The roundabout from interest rates to commodity prices in China: The role of money flow

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The roundabout from interest rates to commodity prices in China: The role of money flow☆ ⁎

Zesheng Suna,b, Yaoqing Wangb, , Xu Zhoub, Lunan Yangb a b

School of Finance and Business, Shanghai Normal University, Shanghai, China School of Economics and Management, Zhejiang University of Science and Technology, Hang Zhou, China

A R T I C LE I N FO

A B S T R A C T

Keywords: Commodity price Money flow Interest rate Speculation ARDL model

This paper examines how money flow interacts with interest rates and commodity prices by using China's unique daily money-flow data and autoregressive distributed lag (ARDL) model. It is found that the money flow to the commodity financial market is driven negatively by interest rates. There was a roundabout transmission from international interest rates to market liquidity, then to price movement and then to money flow during the 2008 international financial crisis. Our analysis finds evidence that neither money flow nor market liquidity positively impact commodity prices, which does not support the popular belief that speculation drives up commodity price fluctuations. Holiday effects also positively influence money flow and commodity prices, while weekend effects positively influenced commodity prices only in the aftermath of the 2008 international financial crisis but negatively influenced international interest rates.

1. Introduction The hike in international commodity prices from 2003–2008 and the international financial crisis and market rescue that followed have resulted in significant monetary expansion and drastic commodity price fluctuations. With the rapid development of the financialization of commodity market, the interaction between interest rates and commodity prices and the role of speculation have attracted extensive attention. The prevailing argument is that monetary factors, but not fundamentals of the economy, should be responsible for the fluctuation of commodity prices (Frankel and Rose, 2009). The fact that noncommercial traders represented by hedge funds flocked into the commodity financial market has bolstered the popular viewpoint that speculators destabilize commodity prices (e.g., Singleton, 2014; Janzan et al., 2018); meanwhile, increasing literature found no evidence regarding speculators’ impact on different commodity prices (Buyuksahin and Harris, 2011; Bosch and Pradkhan, 2015; Knittel and Pindyck, 2016). More importantly, speculators must depend on money flow to the commodity financial market to support their position change and to then impact commodity prices. Thus, the roundabout from interest rates to commodity via money flow is key to understanding monetary and speculative factors’ impact on commodity price movement. Early literature asserts that the commodity financial market could

respond quickly to changes in monetary conditions (Frankel, 1986). The subsequent empirical studies do not reach a consensus on the causality between monetary factors and commodity prices by using monthly, quarterly or yearly data. Mork (1989) and Hamilton (2008) support the viewpoint that the escalation of commodity prices increases the demand for money and raises the price level, while other studies reveal that changes in monetary policies significantly impact commodity prices (Ping, 1998; Anzuini et al., 2010; Choi et al., 2014). The above conflicting empirical studies do not consider money flow and the related transmission mechanisms from monetary factors to commodity prices. Meanwhile, weekly or daily financial position data from different traders are used to measure the impact of speculation on commodity prices, and conflicting results are reported by utilizing the Granger causality test or vector autoregressive (VAR) models: speculation's positive impact on prices (Singleton, 2014; Tian and Tan, 2015), high relativity (Tang and Xiong, 2012), or no impact (Bosch and Pradkhan, 2015; Alquist and Gervais, 2011). Fattouh et al. (2013) asserted that it is not appropriate to measure speculation with the traders’ positions and that there are no persuasive definitions of speculation. We make several contributions to the existing literature. First, whereas existing studies examine the relationship between monetary factors and commodities, to our knowledge, we are the first to explore the roundabout from interest rates to commodity prices via money flow

☆

The authors are thankful for the constructive suggestions from two anonymous referees. We are also thankful for the financial support from the National Social Science Fund of China (17BJY012). ⁎ Corresponding author. E-mail addresses: [email protected] (Z. Sun), [email protected] (Y. Wang), [email protected] (X. Zhou), [email protected] (L. Yang). https://doi.org/10.1016/j.resourpol.2018.10.011 Received 22 November 2017; Received in revised form 25 October 2018; Accepted 31 October 2018 0301-4207/ © 2018 Elsevier Ltd. All rights reserved.

Please cite this article as: Sun, Z., Resources Policy, https://doi.org/10.1016/j.resourpol.2018.10.011

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by using China's daily datasets of money flow to the commodity financial market. Second, different from previous studies on speculation's role on commodity prices by utilizing traders’ position data, we analyze whether money flow affects commodity prices, to examine speculation's impact on price in consideration that speculators must adjust money stocks to support their position change. Third, different from existing studies that use the VAR model or Granger causality tests, this paper uses the autoregressive distributed lag (ARDL) model to incorporate the non-same-order stationary properties of different dimensional data and to assess the long-run equilibrium and short-term dynamic relationship among variables. When the long-run equilibrium is hard to identify or does not exist, the Granger causality test is added to judge the lead-lag relationship between variables. The paper is structured as follows. Section 2 provides the economic and academic background. Section 3 describes data and methodology. Sections 4–6 present empirical results of full samples, post-international financial crisis subsamples and the Granger causality test. Section 7 presents the discussion. The last section concludes the paper.

Fig. 2. China's monetary policy from January 2003 to January 2018. Notes: (1) Deposit reserve ratios A and B are the deposit reserve ratio of medium and small financial institutions and large financial institutions, respectively; (2) The right vertical axis denotes loan balance (Loan) and M2. Source: EPS Database

2. Background 2.1. Economic background

July 2011, China was on a journey of tightening monetary policies to fight again against inflation. The deposit-reserve ratio rose to its peak of 21.50% in July 2011. After that, monetary policy has been tuned in small steps. China launched the Shanghai Interbank Offered Rate (Shibor) on September 9, 2006. It is now the only available daily money variable in China. Therefore, we use September 9, 2006, as the starting date of our empirical study. China is one of the world's largest commodity markets; it produces and consumes more than 40% of the world's steel and iron and nonferrous metals.1 More importantly, the case of China is remarkably unique for the following considerations. First, China's commodity financial market is characteristic of highly centralized regulations; only three commodity financial exchanges currently exist, that is, the Shanghai Futures Exchange (SHFE), the Dalian Commodity Exchange (DCE) and the Zhengzhou Commodity Exchange (ZCE). Each futures exchange addresses specific groups of commodities and takes a market monopoly position, which is completely different from commodity financial markets in developed countries. Second, China has its own unique system of futures margin monitoring, that is, the Futures Margin Supervising Center. The Center regulates the investor's money flow to/ from the exchange's settlement account. Such a money flow links the monetary market and the commodity financial market. Finally, China's capital control policy enables our empirical studies to identify and assess the impact of China's money variables on commodity prices more easily. The nonferrous metals, natural rubber and screw-thread steel studied in this paper are traded in SHFE. SHFE is the largest commodity exchange in China for base metals, steel, energy and chemicals. Copper, aluminum and natural rubber were introduced before 2003 and have been in active trading ever since. Screw-thread steel started trading after the international financial crisis and can cover the iron and steel industry. Fuel oil started trading August 25, 2004, but its trade volume has been very limited. There is no other traded energy in the studied period, so we have excluded the energy commodity. Furthermore, the SHFE also releases a nonferrous price index to reflect the price comovement of these commodities.

