables of the VAR

system. Finally, we generate the impulse responses to visually represent and analyse the behaviour

of volatility on simulated shocks in the speculation ratio or in the hedging ratio respectively and

vice versa.

4. Data

To examine China’s agricultural commodity markets, we analyse three heavily traded

commodity futures contracts for soybeans, soybean meal and sugar. Currently, there are four futures

exchanges in China, namely, the Dalian Commodity Exchange (DCE), the Zhengzhou Commodity

Exchange (ZCE), the Shanghai Futures Exchange and the China Financial Futures Exchange.

Agricultural commodities are mainly traded on DCE and ZCE. Therefore, our analyses focuses on

these two futures exchanges. The contracts for soybeans and soybean meal are traded on the DCE,

whereas the sugar futures contract is traded on the ZCE.

We have selected the most active contracts according to their trading volume. For all three

contracts, daily prices (settlement prices) and daily figures of trading volume and open interest (end

of day) are obtained from Thomson Reuters Datastream. Prices of contracts are quoted in Chinese

Yuan Renminbi per 10 metric ton (MT), daily trading volume represents the number of contracts

traded during a day and open interest reflects the number of contracts outstanding at the end of a

trading day. The sample period extends from 1 July, 2002 to 29 July, 2016 for soybeans, from 1

January, 2001 to 29 July, 2016 for soybean meal, and from 3 March, 2006 to 29 July, 2016 for

sugar. Table 1 provides the key specifications for each futures contract.

[Table 1 about here]

Table 2 displays summary statistics for returns 𝑟𝑟𝑡𝑡, open interest 𝑂𝑂𝑂𝑂𝑡𝑡, trading volume 𝑇𝑇𝑇𝑇𝑡𝑡, the

speculation ratio 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑡𝑡𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 and the hedging ratio 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑡𝑡

𝐻𝐻𝑆𝑆𝐻𝐻𝐻𝐻𝑆𝑆 for all three commodities examined.

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It gives the mean, maximum, minimum, standard deviation (Std. Dev.), skewness, kurtosis and

Jarque–Bera statistics (JB) with corresponding probability values in parenthesis.


Several interesting findings are reported in table 2. The speculation ratios for sugar and soybean

meal futures have the highest means with 1.44 and 1.04. The ratio for soybeans futures shows the

lowest mean with 0.71. Note that a high ratio implicates a high amount of speculative activity

compared to hedging activity. In addition, the speculation ratio of soybean meal futures appears to

be most volatile as indicated by the largest maximum and minimum values and the high standard

deviation of the ratios.

The mean values of the hedging ratios are close to zero and negative for all contracts. The

distance of the extreme values of the hedging ratios is the highest for soybeans. A ratio close to

minus one indicates high speculative activity in respect with hedging activity. Soybean meal shows

the highest speculation with a hedging ratio of -0.99.

Mean returns are close to zero and positive for all the time series examined. According to the

distance of the extreme values (minimum, maximum) and the standard deviation, the market for

soybeans reveals the highest volatility. Skewness and kurtosis parameters indicate that none of the

three time series follows a normal distribution. This can be confirmed by the Jarque-Bera statistics

and its corresponding p-values. Regarding the results of Jarque-Bera tests the null hypothesis of

normal distribution is rejected for all series at the 1% significance level.

[Figure 1 about here]

Figure 1 shows futures prices and calculated speculation ratios for the three commodity

contracts examined. Futures prices for soybeans increased up to early 2008 but then crashed down

during the world financial crisis. Interestingly, the speculation ratio for soybeans has its maximum

also in the beginning of 2008. Time series for soybean meal also indicate co-movements in prices

and the speculation ratio. Peaks in soybean meal prices are also observable in the speculation ratio

for soybean meal. According to these graphs it seems like times of high prices coincide with a high

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dominance of speculative activity compared to hedging activity. After reaching a peak in 2006,

futures prices for sugar fall rapidly till the beginning of 2009. After financial crisis sugar prices rise

up again and reach their maximum in the end of 2011.

We apply augmented Dickey and Fuller (1979) (ADF) unit root tests on prices, returns, trading

volume, open interest, speculation ratio and hedging ratio. The numbers of lags are selected in

accordance with the Akaike information criterion. Results of ADF tests are presented in Table 3.

