A Time-Varying Approach Applied on Chinese Commodity Futures Markets

RE SEARCH AR TI CLE

Speculation and Volatility - A Time-Varying Approach applied on Chinese Commodity Futures Markets

Claudia Wellenreuther1 | Jan Voelzke2

1Department of Economics, University of Münster, Münster, Germany Experts have long discussed and empirically investigated whether 2Department of Economics, University of speculative activity increases volatility on commodity futures mar- Münster, Münster, Germany kets. However, relatively little empirical research has been conducted to analyze the role of speculators on Chinese commodity Correspondence futures markets, mostly because there is no standard measure for Claudia Wellenreuther, Department of Economics, University of Münster, Am speculative activity. Additionally, most of the existing studies on Stadtgraben 9, 48143 Münster, Germany speculation assume the potential effects of such speculation to be Email: [email protected] constant over a long period of time. We address those shortcom- muenster.de ings and empirically investigate the time-varying relationship be- Funding information tween speculative activity on returns volatility in Chinese futures markets for commodities. To measure speculative activity, we use a speculation ratio, defined as trading volume divided by open interest. We sequentially apply two time-varying VAR models with stochastic volatility to four heavily traded metal and agricultural contracts to show how the relationship between returns volatility and the speculation ratio evolves over time. Our findings indicate that speculative activity has little to no impact on volatility. On the contrary, for all commodities examined, returns volatility seems to amplify speculation.

KEYWORDS Speculation Ratio, Returns Volatility, Chinese Futures Markets, Commodities, Time-Varying-Approach

1 2 Wellenreuther and Voelzke

1 | INTRODUCTION

Over the past decade, Chinese commodity futures markets have grown extremely rapidly. In the first half of 2016 in particular, the overall market activity exploded and trading volume reached unprecedented dimensions. The trading activity of the steel rebar contract on the Shanghai Futures Exchange (SHFE) impressively illustrates this development. On April 21, 2016, alone, the trading volume of this sole commodity amounted to 24 million contracts, which is equivalent to 240 million tonnes of steel rebar and to around a third of China’s yearly steel production (Home, 2016, July 12). Although, the trading activity on the futures market of Shanghai steel rebar calmed down in 2017, the daily trading volume of this contract remains high. In 2018, the Shanghai steel rebar contract is still the most traded commodity futures contract in the world. The latest annual futures and options volume survey, published by the Futures Industry Association (FIA), shows that Chinese metal futures contracts are not the only Chinese contracts among the world’s most traded futures contracts; Chinese agricultural futures contracts are as well. In fact, 10 out of the top 15 traded futures contracts in the world are currently traded on Chinese exchanges (Acworth, 2018). In addition to the surge in trading volume, commodity futures in China and worldwide have exhibited increasing returns volatility since 2000, with soaring price spikes in 2007 and 2011 and price crashes between mid 2007 and 2008. In line with the ongoing financialization of commodity futures markets, these events have encouraged an extended academic debate as well as a heated public discussion about the role of speculators on futures markets. Many empirical studies have analysed whether or not speculative activity increases volatility on US commodity futures markets. Overall, these studies tend to find stabilizing effects of speculation. Studies by Till (2009) and Sanders et al. (2010) conclude that speculators are not to blame for the excessive price increase in 2007-2008 because the rise in speculation was merely a response to a rise in hedging demand. Miffre and Brooks (2013) also analyse the role of speculators on five metals, five energy futures, four livestock futures, and twelve agricultural futures markets, and they find that there is no significant influence of speculation on volatility. Further using Granger causality tests, Brunetti et al. (2016) investigate 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 conclude that such funds actually stabilize prices by decreasing volatility. The stabilizing hypothesis is quite reasonable and in line with the traditional theory of speculation by Keynes (1923) and Friedman (1953), which indicates that speculation is only profitable when speculators buy when prices are low and sell when prices are high. Thus, speculative activity increases depressed prices, decreases excessive prices, and smoothes the price process. Additionally, speculation provides market liquidity and helps hedgers to find a matching counterparty to transfer risks, and therefore stabilizes prices (Keynes, 1923). All of the studies mentioned above analyze markets in the US, and only little research has been conducted to analyze speculation in commodity futures markets in China. Contrary to markets in the US, Chinese commodity futures markets appear to be characterized by extremely speculative trading behavior, where presumably speculative activity often exceeds hedging demand. Analyses of historical market data for Chinese commodity futures contracts indicate that in the last few years, overall open interest on these markets has surged to a much lesser extent than trading volume. As market trading volume mostly captures speculators’ activity while open interest mirrors hedging activity, this trend most likely implies that short-term speculators traded in these markets rather than hedgers. For this reason and also due to the growing global importance of Chinese commodity futures markets, it is of particular interest to investigate the role of speculation on these markets. Furthermore, most of the previous studies assume the potential effects of speculation on volatility to be constant over time. It seems highly implausible that either the returns volatility or its dependence on speculative activity are constant over a long observation period. Consequently, this paper analyses the time-varying lead-lag relationship between speculation and volatility on four Chinese commodity futures markets, namely the markets for corn, soybean meal, steel rebar and zinc. We investigate whether speculative trading Wellenreuther and Voelzke 3