The international commodity market underwent a process of continued growth between 2003 and 2008 and experienced sharp downturn and volatility in the aftermath of the 2008 international financial crisis (Fig. 1). The West Texas Intermediate (WTI) oil price escalated from USD30/barrel in early 2003 to USD140/barrel in mid-2008. The LME base metal price index rose from 1,100 in early 2003 to approximately 4,000 in 2007–2008. China's copper price hit its peak in 2006–2007. All of the above three prices/indices dropped sharply in the wake of the 2008 international financial crisis. The same trend occurred with screw-thread steel, which started trading in SHFE shortly after the crisis. As the world's major economies such as China and the United States expanded monetary liquidity, commodity prices began to recover in 2009 and have bounced back to historical peaks by mid-2011. Meanwhile, China's monetary policy has experienced sharp changes over the past 15 years, with the most significant changes occurring in 2006–2011 (see Fig. 2). The process started from raising the depositreserve ratio in 18 consecutive rounds to fight again inflation and turned around after the crisis broke out, marked by a lowering of the deposit-reserve ratio and the launch of the RMB4000 billion rescue plan from late 2008 to the beginning of 2009. Then, from the end of 2009 to

Fig. 1. Commodity price movement from January 2003 to March 2018. Notes: (1) The right vertical axis stands for WTI Oil price in USD/barrel; (2) LME Metal is denoted in the price index; (3) SHFE Copper is in 10 Yuan/ton and SHFE Steel in Yuan/ton. Source: WIND Database

1 Source: 2016 China Nonferrous Metals Industry Yearbook; and 2016 China Steel & Iron Industry Yearbook.

2

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interest rates to commodity prices through two channels of interaction. One is the channel of money flow. When an investor perceives disequilibrium of return between the commodity financial market and the monetary market, an incentive to arbitrage causes the money flow to the commodity financial market to support the change of positions in the commodity financial market. Another is the channel of market liquidity. When a disequilibrium of the return rate among financial markets is perceived, liquidity (of position trading) change will interact with commodities’ price movement. The roundabout from interest rates to commodity prices via money flow will give new evidence on speculation's impact on commodity prices.

2.2. Academic background Changes in monetary conditions result in fast and flexible shortterm price changes in the commodity financial market (Pindyck and Rotemberg, 1990; Browne and Cronin, 2007). Frankel's (1986) overshooting model indicates the commodity prices’ excessive adjustment. However, there is no empirical consensus on the causality between monetary factors and the commodity price. Hamilton (1983, 2008) and Mork (1989) support the viewpoint that the escalation of the commodity price increases the demand for money and raises the price level. Most studies found that changes in monetary policies significantly impact commodity prices (Ping, 1998; Frankel and Rose, 2009; Anzuini et al., 2010; Sun and Sun, 2017). The above conflicting empirical studies, to match macro and price data, are based primarily on monthly, quarterly or yearly datasets, with an emphasis on the VAR model. However, the quick response of the commodity price to the external environment makes it difficult for the above studies to capture causalities among data (Schwarz and Szakmary, 1994). The issue of frequency matching between the macro economy and commodity price variation will also arise if weekly or daily data are used. Further, for lack of money flow data, to our knowledge, there are still no studies that focus on the roundabout from monetary factors to commodity prices via money flow. With the increasing financialization of the commodity market, it is popular to attribute drastic fluctuations in commodity prices to financial speculation. Irrational activities of financial traders cause a positive feedback mechanism in the commodity market (De Long et al., 1990; Tokic, 2011; Lammerding et al., 2013); the investors’ trending trades might amplify price fluctuations, thus making it more difficult to identify financial speculation's influence on prices from the causality perspective (Sun and Guang, 2009). Most previous studies measure speculation by means of stocks/positions held by traders of various types on the commodity financial market. However, the answers are conflicting. One answer is that speculation has at least a partial impact on commodity prices, which supports the popular view. By projecting weekly and monthly excess returns on positions in futures contracts, Singleton (2014) asserts that speculative activity may induce prices to drift away from crude oil fundamental values and may result in price booms and busts. Other empirical studies based on monthly data and vector autoregressive (VAR) models tend to support the impact of speculation on commodity prices (Han et al., 2012; Tian and Tan, 2015). Alternatively, speculation partially contributes to short-term commodity price volatility (BeidasStrom and Pescatori, 2014; Janzan et al., 2018). Meanwhile, some other studies stress high relativity between the size and return rate of commodity index funds (Tang and Xiong, 2012; Masters, 2008) and infer the influence of financial speculation on the movement of commodity prices. Another answer denies the impact of speculation on price. Based on daily/weekly data of positions held by different kinds of traders, the Granger causality test found no evidence that positions held by traders of any type had caused changes in commodity prices; to the contrary, changes in commodity prices resulted in changes in the positions of traders (Kilian and Murphy, 2014; Alquist and Gervais, 2011; Buyuksahin and Harris, 2011). The GARCH model estimation finds no evidence that speculators destabilize commodity prices (Kim, 2015; Bosch and Pradkhan, 2015). However, Fattouh et al. (2013) asserted that it is not appropriate to measure speculation with the Commodity Futures Trading Commission (CFTC)’s cumulative positions held by noncommercial traders or hedge funds and that the academic circle had not even formulated persuasive definitions of speculation or excessive speculation. Further studies are needed to incorporate the fast response of the commodity market to monetary factors and find new perspectives on discussing speculation's role. In this paper, by using daily data, we discuss the roundabout from