The results show that prices of all contracts and some time series of trading volume and open

interest contain a unit root, whereas the ADF test clearly rejects the unit root for returns and both

ratios for all three contracts. Thus, each of the time series that are used in empirical tests are

stationary. To test for conditional heteroscedasticity we perform Engle’s Lagrange Multiplier (LM)

test (Engle 1982) on returns. The test results, also displayed in table 3, show that GARCH effects,

implying volatility clusters, are present in all series. The results of LM tests confirm our assumption

and motivate the usage of the GARCH model.


5. Empirical Results

In this section results of both GARCH and VAR analyses are discussed. We start with the

GARCH results, which are presented in table 4. The GARCH(1,1) model is used to measure the

influence of speculative activity on volatility. First, we run the GARCH model extended by the

speculation ratio. Some findings are similar across all three commodities examined. GARCH and

ARCH parameters and the ratio have a highly statistically significant and positive influence in each

case and stationarity requirements implying shocks die out in finite time are met for all contracts.

The constant, which represents the time-invariant level of conditional volatility, is positive and

highly significant for soybeans and soybean meal. The significantly positive influence of the

speculation ratio implicates that conditional volatility is driven by speculative activity in each case.

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Results of the second run, when the hedging ratio is used as an explanatory variable, support

the results of the first run. For all contracts examined, the GARCH as well as ARCH parameters

are highly significant and positive. Additionally, all stationarity requirements are met. The hedging

ratio has a significantly negative influence on conditional volatility in each case, indicating a

stabilizing influence of hedging activity.4


Further, we discuss the results of the tests that are based on the VAR model. Optimal lag length

(k) for each variable of the VAR model is determined by minimizing the Schwarz information

criterion. We set maximum lag length at kmax=20 (4 trading weeks). For this purpose, all possible

combinations between 1 and 40 lags of the variables are considered. In the case of the speculation

ratio, for soybeans an optimal lag length of k=3 is chosen and optimal lag length of k=4 is selected

for the remaining commodities. In the case of the hedging ratio an optimal lag length of k=1 is used

for each of the commodities.

Table 5 reports results of Granger causality tests between speculation ratio (hedging ratio) and

conditional volatility for all three commodities examined. Number of observation, F-values,

probability values and the number of lags of Granger causality relations are displayed for each of

the contracts.


Testing for Granger causality between the speculation ratio and conditional volatility, the

results are as follows: for soybeans, soybean meal and sugar the null hypothesis can be rejected in

both cases. Therefore, speculation ratio Granger causes conditional volatility and volatility causes

speculation ratio also in the Granger sense. These results imply that the amount of speculative

activity in relation to hedging activity contains information about changes of volatility in the future.

Also, current volatility involves information about futures speculative activity. In the case of the

4 GARCH-in-Mean (GARCH-M) tests are also applied to the data but GARCH terms in the mean equations are not significant. Higher AR terms added in the mean equation are either insignificant or do not change the conclusions.

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hedging ratio, the results indicate that the hedging ratio Granger causes conditional volatility for all

contracts but not vice versa.5

The VAR estimation results are also used to perform a variance decomposition for all

commodities, for that we were able to rejects the null hypothesis of no‐causality. Results of the

variance decomposition for volatility and speculation ratio as well as the hedging ratio are presented

in table 6. The table presents results in percent for trading day 1, 5, 15 and 20. Across all contracts

examined, we observe similar results. Variations in volatility are mostly caused by their own lagged

values, while the speculation ratio appears to play only a minor role in explaining return volatility.

Own lagged values of the speculation (hedging) ratio are also mostly responsible for its own

variation. Thus lagged volatility only explains a small effect of the variation of the two ratios.


In a next step we examine the impact of a one standard deviation shock to the VAR system.

Therefore we create impulse response functions for all commodities. Impulse respond functions

displays the response of volatility to simulated shocks to the speculation (hedging) ratio and vice

versa.

[Figures 2, 3 and 4 about here]

Figure 2, 3 and 4 display impulse response functions for all commodities examined. Figure 2

shows the response of conditional volatility to shocks in the speculation ratio, whereas figure 3

displays the response of the speculation ratio to volatility shocks. Since we were only able to find

unidirectional Granger causality in the case of the hedging ratio, only the significant response of

conditional volatility to shocks in the hedging ratio is presented in figure 4. Shocks are simulated

as one standard deviation and 20 trading days are regarded.