volume drives returns volatility, or whether returns volatility leads to speculative activity. Since there is currently no data source that provides information about the type of traders (hedgers or speculators) active on Chinese futures markets, we use a ratio that combines trading volume and open interest to capture speculation. This speculation ratio was first proposed by Garcia et al. (1986), and it measures the relative dominance of speculative activity relative to hedging activity on a particular market.1 To analyze the dynamic relationship between the speculation ratio and returns volatility, we sequentially apply two time-varying vector autoregression (VAR) models with stochastic volatility. With the first model we describe the joint dynamics of the four commodity futures returns and provide their volatility time series. The second model estimates the dynamics of the obtained volatility time series and the corresponding speculation ratios and analyzes their dependencies. Our results indicate that speculative activity does not influence the volatility for corn, steel rebar and soybean meal for most of the time period. Only for the zinc contract and solely in the first half of the time span, the results imply that the speculation ratio has a significant but economically marginal influence on volatility. It seems that returns volatility is explained by fundamental supply and demand conditions of the commodity rather than by financial speculation. In fact, for all four contracts we find that volatility amplifies speculative activity for the entire time period. The results imply that increased volatility attracts speculators’ attention and leads to more speculative trading. Spec- ulators use futures markets to gain profits by betting on price changes. The greater the volatility on futures markets, the greater the potential profits for speculators. It is quite likely that short-term speculators in particular, who are assumed to account for a large proportion of traders on Chinese commodity futures markets, prefer relatively volatile futures prices over more stable ones. The remainder of this paper is structured as follows: Section 2 gives a brief overview of Chinese commodity futures markets and summarizes important literature on those markets. Section 3 presents the data used and the econometric methodology employed. Section 4 outlines our empirical results and describes further robustness checks. Section 5 summarizes and concludes.

2 | CHINESECOMMODITYFUTURESMARKETS

Chinese futures markets started in the early 1990s and have exhibited an impressive development since then. Cur- rently, there are four futures exchanges in China: Shanghai Futures Exchange (SHFE), Dalian Commodity Exchange (DCE), Zhengzhou Commodity Exchange (ZCE), and China Financial Futures Exchange (CFFEX). The CFFEX was only established in 2006 and is China’s exchange for ﬁnancial derivatives. The ZCE and the DCE, however, trade in agricultural commodity futures, and the SFE specializes in trading metal and energy futures. Therefore, our analysis concentrates on these latter three exchanges. In 2017, all of the three exchanges were ranked among the top 20 leading derivatives exchanges by the number of contracts traded. With a total trading volume of 1.36 billion futures contracts in 2017, the SHEF ranked 9th, the DCE, with 1.1 billion traded futures contracts, ranked 10th, and the ZCE, with 586 million traded futures contracts, ranked 13th among the largest derivatives exchanges in the world. Several Chinese futures contracts traded on the SHEF, DCE or ZCE are among the most extensively traded contracts worldwide. For example, the SHFE steel rebar futures contract has been traded 702 million times in 2017 and is by far the most active commodity contract in the world. Likewise, various Chinese agricultural commodity futures contracts belong to the world’s leading futures contracts. Of the top 20 traded futures contracts, 14 are traded on Chinese exchanges (Acworth, 2018). Table 1 presents trading volumes of the top 20 commodity futures contracts in 2017.

1Bohl et al. (2017) also use the speculation ratio to measure speculative activity and its inﬂuence on returns volatility in Chinese agricultural commodity futures markets. Their results are inconclusive but indicate that the speculation ratio positively inﬂuences volatility for most of the commodities examined. As Bohl et al. (2017) did not utilize a time-varying approach, this paper can be seen as an extension to their study. 4 Wellenreuther and Voelzke