3. Data and methodology Although China implements capital control, disturbance and risks on the international monetary market still can be transmitted to China by means of expectation and trade. Meanwhile, China's role as the world's second largest economy and one of the most important commodity markets would allow its domestic risk be transmitted to the international market. Therefore, we integrate interest rates from the United States as the international interest rates to explore the interaction among interest rates, money flow and the commodity financial market. 3.1. Data Our empirical data begins on September 9, 2006. Because SHFE only approved providing 5-years-ago data of money flow upon a caseby-case application, the ending date of this study is July 29, 2011, and the study includes 1,174 trading days. This sample period covers the drastic fluctuation in commodity prices and China's monetary policy in 2006–2011, as Figs. 1 and 2 demonstrate. The sample period could be divided into two phases by the 2008 international financial crisis, which was marked by a 5-consecutive-days limit down and trading suspension in SHFE after Chinese National Day 2008. Our studies cover metal copper, metal aluminum, natural rubber, screw-thread steel, and the nonferrous metals price index. The price of a single commodity is drawn from the WIND database, being the settlement price of consecutive contractors (Table A.1). Money flow (input and output data), market liquidity index, and the nonferrous metals price index are from the SHFE. We use the ratio of input/output (Flow) to measure the net money flow to the commodity financial market (Fig. A.1). Market liquidity is defined based on the liquidity Sharpe ratio model as a measure to reflect trading activity and liquidity in the futures market, and the data were first reported in January 2, 2008. We use the Shanghai InterBank Offered Rate (Shibor) to measure changes in China's monetary market conditions. Additionally, the American federal funds interest rate (Effrr) is used to measure the international interest rate. The definition of the above variables and sources of data is reported in Table 1. Based on the descriptive statistics (Table A.1 and Fig. A.1) together with the commodity price movements reported in Fig. 1, it can be witnessed that before the peak of money flow into commodity market (Flow) on August 11 of 2008, commodity prices experienced fluctuating and gradual decreasing from their earlier high level. When China's interest rate (Shibor) reached its maximum in mid September 2007, Flow also took its local maximum value synchronously, which is obvious for other circumstances when Shibor took local maximum value, suggesting that change of interest rate could drive money flow. From August to October of 2008, although commodity prices sharply dropped, Flow did not decline significantly, and Shibor still be higher than 3% until the end of September 2008, indicating the close relationship between money flow and interest rate. However after China's National Day of October 2008, the impact of international financial crisis has been transmitted to Chinese economy and commodity market. For the sake of market rescue, China's expanding monetary policy drove Shibor to less 3

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Table 1 Definition of variables and data sources. Variables

Definition

Sources

Index_SHFE

Nonferrous metals futures price index (01/01/2002 = 1000) Copper settlement price at SHFE Aluminum settlement price at SHFE Natural rubber settlement price at SHFE Screw-thread steel settlement price at SHFE Money input to futures market (input) Money output from futures market (output) Input/output ratio Futures market liquidity index (02/01/2008 = 1000) Shanghai inter-bank offered rate American federal funds interest rate

Shanghai Futures Exchange (SHFE)

Price_Cu Price_Al Price_Nr Price_Br Input Output Flow Liquidity Shibor Effrr

SHFE and WIND Database SHFE and WIND Database SHFE and WIND Database SHFE and WIND Database SHFE SHFE The authors’ calculation SHFE WIND database www.federalreserve.gov/releases/h15/data.htm

It is possible the ARDL model might not identify a relationship, or no cointegration exists in some cases. With reference to the studies from Buyushin and Harris (2011) and others, we will then perform an additional Granger causality test to examine the lead-lag relationship after letting the I(1) variables be stationary.

than 1%, and international interest rate (Effrr) also lowered to similar level. The domestic and international monetary expansion jointly boosted the recovery of Flow and commodity prices, and stimulated the intra-market liquidity (Liquidity) in futures market. This trend maintained until China tightened its monetary supply and pushed up Shibor back to 2% in August 2010. Since then, a different trend of interest rate change emerges: it increased in China while declined on the global market. Meanwhile, money flow fluctuated largely, while commodity prices and Liquidity tends to fall. The above evolution suggests the importance of international financial crisis, and the interaction among variables of Flow, Shibor, Prices, and Effrr, which calls for empirical evidence on their relativity. As suggested by Sun and Sun (2017), we introduce a dummy variable Crisis to incorporate the impact the outbreak of the 2008 international financial crisis on China's commodity market, where we assume trading days from October 6 to October 10, 2008, to be 1 and other trading days to be 0, since there are repeated limit downs and trading suspensions after Chinese National Day 2008. Market information both prior to and after China's public holiday and weekend imposes a significant impact on gains and fluctuations of futures trading (Liu et al., 2012; Li and Xiao, 2015). To control for the impacts of the holiday effect and the weekend effect, we introduce the dummy variable Holiday, assuming the days immediately prior to and after the public holiday to be 1 and other trading days to be 0, and the dummy variable Weekend, assuming the last and first trading days of a week to be 1 and others to be 0.

4. Full sample empirical results We will report the long-run equilibrium relationship separately among Shibor, EFFRR, Flow and the commodity price variable with dummy-excluded and dummy-included models. We first determine whether cointegration exists among these variables with an F test (Table A.3). Only ARDL models with Effrr as the dependent variable show no cointegration with other variables when the dummy is excluded. For dummy-included models, only models with Shibor as the dependent variable indicate an uncertain result regarding the existence of long-term equilibrium. We will report the cases where cointegration exists in the following estimation. 4.1. Full sample cointegration estimation The lag period of various variables should be identified before performing the ARDL model estimation. Schwarz (1978) and Akaike (1973) raise SBC and AIC principles of determination, respectively. Pesaran and Shin (1995) suggest choosing the one with the smaller estimation value as the optimal lag period; the AIC principle should be better if the estimation values are equal. The results show that the lag period under the AIC principle should be the optimal lag period (Table A.4). Table 2 reports the dummy-excluded cointegration estimation results. In the Flow model, the prices of various commodity groups have an insignificant impact on money flow, while two interest rate variables both negatively impact money flow. Specifically, a 1% Shibor and Effrr increase can drive money flow to decrease by approximately 0.05% and 0.09%, respectively. This conclusion agrees with the classical theory of financial market arbitrage. In other words, the equilibrium between commodity assets and monetary assets is broken when the interest rate increases. The higher return rate of monetary assets will drive money to flow out from the commodity financial market. For the Shibor model, neither commodity price nor money flow can cause significant changes in China's interest rate. Although China's capital items are not yet opened, the Effrr changes still positively affect China's interest rate significantly. A 1% Effrr increase will drive China's interest rate up 0.47–0.60%, thus indicating the transmission of international financial market risk to the Chinese market and the correlation of transmarket interest rate changes. Commodity price model shows that money flow has a negative but insignificant impact on commodity prices, demonstrating that the conventional argument that speculation drives the commodity price is