Regarding the speculation ratio, for all commodities the response of conditional volatility to

shocks in the speculation ratio is positive. This implicates that a rise in speculative activity leads to

5 Granger causality tests applied on samples only consisting of data exclusive since 2007 do not change the conclusions.

14

a rise in price volatility. The rise of volatility persists up to 5 days for soybeans, up to 9 days for

soybean meal and up to 12 days for sugar and afterwards volatility converged to its mean. All

responses are significant. One exception is the response of soybean meal volatility to shocks in the

ratio. The response becomes significant positive after 4 trading days. Volatility shocks also produce

only positive responses in speculation ratio for all commodities. Therefore, speculative activity is

driven by inclines in volatility. Figure 4 displays the response of volatility to shocks in the hedging

ratio. The response is significant negative for all three commodities, which implies that a rise in the

dominance of hedging activity in a contract leads to a reduction in conditional volatility. All results

of the VAR model support the results of the previous GARCH model.

6. Conclusion

Motivated by periods of high returns volatility as well as the ongoing finalization of

agricultural commodity futures markets, we investigate the impact of speculative activity on returns

volatility of Chinese commodity futures markets. We focus on Chinese futures markets because

these markets are believed to be highly speculative. Additionally, China’s futures markets for

commodities have grown rapidly in the last years and their global importance is increasing.

However, studies investigating Chinese futures markets, especially empirical ones, are rare. On that

account, we use a speculation ratio defined as trading volume divided by open interest to capture

the relative dominance of speculative activity in China’s futures markets. To verify our results we

use a second ratio, which captures the relative importance of the hedging behaviour instead of

speculative behaviour, by combining volume and open interest data in a different way.

To estimate the influence of speculative activity, proxied by the two ratios, on futures returns

volatility, we estimate both GARCH and VAR models. The empirical tests enable us to get insight

into the contemporaneous and the lead-lag relationship between speculative activity and returns

volatility of three heavily traded Chinese futures contracts, namely soybeans, soybean meal and

sugar. The contracts are traded on the Dalian Commodity Exchange and on the Zhengzhou

15

Commodity Exchange. We find a positive influence of the speculation ratio on volatility for all

commodities examined, using the GARCH model. These empirical results indicate that a rise in

speculative activity can lead to an increase in returns volatility. This deduction is supported by a

negative influence of the hedging ratio on returns volatility. Moreover, we show that for soybeans,

soybean meal and sugar both ratios Granger cause conditional volatility and vice versa. The

influence is positive in the case of the speculation ratio and negative in the case of the hedging ratio.

These results imply that the amount of speculative activity in relation to hedging activity contains

information about changes in futures volatility.

Interestingly, our results seem to be inconsistent with the results of the current literature, which

finds a stabilizing influence of speculation on returns volatility. The stabilizing hypothesis is quite

reasonable since speculation provides liquidity to the market, helps hedgers to find their

counterparties to hedge their risks, improves price discovery and therefore stabilizes prices.

So, why are our results contrary to previous findings? In contrast to our research, most of the

studies that have found stabilizing effects concentrate on US markets and use weekly data of CFTC

reports to measure speculation. Our research, however, investigates Chinese commodity futures

markets, which appear to be characterized by trading behaviour that is extremely speculative and

where presumable speculative activity often exceeds the hedging demand. In that context, our

results imply that speculation is not harmful in general, but excessive speculation, which is above

hedging needs drives returns volatility. Finally, our study suggests useful policy implications. First,

it is important to distinguish between speculation and excessive speculation and therefore policy

decisions should concentrate on an adequate level of speculation, in relation to the level of hedging

activity. Furthermore, regulation should aim to curb only “harmful” speculation, which is

disproportionately high to hedging activity.

16

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Table 1: Contract specifications

Contract Futures Exchange

Contract Size Currency Sample Number of Obs.