TA B L E 1 Global Top 20 Commodity Futures Contracts

Contract Volume Jan-Dec 2017

1 Steel Rebar Futures, Shanghai Futures Exchange 702,019,499

2 Brent Oil Futures, Moscow Exchange 451,643,376

3 Iron Ore Futures, Dalian Commodity Exchange 328,743,737

4 WTI Light Sweet Crude Oil (CL) Futures, New York Mercantile Exchange 310,052,767

5 Brent Crude Oil Futures, ICE Futures Europe 241,544,633

6 Soybean Meal Futures, Dalian Commodity Exchange 162,877,864

7 Corn Futures, Dalian Commodity Exchange 127,323,949

8 Henry Hub Natural Gas (NG) Futures, New York Mercantile Exchange 108,391,797

9 Hot Rolled Coil Futures, Shanghai Futures Exchange 103,131,555

10 Bitumen Futures, Shanghai Futures Exchange 97,440,530

11 Zinc Futures, Shanghai Futures Exchange 91,449,266

12 Corn Futures, Chicago Board of Trade 89,876,782

13 Rubber Futures, Shanghai Futures Exchange 89,341,052

14 Rapeseed Meal (RM) Futures, Zhengzhou Commodity Exchange 79,736,545

15 Nickel Futures, Shanghai Futures Exchange 74,154,526

16 Gold (GC) Futures, Commodity Exchange (COMEX) 72,802,171

17 RBD Palm Olein Futures, Dalian Commodity Exchange 68,046,475

18 Aluminum Futures, Shanghai Futures Exchange 65,423,439

19 White Sugar (SR) Futures, Zhengzhou Commodity Exchange 61,073,198

20 Soybean Oil Futures, Dalian Commodity Exchange 57,158,378

This table presents trading volumes for the top 20 global commodity futures contracts in 2017. Data are obtained from FIA 2017 Annual Volume Survey (Acworth, 2018). Wellenreuther and Voelzke 5

In contrast to US futures markets, futures markets in China are relatively young. Hence, the research on those markets is far less extensive. According to Zhao (2015), studies on China’s futures markets mainly concentrate on the price linkages between futures contracts traded on Chinese markets and similar contracts traded on foreign markets (see, e.g., Fung et al., 2003, 2010, 2013; Li and Zhang, 2013; Lee et al., 2013). These studies conclude that prices for Chinese futures are connected to prices for US futures and indicate a continuing improvement in the efficiency of Chinese futures markets. However, only a few studies actually analyse features of trading activities in Chinese futures markets and the interactions with price dynamics (see, e.g., Chan et al., 2004; Chen et al., 2004). One such study by Chan et al. (2004) analyses the daily volatility behaviour in Chinese futures markets for copper, mungbeans, soybeans and wheat, and finds that trading volume, which captures speculation, is positively related to volatility. Conversely, they find that open interest, mainly held by hedgers, negatively affects volatility. Similary, a study by Chen et al. (2004) investigates the relationship between returns and trading volume of Chinese futures contracts for copper, aluminium, soybeans and wheat. The authors observe contemporaneous correlations and lead-lag relationships between trading volume and returns when absolute returns are used. More recently, results by Bohl et al. (2017) also mainly imply that speculative activity, measured by open interest and trading volume data, positively influences returns volatility in Chinese agricultural commodity futures markets. Except for the last three studies mentioned, which indicate that (speculative) trading volume has a destabilizing effect on prices, there is only anecdotal evidence suggesting that futures markets in China are dominated by speculators. Academic research on speculation, however, is primarily focused on US futures markets. This is not just because US futures markets are globally important, but also because the Commodity Futures Trading Commission (CFTC) provides a database that separates hedging from speculative activity. This database is available for several US futures contracts and is often used in empirical studies. Since there is no such a database for trading activity on futures markets in China, empirical literature on speculation in these markets is rare. Therefore, the impressive development of Chinese commodity futures markets is not matched by research on those markets. Against this backdrop, this paper analyzes the role of speculators on Chinese futures markets for commodities.

3 | DATAANDMETHODOLOGY

3.1 | Data

In order to investigate the time-varying nature of speculative activity on Chinese commodity futures markets, we analyze daily data for two metal and two agricultural contracts. In particular, we examine contracts for soybean meal and corn, traded on the Dalian Commodity Exchange (DCE) and contracts for zinc and steel rebar, traded on the Shanghai Futures Exchange. All four contracts are among the worlds’ most heavily traded futures contracts. We obtain time series data of daily settlement prices, trading volume and open interest from Thomson Reuters Datastream for all contracts examined 2. 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 describes the number of contracts outstanding at the end of each trading day. The sample periods extend from March 27th, 2009 to June 20th, 2018

for all contracts. Based on the settlement prices, we calculate log returns (rt = (l n(Pt ) − l n(Pt −1)) × 100) for each of the four commodities. To examine the character of daily trading activities, we compute a ratio that combines daily measures of trading volume and open interest. As proposed by Garcia et al. (1986), the speculation ratio is deﬁned as daily trading volume