3.2. Methodology To choose an appropriate empirical model, we first perform a unit root test with the Augmented Dickey-Fuller (ADF) test (Table A.2). It is found that all price and market liquidity variables are I(1) processes, while interest rate and money flow variables are I(0) processes. To overcome the dimensional difference among variables, we calculate the natural logarithm of price, money flow and interest rate variables for the empirical study. Because the time series of Liquidity and Price_Br are shorter than that of the full sample, besides full-sample estimation, we also estimate the post-international financial crisis subsample as to make full use of the available data and assess the robustness of fullsample results. Different from the prevailing VAR and GARCH models used in the literature (e.g., Kilian, 2009; Belke et al., 2010), we employ the ARDL (autoregressive distributed lag) model introduced by Pesaran and Shin (1995) and Pesaran et al. (2001) for estimation(Appendix B) to adapt the non-same-order stationary properties of our data and perform a cointegration test. The ARDL model is advantageous in that it can test the long-run equilibrium and short-term dynamic relationship at the same time and indicate the direction of the long-run relationship among variables. 4

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commodity prices negatively except for in the case of metal aluminum but Effrr imposes a significantly negative impact only on aluminum prices. This finding reveals that money and commodities are, to a certain degree, in an equilibrium substitution, which is triggered by changes in returns. However, the results shown in Table 2 indicate that interest rate has no significant impact on commodity prices, meaning that the above equilibrium substitution is weak and that movement of the interest rate reflects the disequilibrium of return on different markets mainly by driving money flow. In addition, the Price model of Table 3 indicates that Flow either insignificantly impacts commodity price in a negative direction in most cases or has a significantly negative impact for only the case of natural rubber. This again gives evidence that speculative trading does not drive up commodity prices. A significant holiday effect could be found, as shown in Table 3. On holiday-related trading days and at the 5% significance level, the Flow model estimation shows that money flow rises above 0.70% and the Price model estimation indicates that holidays cause a significant influence on commodity prices. This means that on all holiday-related trading days, commodity price movement must reflect and balance the uncertain information and anticipated return changes that investors face around holidays. In general, the international financial crisis had a significantly negative impact on commodity prices. This is largely in agreement with our observation that China's commodity prices dropped sharply in October 2008.

Table 2 Full sample cointegration estimation: Dummy-excluded model. Dependent variables Flow model

Price

Index

Index

3.652 (3.999) −3.196 (9.208) 3.817 (3.691) 2.504 (3.233) 21.900 (29.828) −20.458 (118.277) 15.620 (34.133) 3.084 (31.158) -

Al

-

Nr

-

Cu

-

Al Nr Cu Shibor model

Index Al Nr Cu

Price Model

Flow

Shibor

-

−0.053 (0.025) −0.054** (0.025) −0.053** (0.025) −0.053** (0.025) -

1.2712 (0.9768) 1.2522 (0.9668) −0.477 (0.618) 1.270 (0.978) −0.0004 (0.0006) −0.0000 (0.0002) −0.0014 (0.0007) −0.0004 (0.0006)

Effrr **

0.0002 (0.0003) 0.0000 (0.0001) 0.0002 (0.0004) 0.0002 (0.0004)

−0.091*** (0.020) −0.092*** (0.020) −0.091*** (0.020) −0.091*** (0.020) 0.594*** (0.204) 0.5877*** (0.2022) 0.473** (0.210) 0.591*** (0.204) −0.0003 (0.0003) −0.0001 (0.0001) −0.0002 (0.0003) −0.0002 (0.0003)

Notes: (1) **and*** represent 5% and 1% significance level, and the figures in () are standard errors. (2)This table shows the cointegration relationship among money flow(Flow), interest rates(Shibor and Effrr), and commodity prices(Price) with dummies excluded.(3)A significant value of the estimated coefficient means that a long-term equilibrium exists between this variable and dependent variable, and also shows the direction of such impact.

4.2. Full sample ARDL-ECM estimation Tables 4 and 5 report the estimation results based on the Error Correction Model of the ARDL framework (ARDL-ECM). Regardless of the dummy variables, the regression coefficient of the error correction term, ecm (−1), is negative at least at the 5% significance level, indicating that China's interest rate, money flow and commodity prices are all in regression to its long-term equilibrium values after suffering the shock. The Flow model shows that during a short-term adjustment, the Shibor variable at the current term and the first lagged term and the Effrr variable all impose negative impacts on money flow, while commodity prices have no positive impact on money flow. The Shibor model shows that short-term changes and adjustments are subject to the influence of its own lagged items, indicating that what drives Shibor to equilibrium in the short term is not the movement of commodity prices or international interest rates, but instead the fundamentals of the China financial market and macroeconomy as reflected by its own lag items. For the Price model, we find that Effrr plays an important role in addition to the influence of its own lag items, although there is a

not supported at least at the channel of money flow. The empirical findings of Buyusahin and Harris (2011) already demonstrated that changes in positions among different investors on the futures market do not lead commodity prices. By combining the two points above, we can conclude that a hike in commodity prices does not originate from an inflow of speculative capital (Knittel and Pindyck, 2016); it mainly reflects the impact of factors such as industrial growth and other macroeconomic fundamentals (Wang and Wang, 2018). The results of the dummy-included model are reported in Table 3. It is found that Shibor impacts money flow negatively, but insignificantly, except in the case of natural rubber. In contrast, Effrr has a significantly negative impact on money flow, with elasticity close to the estimation results of Table 2. In the price model, Shibor significantly influences Table 3 Full Sample Cointegration Estimation: Dummy-included Model. Dependent variable Flow model

Price

Flow

Shibor

Effrr

Weekend

Holiday

Crisis

-

Index

2.453 (4.234) −1.502 (9.532) −1.185 (3.383) −0.326 (2.515) -

Al

-

Nr

-

Cu

-

−0.128 (0.084) −0.136 (0.084) −0.139* (0.084) −0.135 (0.084) −0.002* (0.0008) −0.0003 (0.0002) −0.002* (0.001) −0.002** (0.0009)

−0.104*** (0.026) −0.1046*** (0.0263) −0.104*** (0.026) −0.101*** (0.027) −0.0005 (0.0003) −0.0002* (0.0001) −0.0004 (0.0003) −0.0004 (0.0003)