No. 1 Soybeans Dalian Commodity exchange (DCE)

10 MT Chinese Yuan Renminbi

7/01/2002 -7/29/2016 (daily) 3227

Soybean Meal Dalian Commodity exchange (DCE)


01/05/2001 - 07/29/2016 (daily) 3475

White Sugar Zhengzhou Commodity exchange (ZCE)


3/03/2006 -7/29/2016 (daily) 2487

Table 2: Descriptive Statistic

Note: This table presents descriptive statistics of the investigated time series of the three futures contracts. 𝐫𝐫𝐭𝐭, 𝐎𝐎𝐎𝐎𝐭𝐭 and 𝐓𝐓𝐓𝐓𝐭𝐭 describe the returns, end- of-day open interest and daily trading volume on day t. The speculation ratio is represented by 𝐑𝐑𝐑𝐑𝐭𝐭𝐑𝐑𝐑𝐑𝐭𝐭

𝐒𝐒𝐒𝐒𝐒𝐒𝐒𝐒and the hedging Ratio by 𝐑𝐑𝐑𝐑𝐭𝐭𝐑𝐑𝐑𝐑𝐭𝐭𝐇𝐇𝐒𝐒𝐇𝐇𝐇𝐇𝐒𝐒. JB stands for Jarque-Bera statistics and significance at

the 1% level is represented by ***. Both open interest and trading volume are presented in units of 1000. All data is taken from Thomson Reuters Datastream.

Variable Mean Maximum Minimum Std.dev. Skewness Kurtosis JB

Soybeans 𝒓𝒓𝒕𝒕 0.016 6.189 -9.594 1.057 -0.570 11.965 10980.468***

𝑶𝑶𝑶𝑶𝒕𝒕 461.467 1205.210 87.022 210.408 0.918 3.840 548.340***

𝑻𝑻𝑻𝑻𝒕𝒕 338.442 2677.400 2.796 317.679 2.041 9.035 7136.405***

𝑹𝑹𝑹𝑹𝒕𝒕𝑹𝑹𝑹𝑹𝒕𝒕𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺

0.708 7.099 0.015 0.588 2.591 15.221 23693.053***

𝑹𝑹𝑹𝑹𝒕𝒕𝑹𝑹𝑹𝑹𝒕𝒕𝑯𝑯𝑺𝑺𝑯𝑯𝑯𝑯𝑺𝑺

-0.010 0.844 -0.990 0.094 -2.097 24.542 64759.520***

Soybean Meal 𝒓𝒓𝒕𝒕 0.017 8.792 -14.644 1.423 -0.963 13.974 17974.620***

𝑶𝑶𝑶𝑶𝒕𝒕 1240.684 5837.670 7.682 1319.471 0.973 2.826 552.577***

𝑻𝑻𝑻𝑻𝒕𝒕 1066.920 11868.480 1.800 1313.051 2.336 10.652 11638.600***

𝑹𝑹𝑹𝑹𝒕𝒕𝑹𝑹𝑹𝑹𝒕𝒕𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺 1.104 8.379 0.010 0.803 2.229 11.626 13653.288***

𝑹𝑹𝑹𝑹𝒕𝒕𝑹𝑹𝑹𝑹𝒕𝒕𝑯𝑯𝑺𝑺𝑯𝑯𝑯𝑯𝑺𝑺

-0.004 0.591 -0.999 0.065 -3.626 48.854 312055.157***

Sugar 𝒓𝒓𝒕𝒕 0.006 10.796 -10.370 1.201 -0.092 15.805 16993.448***

𝑶𝑶𝑶𝑶𝒕𝒕 801.586 1556.438 7.116 364.161 -0.338 2.495 73.648***

𝑻𝑻𝑻𝑻𝒕𝒕 1112.418 5438.290 1.436 836.960 1.389 5.428 1410.622***

𝑹𝑹𝑹𝑹𝒕𝒕𝑹𝑹𝑹𝑹𝒕𝒕𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺 1.443 7.594 0.029 0.897 1.651 7.779 3496.266***

𝑹𝑹𝑹𝑹𝒕𝒕𝑹𝑹𝑹𝑹𝒕𝒕𝑯𝑯𝑺𝑺𝑯𝑯𝑯𝑯𝑺𝑺 -0.001 0.527 -0.774 0.047 -0.989 48.293 212987.313***

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Figure 1: Futures prices and speculation ratios for soybeans, soybean meal, and sugar.