2Continuous futures price series are employed. These are calculated by using the price of the nearest contract month as a starting point until the contract reaches its expiry date. Afterwards prices of the next trading contract month are taken. 6 Wellenreuther and Voelzke

(TVt ) divided by end-of-day open interest (OTt ):

Spec TVt R ati ot = . (1) OIt

It therefore measures the relative dominance of speculative activity to the hedging activity in the contract analyzed. While a high speculation ratio indicates high speculative activity compared to hedging activity, a low speculation ratio implies relatively low speculative activity. Therefore, a rise in the speculation ratio mirrors a rise in the dominance of speculators in the market. This speculation ratio reflects the assumption that hedgers hold their positions for longer periods due to their underlying positions, whereas speculators mostly try to avoid holding their positions overnight. Based on their different trading behavior, speculators and hedgers influence the amount of trading volume and open interest in different ways. Speculators mostly affect trading volume instead of open interest, as 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 largely held by hedgers (Rutledge, 1979; Leuthold, 1983; Bessembinder and Seguin, 1993). Noticeably, the ratio’s ability to measure the dominance of speculative activity rests on the assumption that hedgers and speculators hold their trading positions for different time periods. Empirical evidence supports the assumption that hedgers tend to hold their position for longer periods than speculators (Ederington and Lee, 2002; Wiley and Daigler, 1998). We are convinced that especially for Chinese commodity futures markets, the assumptions about the different trading behaviors are realistic. A report published by Citigroup Research, which analyzed the composi- tion of traders active on China’s commodity markets in 2016, indicates that most speculative trades were conducted through high-frequency transactions, with an average tenure of each contract reportedly less than four hours (Liao et al., 2016). As a robustness check, we control for macroeconomic factors that are assumed to influence commodity returns and their volatility. The first control variables are two oil prices, namely the ICE Brent crude oil futures contract as a benchmark for the world oil price and the Dubai Mercantile Exchange (DME) Oman futures contract as a benchmark for the Asian crude oil price. Oil price shocks influence commodity prices in different ways. A surge in oil prices, for example, increases transportation costs, and thus can affect commodity supply. Furthermore, an increase in oil prices may amplify the demand for agricultural commodities used in biofuel production. The forgoing variables also appear in Chen et al. (2010), Ji and Fan (2012) and Nazlioglu and Soytas (2012). Next, following Frankel (2006) and Akram (2009), we add interest rate changes to control for effects of Chinese monetary policy decisions by applying a Chinese 10-year treasury bond futures contract. In line with Tang and Xiong (2012), we apply the MSCI World Index of equity prices as a proxy for world demand and the MSCI Emerging Markets Index as a proxy for the demand in emerging economies such as China, Brazil and Russia. All four time series are obtained from Thomson Reuters Datastream. In our analysis we use log differences of the four macroeconomic control variables. Summary statistics for the speculation ratio, the log returns and the four macroeconomic controls are reported in Table 2. Table 2 displays the mean, maximum, minimum, standard deviation, skewness, kurtosis and the number of obser- vations for all commodities examined. The mean values of the returns are close to zero for all commodity contracts. Skewness and kurtosis parameters indicate that none of the four time series follows a normal distribution. Steel rebar shows the highest mean of the speculation ratio with 1.41. A high speculation ratio indicates relatively high speculative activity in contrast to hedging activity. Corn shows the lowest mean of the speculation ratio with 0.36. Thus, speculative activity is on average relatively low on the corn market. Indicated by the distance of the extreme values (maximum and minimum) as well as by the standard deviation, the speculation ratio for zinc seems to be the most volatile. Figure 1 compares speculation ratios of the four Chinese futures contracts to speculation ratios calculated Wellenreuther and Voelzke 7