−0.072 (0.141) −0.0540 (0.1400) −0.051 (0.141) −0.048 (0.141) 0.001 (0.001) 0.0002 (0.0003) 0.002 (0.001) 0.002 (0.0010)

0.703** (0.326) 0.740** (0.323) 0.744** (0.325) 0.742** (0.325) 0.007*** (0.003) 0.002* (0.0009) 0.012*** (0.003) 0.011*** (0.003)

0.045 (0.697) −0.054 (0.679) −0.083 (0.706) −0.071 (0.689) −0.032*** (0.008) −0.004 (0.003) −0.052*** (0.009) −0.035*** (0.009)

Index Al Nr Cu

Price model

−0.0003 (0.0005) −0.0000 (0.0002) −0.001* (0.0006) −0.0001 (0.0006)

Notes: (1) ** and *** represent 5% and 1% significance level, and the figures in () are standard errors. (2) This table shows the cointegration relationship among variables including dummies like Crisis, Weekend and Holiday. (3) A significant value of the estimated coefficient means that a long-term equilibrium exists between this variable and dependent variable, and also shows the direction of such impact. 5

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Table 4 ARDL-ECM estimation: Dummy-excluded model.

Flow model

Variables

ecm (−1)

dFlow (−1)

dShibor

Index

−0.650 [−18.480] −0.654*** [−18.563] −0.648*** [−18.428] −0.652*** [−18.508] ecm (−1) −0.009** [−2.175] −0.009** [−2.240] −0.009** [−2.195] −0.009** [−2.190] ecm (−1) −0.935*** [−18.129] −0.821*** [−17.649] −0.682*** [−15.912] −0.820*** [−17.609]

−0.110 [−3.802] −0.110*** [−3.774] −0.115*** [−3.961] −0.110*** [−3.777] dShibor (−1) 0.063** [2.137] 0.064** [2.182] 0.063** [2.142] 0.063** [2.143] dFlow

−0.081 [−2.129]

***

Al Nr Cu Shibor model

Variables Index Al Nr Cu

Price model

Variables Index Al Nr Cu

***

dShibor (−1)

dEffrr

−0.081** [−2.140] −0.083** [−2.184] dShibor (−2) −0.063** [−2.131] −0.062** [−2.120] −0.062** [−2.120] −0.063** [−2.126] dEffrr

0.004*** [3.033] −0.0006* [−1.951]

dEffrr (−1) −0.011** [−1.971] −0.003** [−2.355]

dPrice (−1) −4.092*** [−2.716]

***

−0.530* [−1.918]

−0.001** [−2.266]

dPrice

−0.067 [−4.033] −0.070*** [−4.172] −0.067*** [−4.029] −0.069*** [−4.143]

**

−4.029** [−2.411]

−2.803* [−1.674]

dEffrr (−3) 0.018*** [3.063]

dPrice (−1) −0.142*** [−3.319]

0.019*** [3.469]

−0.131*** [−3.492] −0.071* [−1.811]

dPrice (−2) −0.050* [−1.701] −0.085*** [−2.920] −0.089*** [−3.054] −0.058** [−1.980]

Notes: (1) *, ** and *** represent 10%, 5% and 1% significance level, and Figures in [] are t statistics of the estimated coefficient. (2) This table shows the impact of error correction term ecm (−1) and the different lagged term of variables excluding dummies on Flow, Shibor, and Price, respectively. (3) The significant value of the estimated coefficient of ecm (−1) indicates the rate of convergence to equilibrium, and a significant estimated value of the lagged term means that a short-term impact on dependent variable exists.

In the short-term adjustment, the weekend and holiday factors impose a significant negative impact on money flow, implying that investors tend to hold more prudent motives on weekends or holidays to decrease the size of money flow to the commodity financial market. Holiday factors show a positive effect on prices except for aluminum, while weekend factors have a positive effect on aluminum only. This indicates that when market suspension during weekends and holidays increases the risks of uncertainty, there must be a higher commodity price return to compensate for such risks, thus driving the commodity market return to equilibrium.

difference among commodity groups; Flow does not have a significant impact on commodity price in the short term with the exception of its significantly negative impact in the natural rubber market. This is similar to the cointegration estimation results. In the dummy-included Flow model, Effrr imposes a negative shortterm impact on money flow, while Shibor's impact is remarkably weakened. Nevertheless, the commodity prices are not impacted by money flow in the short-term adjustment except for the case of natural rubber. This indicates that the role of money flow is weak in the adjustment of commodity prices. For the case of natural rubber, a significantly negative impact of Flow on the price could be found in the short term.

Table 5 ARDL-ECM estimation results: Dummy-included model.

Flow model

Variable

ecm (−1)

dFlow (−1)

Index

−0.645 [-18.540] −0.646*** [−18.564] −0.644*** [−18.489] −0.643*** [−18.403] ecm (−1) −1.028*** [−23.676] −0.874*** [−29.593] −0.838*** [−29.235] −0.913*** [−31.391]

−0.122 [−4.187] −0.122*** [−4.199] −0.124*** [−4.259] −0.124*** [−4.246] dFlow

Al Nr Cu Price Model

Variable Index Al Nr Cu

***

dShibor

***

dEffrr

dPrice

−0.067 [−3.835] −0.068*** [−3.877] −0.067*** [−3.851] −0.065*** [−3.722] dShibor (−1) ***

−0.090* [−1.654]

dShibor −0.002* [−1.799]

dWeekend

dWeekend (−1)

dHoliday (−1)

−3.467 [−2.295]

−0.183 [−3.005] −0.177*** [−2.903] −0.176*** [−2.887] −0.174*** [−2.849] dWeekend

−0.144 [−2.352] −0.154** [−2.518] -0.150** [−2.454] -0.155** [−2.522] dHoliday 0.007*** [2.680]

−0.421*** [−2.736] −0.440*** [−2.860] −0.435*** [−2.834] −0.439*** [−2.853] dCrisis −0.058*** [−4.303] −0.019*** [−5.548] −0.044*** [−5.923] −0.053*** [−4.269]

**

−3.189* [−1.893]

dEffrr

0.005*** [3.256] −0.0008* [−1.665]

dPrice (−1)

0.009* [1.795] 0.010* [1.929]

dPrice (−1) −0.072** [−2.448]

***

**

0.0006** [2.452] 0.010*** [4.182] 0.010*** [3.746]

dCrisis (−1)

0.010*** [2.849]

Notes: (1) *, ** and *** represent 10%, 5% and 1% significance level, and Figures in [] are t statistics of the estimated coefficient. (2) This table shows the impact of error correction term ecm (−1) and the different lagged term of variables including dummies on Flow, Shibor, and Price, respectively. (3) The significant value of the estimated coefficient of ecm (−1) indicates the rate of convergence to equilibrium, and a significant estimated value of the lagged term means that a short-term impact on dependent variable exists. 6

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5. Post-international financial crisis subsample estimation

commodity prices positively. This is consistent with the results of the full-sample estimation.