2000

2500

3000

3500

4000

4500

5000

5500

6000

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

CN

Y/1

0 M

TSoybeans Futures Price

0

1

2

3

4

5

6

7

8

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

Rat

io (T

V/O

I)

Soybeans Speculation Ratio

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1000

1500

2000

2500

3000

3500

4000

4500

5000C

NY

/10

MT

Soybean Meal Futures Price

0

1

2

3

4

5

6

7

8

9

Rat

io (T

V/O

I)

Soybeans Meal Speculation Ratio

22

Note: The graphs show daily prices quoted in Chinese Yuan Renminbi (CNY) per 10 MT and daily speculation ratios, computed as daily trading volume divided by end of day open interest for soybeans, soybean meal and sugar. All data is taken from Thomson Reuters Datastream.

2000

2500

3000

3500

4000

4500

5000

5500

6000

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

CN

Y/1

0 M

TSugar Futures Price

0

1

2

3

4

5

6

7

8

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

Rat

io (T

V/O

I)

Sugar Speculation Ratio

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Table 3: Augmented Dickey Fuller (ADF) Test and Lagrange Multiplier (LM) test

Price Returns Volume Open Interest 𝑹𝑹𝑹𝑹𝒕𝒕𝑹𝑹𝑹𝑹𝒕𝒕

𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺 𝑹𝑹𝑹𝑹𝒕𝒕𝑹𝑹𝑹𝑹𝒕𝒕

𝑯𝑯𝑺𝑺𝑯𝑯𝑯𝑯𝑺𝑺

Soybeans -2.21 (7) -20.27***(6) -4.63***(20) -3.66***(17) -5.68***(17) -11.77***(13)

Soybean Meal -2,64*(6) -19,39***(6) -2,5(29) -1,56(29) -4,26***(29) -16,02***(10)

Sugar -1.11(6) -21.52**(5) -4.2***(11) -2,58*(21) -5.64***(10) -44,19***(0)

LM(1) LM(5) LM(10) LM(15) LM(20)

Soybeans 44,19*** 74,78*** 91,87*** 92,01*** 93,22***

Soybean Meal 27.61*** 49.88*** 55.39*** 60.31*** 123.04***

Sugar 13.00*** 27.17*** 73.83*** 76.34*** 77.99***

Notes: First rows show results of the ADF test and the lower rows show results of the LM tests. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% level, respectively. Numbers of Lags for each ADF- and LM-test are given in parenthesis. Lags are chosen according the AIC.

Table 4: GARCH(1,1)

Speculation Ratio

Soybeans Soybean Meal Sugar

Constant 0.19*** 0.19*** -0.07***

Resid² 0.26*** 0.19*** 0.18***

Volatility 0.38*** 0.53*** 0.51***


0.37*** 0.37*** 0.39***

Hedging Ratio

Soybeans Soybean Meal Sugar

Constant 0.28*** 0.42*** 0.58***

Resid² 0.3*** 0.25*** 0.03***

Volatility 0.49*** 0.57*** 0.58***

𝑹𝑹𝑹𝑹𝒕𝒕𝑹𝑹𝑹𝑹𝒕𝒕𝑯𝑯𝑺𝑺𝑯𝑯𝑯𝑯𝑺𝑺 -0.67*** -1.24*** -1.90***

Notes: Table 4 shows results for volatility equation (eq. 4) using normal distribution. Resid²(-1) and Volatility(-1) represent squared residuals from mean equation (eq. 3) and conditional volatility. Ratiot

Specstands for the computed speculative ratio and captures speculative activity. Ratiot

Hedgerepresents the hedging ratio. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% level, respectively.

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Table 5: Results Granger-Causality test

Null Hypothesis: Obs F-Statistic Prob.

Soybeans

𝑹𝑹𝑹𝑹𝒕𝒕𝑹𝑹𝑹𝑹𝒕𝒕𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺 does not Granger Cause

Conditional Volatility 3224 13.0781*** 0.00000002

Conditional Volatility does not Granger Cause 𝑹𝑹𝑹𝑹𝒕𝒕𝑹𝑹𝑹𝑹𝒕𝒕

𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺 Lags = 3 7.57535*** 0.00005