TA B L E 2 Summary Statistics

Speculation Ratio

Corn Soybean Meal Steel Rebar Zinc

Mean 0.36 0.79 1.40 1.24

Maximum 3.74 4.30 7.67 7.22

Minimum 0.04 0.03 0.14 0.06

Std. Dev. 0.28 0.54 0.78 0.90

Skewness 3.98 2.11 1.98 1.83

Kurtosis 32.00 8.73 11.04 7.99

Number Obs. 2242 2242 2242 2242

Returns

Corn Soybean Meal Steel Rebar Zinc

Mean 0.00 0.00 0.00 0.00

Maximum 0.12 0.08 0.08 0.05

Minimum -0.16 -0.12 -0.09 -0.05

Std. Dev. 0.01 0.01 0.01 0.01

Skewness -2.67 -0.79 0.12 -0.32

Kurtosis 69.82 14.33 8.50 6.20

Number Obs. 2241 2241 2241 2241

Macro Variables

OIL-ASIA OIL-US MSCI-E MSCI-W T-BOND

Mean 501.04 516.55 6266.44 9678.99 3.58

Maximum 789.07 827.71 8048.35 14283.49 4.71

Minimum 156.46 183.44 3252.30 4710.28 2.66

Std. Dev. 159.24 161.52 835.98 2091.76 0.41

Skewness -0.11 -0.05 -0.65 0.26 0.26

Kurtosis 1.67 1.63 4.09 2.26 3.10

Number Obs. 2470 2470 2470 2470 2470

This table presents summary statistics of the investigated time series of the four commodity futures contracts as well as for the four macroeconomic variables. OIL-ASIA and OIL-US stand for the Asian and the US crude oil price. MSCI-E and MSCI-W represent the MSCI Emerging Markets Index and the MSCI World Index. T-BOND denotes the Chinese 10-year treasury bond futures contract. All data is taken from Thomson Reuters Datastream. 8 Wellenreuther and Voelzke

9 Corn 9 Soybean meal 8 8

7 7

6 6

5 5

4 4 Ratio (TV/OI) Ratio (TV/OI) 3 3

2 2

1 1

0 0 2006 2008 2010 2012 2014 2016 2018 2006 2008 2010 2012 2014 2016 2018

US China US China

9 Steel rebar 9 Zinc 8 8

7 7

6 6

5 5 TV/OI) ( 4 4 Ratio Ratio (TV/OI) 3 3

2 2

1 1

0 0 2006 2008 2010 2012 2014 2016 2018 2006 2008 2010 2012 2014 2016 2018

US China EU China

F I G U R E 1 Speculation Ratios

for equivalent US or European futures contracts. The graphs in Figure 1 clearly show that the speculation ratios of Chinese futures markets are always higher than the ones calculated for US futures markets. This supports the assumption that in contrast to US futures markets, Chinese futures markets are dominated by short-term traders who go in and out of the market during the same day.

3.2 | Methodology

We aim to describe the dynamics of daily commodity futures returns and its interplay with speculation. Therefore, we derive an underlying volatility process and analyze its relationship with speculative activity, proxied by the speculation ratio. It seems highly implausible that either the dynamics of the returns, the volatility, or the volatility’s dependence on speculative activity are constant over a long observation period. Thus, confidence intervals and test results that are built on the assumption of constant parameters are not reliable. Consequently, we use a flexible framework that allows all parameters that govern the dynamics of volatility and speculative activity to vary stochastically over time. In particular, we apply a two-step procedure in which each step implements a time-varying parameter vector autoregressive (TVP-VAR) model with stochastic volatility3. This procedure offers several advantages. First, this method enables us to easily uncover the volatility dynamics and gives a straight forward interpretation using a Bayesian ap-

3The optimal lag numbers for the TVP-VAR models are chosen according to AIC criterion. We apply one lag for all four commodities. Wellenreuther and Voelzke 9

proach. Second, allowing the parameters to change over time may avoid severe statistical mistakes due to ignored structural breaks or changes in the coefficients. Additionally, it enables us to visualize possible changes and to analyze the evolution of the corresponding interdependencies. We obtain this parameter flexibility in a parsimonious way by using a random walk as the prior for the coefficient matrix. This assumption implies undesirable long-run properties. However, the flexibility of a random walk over a finite period of time enables us to capture permanent shifts in the parameters, and it keeps the model simple. A Markov chain Monte Carlo (MCMC) procedure is used to gain smoothed estimates of the latent volatility processes that are based on the entire set of data. According to Primiceri (2005), smoothed estimates are more efficient and particularly suitable when “the objective is an investigation of the true evolution of the unobservable state over time” (Primiceri, 2005). The considered econometric model is of the form

−1 yt = ct + Bt yt −1 + At Σt t (2)

Bt = Bt −1 + νt (3)

αt = αt −1 + ξt (4)

log(σt ) = log(σt −1) + ηt , (5)

with intercept ct and coeﬃcient matrix Bt , both time varying. At is a lower triangular matrix (with ones on the main

diagonal) whose free elements are stacked in αt

 1 0 ... 0      . . . . . a . . .  21,t  At =    . . . . .   . . . 0     an1,t ··· ann−1,t 1  

and Σt as a diagonal matrix with positive elements σt on the diagonal

σ 0 ... 0   1,t     . . .   0 σ . .   2,t  Σt =   .  . . . . .   . . . 0       0 ··· 0 σn,t    To investigate the eﬀect of speculative activity on returns volatility, we provide a two-step procedure using the

above-stated vector autoregressive model in both steps. In the ﬁrst step, yt denotes the multivariate time series 4 p − of logarithmized commodity returns. The approach provides a volatility sample ωi,t := V ar (A 1t Σt t )i,i for each commodity return i at time t . For each commodity, the time-point-wise mean of this sample is an estimator for the

unobserved latent volatility process, denoted by ω¯i,t in the following. ω¯i,t quantiﬁes the degree of variation in returns (volatilities) of the commodity futures examined.