Since Liquidity data starts from January 2008, to include the Liquidity variable into the model and concurrently to test the robustness of full-sample empirical results, in this section we perform post-international financial crisis subsample empirical studies. The subsample estimation results of the F test shows that the Shibor models do not have a cointegration relationship; the Liquidity model also shows an absence of cointegration in most dummy-excluded cases (Table A.5). The selection of the lag period demonstrates that the lag period under the AIC principle is optimal.2

5.2. Subsample ARDL-ECM estimation In the Liquidity model of aluminum (Table A.7), Shibor and Effrr all impose a negative influence on Liquidity in the short run, demonstrating the equilibrium relationship between monetary assets and commodity assets. Similarly, through the flow channel, Shibor's negative impact on Flow is found again. Meanwhile, Liquidity influences Effrr in the short run similar to the long-run equilibrium estimation results. However, the flow channel has a less significant impact on Effrr in the short run. A two-way influence can be found between Liquidity and prices. Liquidity can drive up commodity prices in the current period but demonstrates a negative influence on certain commodities in the following periods; the hike in commodity prices raises market liquidity in the current period but then has a negative influence in the following periods. However, in the flow channel, prices do not impact Flow significantly in the short term in most cases; conversely, Flow has no significant influence on commodity prices, which is consistent with the cointegration results. Subsample ARDL-ECM estimation results with dummy variables are reported in Table 7. In general, the significantly negative impact of the interest rate on the stock and flow channels in the short term is proved again, and the two-way positive impact between prices and the stock channel in the current period is also affirmed. Similar to the estimation in Table A.7, no significant one-way/two-way relationship exists between prices and the flow channel. As for dummy variables, the holiday effect exists negatively on the stock channel and positively on the flow channel. This indicates that when a holiday draws near and uncertainty rises, investors tend to minimize their clearance risks conservatively by reducing transactions, increasing money inflow in the current period and decreasing money inflow in the lagged period. Such a risk aversion motive is also demonstrated in the weekend effect, which is significantly negative on Flow and Effrr and positive on prices in most cases. Crisis creates a very significantly negative impact on Liquidity in the short term, demonstrating that the drastic market risks following the 2008 international financial crisis have turned into strong motives for prudent trading, which then drives investors to reduce trade sharply in the short term.

5.1. Subsample cointegration relationship estimation For the dummy-excluded aluminum model, Liquidity is negatively affected by Effrr, demonstrating that the risk input from the international market imposes a significant impact on the investors’ sentiment and willingness to trade (Table A.6). Shibor negatively impacted money flow in the Flow model, similar to the full-sample model estimation. This again is evidence of the equilibrium relationship between holding money assets and commodity assets, i.e., the rise of the interest rate weakens investors’ motivation to hold commodity assets and thus decreases money flow to the commodity financial market. There is only partial evidence that commodity prices impact the two channel variables of Flow and Liquidity. Although the metal price index and copper price positively drive up money flow, such impact disappears for the case of aluminum and natural rubber. Meanwhile, aluminum price does not significantly impact Liquidity. Additionally, the two channel variables have no significantly positive effects on prices. A two-way negative relationship could be found between the variables Effrr and Liquidity. In consideration of China's increasingly important role in the global macroeconomy after the 2008 international financial crisis, the signals of Chinese market risks and international market risks interact through the Liquidity channel of China's commodity financial market. In the Price model, it again is found that Effrr has a significant negative influence on commodity prices; Shibor's impact is negative but insignificant in most cases. Compared to the significant results in fullsample models, in the wake of the 2008 international financial crisis, monetary market disturbance and risks originate more from the international market than China's market. Flow does not impact commodity prices significantly, supporting the results from full-sample models. Meanwhile, the impact of Liquidity on commodity prices is negative and significant except for natural rubber. Due to investors’ prudent motives and pessimistic moods in the aftermath of the 2008 international financial crisis (Sun and Sun, 2017), the rise in market liquidity does not drive up, but rather inhibits commodity prices. We now include dummy variables in the analysis (Table 6). Effrr again has a significant negative impact on Liquidity and prices; Flow does not impact prices significantly; Liquidity significantly influences Effrr and prices negatively. The influences of interest rates and price variables are differentiated. The metal price index and copper price exerts a stronger influence on Flow; Shibor imposes a significant impact on Flow in natural rubber and metal aluminum, which is consistent with the results of dummy-excluded model. Liquidity has no significant reaction on dummy variables and responds only to international interest rate movement. Holiday's positive impacts on Flow and prices again are found. Weekend significantly influences Effrr negatively, except for natural rubber, and impacts

6. Granger causality testing results Because there are cases that ARDL model estimation cannot identify or for which it shows no cointegration relationship; screw-thread steel started trading on March 27, 2009, and the data duration limits us in performing ARDL estimation, we employ the Granger causality test in this section to do a supplementary analysis according to Buyusahin and Harris (2011). After stabilized variables are obtained by calculating the first difference for all money and price variables with the dummies being exogenous, we establish the VAR model to perform the Granger causality test. We report the estimation results for the 2nd to 5th lag periods based on the AIC principle and the SBC principle. 6.1. Full-sample Granger causality test As Table 8 shows, the continuous one-way Granger causality from price to money flow is only demonstrated the case of the metal index, and no other lead–lag correlation from price to money flow is found. Conversely, except for aluminum at the 5th lag period, we cannot find one-way Granger causality from money flow to price. The aforementioned ARDL modeling estimations have indicated that money flow has no long-term impact on commodity prices. Therefore, the Granger causality test gives further evidence that money flow does not influence commodity prices, and the impact of commodity price on money flow is unimportant. One-way Granger causality from Shibor to Flow is also

2 The selection of the lag period for the subsample ARDL model again shows that the estimation under the AIC principle is optimal. We do not report the estimation results.