𝑹𝑹𝑹𝑹𝒕𝒕𝑹𝑹𝑹𝑹𝒕𝒕𝑯𝑯𝑺𝑺𝑯𝑯𝑯𝑯𝑺𝑺 does not Granger Cause



𝑯𝑯𝑺𝑺𝑯𝑯𝑯𝑯𝑺𝑺 Lags = 1 2.83604* 0.0923

Soybean Meal


. does not Granger Cause Conditional Volatility

3470 9.53301*** 1.E-07


𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺 Lags 4 6.13641*** 6.E-05

𝑹𝑹𝑹𝑹𝒕𝒕𝑹𝑹𝑹𝑹𝒕𝒕𝑯𝑯𝑺𝑺𝑯𝑯𝑯𝑯𝑺𝑺 does not Granger Cause



𝑯𝑯𝑺𝑺𝑯𝑯𝑯𝑯𝑺𝑺 Lags = 1 1.86200 0.1725

Sugar

𝑹𝑹𝑹𝑹𝒕𝒕𝑹𝑹𝑹𝑹𝒕𝒕𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺does not Granger Cause

Conditional Volatility 2482 10.1834*** 4.E-08


𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺 Lags = 4 2.79944** 0.0246

𝑹𝑹𝑹𝑹𝒕𝒕𝑹𝑹𝑹𝑹𝒕𝒕𝑯𝑯𝑺𝑺𝑯𝑯𝑯𝑯𝑺𝑺does not Granger Cause



𝑯𝑯𝑺𝑺𝑯𝑯𝑯𝑯𝑺𝑺 Lags = 1 1.22867 0.2678

Note: F-values of the Granger causal relations for each variable within the VAR model are presented. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% level, respectively. The lag lengths are based on the SIC.

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Table 6: Variance Decomposition

Speculation Ratio Soybeans Soybean meal Sugar

Explained Variable Day Volatility Ratiot

Spec Volatility RatiotSpec Volatility Ratiot

Spec

Volatility 1 100.00 0.00 100.00 0.00 100.00 0.00 5 98.78 1.22 99.72 0.28 98.88 1.12 10 97.08 2.92 98.47 1.53 96.21 3.79 15 96.29 3.71 97.38 2.62 93.29 6.71 20 95.97 4.03 96.62 3.38 90.80 9.2

RatiotSpec 1 1.10 98.90 1.21 98.79 0.20 99.79

5 3.39 96.61 2.91 97.09 1.61 98.39 10 4.25 95.75 3.84 96.16 2.49 97.51 15 4.53 95.47 4.28 95.72 3.17 96.83 20 4.64 95.36 4.51 95.49 3.66 96.34

Hedging Ratio Soybeans Soybean meal Sugar

Explained Variable Day Volatility Ratiot

Hedge Volatility RatiotHedge Volatility Ratiot

Hedge

Volatility 1 100.00 0.00 100.00 0.00 100.00 0.00 5 99.50 0.50 99.64 0.36 99.63 0.37 10 99.49 0.51 99.63 0.37 99.57 0.43 15 99.49 0.51 99.63 0.37 99.56 0.44 20 99.49 0.51 99.63 0.37 99.56 0.44

RatiotHedge 1 0.05 99.95 0.01 99.99 0.03 99.97 5 0.13 99.87 0.08 99.92 0.07 99.93 10 0.14 99.86 0.08 99.92 0.08 99.92 15 0.14 99.86 0.08 99.92 0.09 99.91 20 0.14 99.86 0.08 99.92 0.09 99.91

Note: The table presents variance decompositions based on the two VAR models with endogenous variables volatility and 𝐑𝐑𝐑𝐑𝐭𝐭𝐑𝐑𝐑𝐑𝐭𝐭

𝐒𝐒𝐒𝐒𝐒𝐒𝐒𝐒( 𝐑𝐑𝐑𝐑𝐭𝐭𝐑𝐑𝐑𝐑𝐭𝐭𝐇𝐇𝐒𝐒𝐇𝐇𝐇𝐇𝐒𝐒).

Figure 2: Impulse Response Functions – Response of Volatility to Speculation Ratio

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Note: Impulse response functions are displayed along with corresponding plus and minus 2 standard error bands (dashed lines), used to determine statistical significance. The impulse response functions show responses to Cholesky one standard deviation innovations. The horizontal axis shows the number of days after the shock.

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Figure 3: Impulse Response Functions – Response of Speculation Ratio to Volatility.

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Figure 4: Impulse Response Functions – Response Volatility to Hedging ratio.

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