4We use the MCMC approach proposed by Primiceri (2005) and Del Negro and Primiceri (2015), and its implementation given in the R package ’bvarsv’ by Fabian Krueger to derive posteriors of the model parameters given the observed data. We set the thinning parameter to 100, use 50000 MCMC steps after dismissing the first 10000 steps as the burn-in phase for both steps, and set pQ to 400 for the second step, which reduces the prior mean of V ar (νt ) in comparison to the default value. The other (hyper)parameters are set to their default value, leading to an elicitation that implies not flat, but diffuse and uninformative priors. We refer to Primiceri (2005); Del Negro and Primiceri (2015) and to the R package manual for very detailed prior discussions. 10 Wellenreuther and Voelzke

The second step is used to examine whether a systematic relationship exists between the previously estimated volatility process and speculative activity. Therefore, we again use the time-varying VAR model to describe the joint dynamics of the volatility and the speculation ratios and apply equation (2) for each commodity. This time, yt represents a bivariate time series ω¯i,t ri,t , where ω¯i,t describes the estimated volatilities and ri,t the time series of speculation ratios for each commodity 1, ··· , n.

For reasons of clarity, we denote this time series zi,t and add a superindex z to its parameters in the following. z We repeat the previously outlined estimation procedure and implementation and obtain an estimate for the Bt -matrix process, describing how the relationship between the lagged variables and the actual variables evolves over time. Here, we are particularly interested in the coeﬃcients that indicate a Granger causal eﬀect from speculative activity z z on returns volatility, and vice versa, i.e. B1,2,t , respectively B2,1,t .

4 | EMPIRICAL RESULTS

4.1 | Main results

We use the described methodology to investigate the influence of speculative activity and returns volatility for soybean meal, corn, steel rebar and zinc over time. As mentioned above, in the first step of our approach we estimate stochastic volatility for all of the four commodities examined. The dynamics of the estimated stochastic volatility process along with the speculation ratio are visualized in Figure 2. As shown by the graphs, neither the volatilities nor the speculation ratios are constant over time. Rather, the figures imply that both series vary considerably over time, which reinforces the use of the TVP-VAR model with stochastic volatility. Stochastic volatility for the steel rebar contract, for example, seems to be particularly high in 2016. In 2016, the trading volume of the SHFE steel rebar contract increased enormously. For corn, however, the speculation ratio is relatively small and stable over time. Additionally, the graphs indicate a co-movement between the volatility series and the series of the speculation ratios. Nevertheless, no inferences can be drawn about whether the volatility follows speculation or speculation is driven by volatility. Both time series serve as the data source for the second step, which is used to empirically analyze the observed co-movement. In particular, the second step reveals whether this co-movement is driven by a Granger causal effect of speculative activity on returns volatility or vice versa. Granger causality exists when either the coefficients B1,2,t , which describe the effect of speculation on volatility, or the coefficients B2,1,t , which represent the effect of volatility on speculation, are considerably different from zero. The posterior medians of the different critical coefficients are shown in Figure 3. Figure 3(a) displays the effect of the speculation ratio on volatility over time and shows whether the speculation ratio Granger-causes volatility. Figure 3(b) visualizes the effect of volatility on the speculation ratio over time. For corn and steel rebar, we observe no significant influence of the speculation ratio on volatility in the sense of Granger causality for the whole time period. For soybean meal, the speculation ratio’s effect on volatility also appears to be insignificant for most of the time period but becomes significant only after late 2016. Only for zinc, the speculation ratio significantly drives volatility for a longer period of time. The coefficients are significant positive from 2010 to 2015, but the extent of the influence is marginal. As in all cases the coefficients are either insignificant or economically marginal, it does not appear that the speculation ratio has a relevant effect on returns volatility. It rather seems that returns volatility is driven by the fundamental supply and demand conditions of the commodity. On the contrary, we did observe non-zero coefficients when assessing how volatility affects speculative activity, as depicted in Figure 3(b); the results suggest that volatility is driving speculation. The relationship holds for all four Wellenreuther and Voelzke 11