7

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Table 6 Subsample cointegration estimation: Dummy-included model. Dependent variables Liquidity model

Price Index

Index

146.515 (128.522) 394.402 (347.556) 172.217 (149.961) 145.142 (124.469) 10.964*** (4.057) 3.816 (8.496) −1.380 (2.529) 8.466*** (3.209) 8.875 (6.967) 116.743* (62.633) 1.371 (7.091) −7.776 (8.005) -

Al

-

Nr

-

Cu

-

Al Nr Cu Flow model

Index Al Nr Cu

Effrr model

Index Al Nr Cu

Price model

Flow 3.640 (3.209) 3.422 (2.792) 3.237 (2.661) 3.408 (2.925) 0.180 (0.166) 0.202 (0.187) 0.184 (0.166) 0.196 (0.153) −0.0001 (0.0009) −0.0001 (0.0003) 0.0002 (0.001) −0.0007 (0.001)

Liquidity

Shibor

Effrr

-

−1.023 (2.130) −1.117 (1.962) −0.965 (1.911) −1.027 (2.043) −0.131 (0.084) −0.153** (0.085) −0.163** (0.085) −0.137 (0.085) −0.268 (0.246) −0.284 (0.274) −0.299 (0.248) −0.273 (0.224) −0.002* (0.001) −0.0002 (0.0004) −0.003 (0.002) −0.002 (0.002)

−3.258 (0.870) −3.198*** (0.771) −3.051*** (0.732) −3.184*** (0.809) −0.039 (0.102) −0.096 (0.102) −0.105 (0.101) −0.044 (0.101) -

−0.007 (0.037) −0.025 (0.037) −0.028 (0.037) −0.011 (0.0366) −0.289*** (0.035) −0.284*** (0.040) −0.287*** (0.036) −0.289*** (0.032) −0.002*** (0.0005) −0.0005*** (0.0002) −0.002** (0.0006) −0.0008 (0.0007)

***

−0.005*** (0.002) −0.002*** (0.0005) −0.005* (0.002) −0.003** (0.002)

Weekend

Holiday

Crisis

−0.039 (1.845) 0.314 (1.653) 0.534 (1.651) 0.303 (1.743) 0.246 (0.197) 0.267 (0.201) 0.278 (0.201) 0.296 (0.199) −0.561** (0.283) −0.657** (0.333) −0.552 (0.280) −0.513** (0.251) 0.003** (0.001) 0.0008** (0.0004) 0.003 (0.002) 0.0002* (0.002)

−9.086 (8.380) −7.847 (6.904) −8.457 (7.262) −9.059 (8.045) 0.751** (0.312) 0.910*** (0.316) 0.957*** (0.317) 0.779** (0.313) −0.562 (0.599) −0.691 (0.678) −0.549** (0.605) −0.410 (0.542) 0.010*** (0.003) 0.002** (0.001) 0.017*** (0.005) 0.016*** (0.004)

−40.832 (38.757) −54.487 (47.619) −42.510 (39.661) −44.861 (43.460) −1.036 (1.075) −0.462 (0.664) −1.146 (1.093) −0.305 (0.656) −10.225** (4.621) −13.639** (6.075) −10.970** (4.816) −9.621** (4.095) −0.015 (0.011) 0.021*** (0.006) −0.044*** (0.020) −0.043*** (0.013)

Notes: (1) ** and *** represent 5% and 1% significance level, and the figures in () are standard errors. (2) This table shows the subsample cointegration relationship among variables including dummies. (3) A significant value of the estimated coefficient means that this variable has a significant impact on dependent variable.

is consistent with the cointegration estimation of the ARDL model. It is found that Effrr leads Flow one way except for in the case of natural rubber, so in the wake of the international financial crisis, money flow is driven more by factors such as international market conditions and macroeconomic fundamentals. In the cases of highly internationalized commodities such as copper and the metal index, Effrr significantly leads commodity prices. However, due to China's role in the global commodity market, its money flow and commodity prices movement can lead the international interest rate in the cases of most commodities. This is similar to the results of the full-sample estimation. In summary, the above two-way Granger causality demonstrates the importance of risk information transmission among markets in the wake of the 2008 international financial crisis.

proved for all commodity groups that were studied. It is worth noting that both Flow and prices constitute a Granger cause for Effrr, but no Granger causality is observed between Shibor and Effrr (Table 9). This might be because money flow and commodity prices are reflected as China's market information, which affects international market and leads changes in the international interest rate. Although existence of cointegration is found by the ARDL model between Shibor and Effrr, quick arbitrage between China and international financial markets is difficult in the short term due to China's capital control. There is difference in the Granger causality from price to Shibor/ Effrr for different commodities. A two-way Granger causality between price and Effrr exists in cases of the metal index and copper market, where copper in China is highly dependent on overseas sources, high international integration and pricing capabilities (Li and Zhang, 2013); copper covers a high percentage in the SHFE's metal index. Nevertheless, a different story occurs for aluminum and natural rubber. Aluminum is subject more to the impact of China's excessive capability and is not sensitive to changes in the interest rate (Sun et al., 2013). The prices of natural rubber can act on both the China and the international interest rate and is reflected on the prospect of China's macroeconomic situation to a higher extent.

7. Discussion We use China's unique data of money flow on the commodity financial market, the ARDL model, and the Granger causality test to perform empirical studies on the roundabout from interest rates to commodity prices via money flow. Major empirical results are presented in Figs. 3 and 4. In case the direction of influence is not significant for all commodities, we mark partial impact (P) for those cases where at least two of five commodities are significant. In the Granger causality test results, we also highlight the lag periods involving significant influences.

6.2. Subsample Granger causality test Similar to the results of the full-sample test, no solid support for Granger causality between commodity prices and money flow is found except in the case of the metal index, indicating again that speculative money inflow does not drive up commodity prices (Table A.8), and only money flow leads market liquidity. Therefore, neither Flow nor Liquidity is the Granger cause of commodity prices. In contrast, some evidence indicates that commodity prices lead market liquidity (Table A.9). This

7.1. Roundabout transmission from interest rate to commodity price Compared with the existing literature that stresses the impact of monetary market movement on commodity prices (e.g., Frankel, 1986), our paper emphasizes the transmission mechanism from monetary market to commodity price by introducing money flow and market 8

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Table 7 Subsample ARDL-ECM estimation: Dummy-included model. Liquidity model Variables

ecm (−1)

dLiquidity (−1)

dShibor

dEffrr

dPrice

Index

−0.002 [−1.287] −0.002 [−1.414] −0.002 [−1.341] −0.002 [−1.419]

0.543*** [17.241] 0.550*** [17.577] 0.551*** [17.557] 0.550*** [17.548]