Corn Soybean meal 6 Spec. Ratio Spec. Ratio value Volatility value Volatility 2

3 1

0 0

2010 2012 2014 2016 2018 2010 2012 2014 2016 2018 Date Date

6 6

4 4 Steel rebar Zinc Spec. Ratio Spec. Ratio value Volatility value Volatility

2 2

0 0

2010 2012 2014 2016 2018 2010 2012 2014 2016 2018 Date Date

F I G U R E 2 Speculation Ratio and Volatility

16 12 Wellenreuther and Voelzke

$PSO 4PZCFBONFBM 0.04

0.02

0.03

0.01

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0.00

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0.00

−0.02

2010 2012 2014 2016 2018 2010 2012 2014 2016 2018 %BUF %BUF 4UFFMSFCBS ;JOD 0.015 0.020

0.010 0.015

0.005 0.010

0.000

0.005

−0.005

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−0.010

−0.005 2010 2012 2014 2016 2018 2010 2012 2014 2016 2018 %BUF %BUF

(a) Eﬀect of the speculation ratio on volatility

$PSO 0.20 4PZCFBONFBM

0.10 0.15

0.05 0.10

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2010 2012 2014 2016 2018 2010 2012 2014 2016 2018 'ate 'ate 4UFFMSFCBS ;JOD

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(b) Eﬀect of volatility on the speculation ratio

z F I G U R E 3 Time-varying relationship between volatility and the speculation ratio: B2,1,t posterior median and 95% credibility interval. Wellenreuther and Voelzke 13 commodities examined. The eﬀects are positive for all commodities and for all points in time. The results indicate that when volatility rises, either speculative trading volume increases or hedging activity decreases. As high volatility implies the chance for speculators to gain higher proﬁts, it is likely that a surge in volatility attracts speculators’ attention and increases speculative activity. Particularly in China, where a large proportion of speculation is conducted by high-frequency trades, it is reasonable that those short term speculators might prefer more volatile markets.

4.2 | Robustness checks

We perform two additional robustness checks. The first one addresses the potential influence of macroeconomic fundamental variables that could be erroneously assigned to the stochastic volatility of the commodity returns in our setting. Therefore, we rerun the setting as done before, but we add the fundamental control variables described in Section 3.1 into the first step of the analysis. In particular, we add one fundamental variable at a time into the estimations of the stochastic volatility processes. We use returns on treasury bonds, two oil prices and two stock price indices to control for return changes caused by changes of those fundamental returns. Hence, potential effects on the filtered volatility are ruled out. Afterwards we again implement the second step of the analysis as described in Section 3.2. Table 3 shows results after the first robustness check, summarizing Granger causalities for all commodities and fundamental returns combinations. As shown in Table 3, for most of the commodities and for all fundamental return combinations, the results of the robustness check are in line with our initial findings. For steel rebar and corn the influence of the speculation ratio on volatility is insignificant for the entire time period. The speculation ratio calculated for the zinc contract again significantly affects volatility in the first half of the time period, but after 2015 the influence becomes insignificant. For soybean meal we find phases between 2011 and 2013 as well as after 2017 where the influence of the speculation ratio on volatility is different from zero. However, for zinc and for soybean meal, the extent of the significant positive influences is marginal. Furthermore, the robustness check confirms a significant positive effect from returns volatility on speculative activity for all commodities and all fundamental return combinations. In all cases, the influence of volatility on the speculation ratio is stronger than the impact of the speculation ratio on volatility. Therefore, we conclude that the first robustness check supports our general results, indicating no substantial effect of speculation on volatility but a significant positive effect of volatility on the speculation measure. The second robustness check mitigates criticism on the methodology that a certain volatility trajectory (rather than the whole distribution) has been transmitted from the first step into the second step of the analysis. For this reason, the second robustness check uses the full posterior of the volatility trajectories of the first step, and uses a sample of 100 draws instead of the mean. For each draw we conduct the second step. The merged posterior samples are presented in Figure 4. The results of the second robustness check support our finding that speculators on Chinese futures exchanges are attracted by high volatility. For each of the four commodity contracts we observe a significant positive influence of volatility on the speculation ratio for the whole time period. Only for the corn contract do the results indicate very short periods where the influence is not significantly different from zero. In contrast to our initial results, the influence of the speculation ratio on volatility becomes significantly positive for soybean meal and zinc for the entire time period. For steel rebar the speculation ratio affects volatility only in the first half of the sample, and in the case of corn the influence remains insignificant. Compared to the remaining commodities, the speculation ratio for the corn contract is relatively low and stable. However, the significant impacts of the speculation ratios on volatility, even in times when the speculation ratios are relatively high, are always low. Therefore, we conclude that while there is a clear positive impact of volatility on the speculation ratio, the influence of the speculation ratio on volatility is inconclusive. In some cases the effects of the speculation ratio are significantly positive but the size of the impacts is economically marginal. 14 Wellenreuther and Voelzke

TA B L E 3 Robustness check

Exogenous Variable Speculation → Volatility Volatility ← Speculation

Corn

TBond insigniﬁcant signiﬁcant positive

OIL_Asia insigniﬁcant signiﬁcant positive

OIL_US insigniﬁcant signiﬁcant positive

MSCI_W insigniﬁcant signiﬁcant positive

MSCI_Em insigniﬁcant signiﬁcant positive

Soybean meal

TBond insigniﬁcant signiﬁcant positive

OIL_Asia mainly insigniﬁcant signiﬁcant positive

OIL_US mainly insigniﬁcant signiﬁcant positive

MSCI_W mainly insigniﬁcant signiﬁcant positive

MSCI_Em mainly insigniﬁcant signiﬁcant positive

Steel rebar

TBond insigniﬁcant signiﬁcant positive

OIL_Asia insigniﬁcant signiﬁcant positive

OIL_US insigniﬁcant signiﬁcant positive

MSCI_W insigniﬁcant signiﬁcant positive

MSCI_Em insigniﬁcant signiﬁcant positive

Zinc

TBond insigniﬁcant signiﬁcant positive

OIL_Asia insigniﬁcant signiﬁcant positive

OIL_US insigniﬁcant signiﬁcant positive

MSCI_W insigniﬁcant signiﬁcant positive

MSCI_Em insigniﬁcant signiﬁcant positive

This table summarizes results of the first robustness check. It indicates significance of Granger causality relations between speculation and volatility after controlling for different macroeconomic effects. Wellenreuther and Voelzke 15

$PSO 4PZCFBONFBM

0.06 0.04

0.02 0.04

0.00 0.02

−0.02

2010 2012 2014 2016 2018 2010 2012 2014 2016 2018 EBUF EBUF 4UFFMSFCBS ;JOD

0.03 0.03

0.02

0.01

0.00

0.00 −0.01

2010 2012 2014 2016 2018 2010 2012 2014 2016 2018 EBUF EBUF

(a) Eﬀect of the speculation ratio on volatility

$PSO 4PZCFBONFBM 0.15 0.12

0.08 0.10

0.04

0.05

0.00

0.00 2010 2012 2014 2016 2018 2010 2012 2014 2016 2018 EBUF EBUF 4UFFMSFCBS ;JOD

0.5 0.3

0.4

0.2

0.3

0.1

0.2

2010 2012 2014 2016 2018 2010 2012 2014 2016 2018 EBUF EBUF

(b) Eﬀect of volatility on the speculation ratio

z F I G U R E 4 Robustness check 2: Time-varying relationship between volatility and the speculation ratio: B2,1,t posterior median and 95% credibility interval. 16 Wellenreuther and Voelzke

5 | CONCLUSION

Motivated by a sharp increase in speculative trading volume on Chinese commodity futures markets in the past years, we investigate the dynamic relationship of speculative activity and returns volatility over time. China’s futures markets for commodities have grown rapidly in the last years, and in terms of trading volume, they are already among the most active futures markets in the world. However, the size of Chinese commodity futures markets is not matched by the research on those markets, and empirical studies on speculation are particularly limited. Tothis end, we employ a speculation measure that combines volume and open interest data in a flexible framework to describe and analyze their co-movements with volatility of futures returns in the Chinese futures markets for soybean meal, corn, steel rebar and zinc. As we observe that the speculation ratios and the returns volatilities are not constant over time, we assume their interdependences also to be time varying. Consequently, we sequentially apply two time-varying parameter vector autoregressive (TVP VAR) models with stochastic volatility. This procedure enables us to analyze the dynamic relationship between the speculation ratio and returns volatility and how it evolves over time. With the first model, we describe the joint dynamics of the four commodity futures returns and estimate their volatility time series. The second model is used to estimate the time-varying dynamics of volatility and the speculation ratio and to analyze their dependencies. Our empirical results predominately suggest that there is no or solely a marginal positive effect from speculative activity on the volatility of futures returns. However, we find evidence that an increase in volatility attracts speculators’ attention and amplifies the relative dominance of speculative activity on the markets. As high volatility includes the potential for high profits, it is likely that a surge in volatility attracts speculators’ attention and increases speculative activity. Particularly on Chinese futures markets, where a large proportion of speculation is executed by high-frequency trades, it is reasonable that those short term speculators might prefer more volatile markets.

Acknowledgements

An earlier version of the paper was presented at the Financialization of Commodity Markets Workshop (2017) in Bolzano and at the 11th International Conference on Computational and Financial Econometrics (CFE 2017) in London. The authors are grateful for the comments by the seminar participants. Also, we would like to thank the editor of the Journal of Futures Markets, one anonymous referee, Martin T. Bohl and Pierre L. Siklos for helpful comments and discussions. Wellenreuther and Voelzke 17

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