−0.031* [−1.768] −0.031* [−1.744] −0.0323* [−1.83] −0.031* [−1.764]

−0.034** [−2.041] −0.047** [−2.766] −0.0423** [−2.51] −0.043** [−2.534]

0.294*** [3.059] 0.864** [2.278] 0.286** [2.557] 0.301*** [2.920]

ecm (−1) −0.673*** [−14.566] −0.663*** [−14.289] −0.667*** [−14.330]

dFlow (−1) −0.123*** [−3.227] −0.126*** [−3.291] −0.123*** [−3.194]

dShibor −0.686** [−2.514] −0.668** [−2.434] −0.651** [−2.364]

dPrice (−1) −5.152*** [−3.538]

dWeekend (−1) −0.211** [−2.765] −0.223*** [−2.936] −0.226***

−0.668*** [−14.498]

−0.125*** [−3.297]

−0.656** [−2.404]

ecm (−1) −0.035*** [−3.515] −0.031*** [−3.128] −0.035*** [−3.518] −0.039*** [−3.834] Price model ecm (−1) −1.118*** [−29.689] −0.884*** [−23.594] −0.802*** [−21.714] −0.886*** [−23.171]

dLiquidity −0.220** [−2.529] −0.232** [−2.699] −0.210** [−2.418] −0.210** [−2.419]

dLiquidity (−1) 0.184** [2.154] 0.198** [2.359] 0.163* [1.900] 0.183** [2.143]

dPrice /

dPrice (−1) /

3.205*** [3.772]

−1.861** [−2.234]

dLiquidity 0.047*** [3.269] 0.008** [2.036] 0.035** [2.698] 0.043*** [3.014]

dLiquidity (−1) −0.048*** [−3.407] −0.010** [−2.565] −0.038*** [−2.979] −0.039*** [−2.835]

dEffrr

dEffrr (−1)

Al Nr Cu

dPrice (−1)

−0.215** [−1.963] /

dHoliday

dCrisis

dCrisis (−1)

−0.018** [−2.000] −0.017* [−1.902] −0.042** [−2.514] −0.019** [−2.073]

−0.512*** [−9.327] −0.541*** [−8.230] −0.538*** [−8.176] −0.523*** [−7.912]

−0.107** [−2.237] −0.106** [−2.217] −0.118** [−2.465] −0.109** [−2.285]

Flow model Index Al Nr

Cu

[−2.949] −0.240*** [−3.157]

dHoliday 0.322** [2.110] 0.340** [2.224] 0.357** [2.317]

dHoliday (−1) −0.457*** [−2.985] −0.503*** [−3.271] −0.513*** [−3.334]

0.325** [2.133]

−0.478*** [−3.123]

dWeekend −0.020** [−2.384] −0.021** [−2.513] −0.020** [−2.357] −0.020** [−2.395]

/

/

/

dWeekend 0.003** [1.994] 0.0007** [2.03]

dHoliday 0.011*** [3.021] 0.002** [2.306] 0.013*** [4.143] 0.009*** [2.627]

dCrisis

dCrisis (−1)

−0.050** [−1.967]

0.009* [1.939] 0.034** [2.053] 0.042** [2.320]

Effrr model Index Al Nr Cu

Index Al Nr Cu

0.006*** [3.733] −0.003* [−1.816] −0.011* [−1.778]

0.002* [1.674]

Notes: (1) *, ** and *** represent 10%, 5% and 1% significance level, and Figures in [] are t statistics of the estimated coefficient. (2) This subsample result table shows the impact of error correction term ecm (−1) and the different lagged term of variables including dummies on Flow, Shibor, and Price, respectively. (3) The significant value of the estimated coefficient of ecm (−1) indicates the rate of convergence to equilibrium, and a significant estimated value of the lagged term means that a short-term impact on dependent variable exists.

information flow exist for copper futures traded on the SHFE, London Metals Exchange (LME) and New York Commodity Exchange (COMEX). Importantly, copper occupies the highest weight in the SHFE's metals index. Therefore, the roundabout transmission reflected in copper and metal index is in line with the investors’ choice. Furthermore, we observe that commodity prices may not necessarily influence money flow in the long run. In the full-sample case, prices do not influence money flow; only certain commodity prices have a significant impact on money flow in the post-international financial crisis subsample case. The reason may be that, when compared to the size of the monetary market and financial market, the price movement of a single commodity causes very weak influences on the equilibrium between monetary assets and commodity assets.

liquidity. Robust evidence is found that money flow is negatively driven by interest rate, that is, when the interest rate changes, the prior balance of return rate between monetary assets and commodity assets is broken; an increase in the interest rate gives investors stronger motivation to hold monetary assets, causing the money to flow out from the commodity financial market. What is worth noting is that although the international interest rate does not impact money flow significantly in the subsample modeling, it does drive money flow indirectly through a roundabout transmission channel. The risk of the 2008 international financial crisis lowered international interest rate as a means of rescue in the aftermath of the crisis and has impacted commodity price and the stock channel of the commodity financial market. These influences first raise market liquidity, then drive the movement of commodity prices, and finally drive money flow to the commodity financial market. Nevertheless, such a roundabout transmission from the international interest rate to money flow occurs for copper and the metal index only, based on the ARDL model and the Granger causality test. Li and Zhang (2013) and Kang et al. (2018) note that for China, copper is highly dependent on imports with a high degree of financialization and integration among the international market. Rutledge et al. (2013) emphasizes that long-term equilibrium and effective transmarket

7.2. The role of money flow and speculation Since speculators must rely on the money flow on the commodity financial market to support position changes, if we argue that speculation can impact commodity prices, money flow should positively impact prices. However, our empirical study, based on the ARDL model and Granger causality test, gives evidence that money flow does not impact commodity prices. Such a result is robust for the full-sample, 9

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Table 8 Full-sample Granger causality test: The role of money flow. Δ Day

Δ Price→ ΔFlow

Nonferrous metals price index 2 11.16*** (0.004) 3 9.64** (0.022) 4 10.43** (0.034) 5 11.59** (0.041) Metal copper 2 5.51 (0.064) 3 5.09 (0.166) 4 6.03 (0.197) 5 6.59 (0.253) Metal aluminum 2 3.11 (0.211) 3 2.62 (0.455) 4 3.79 (0.435) 5 4.33 (0.503) Natural rubber 2 3.44 (0.179) 3 4.26 (0.235) 4 8.59 (0.072) 5 7.69 (0.174)

ΔFlow→ Δ Price

Δ Shibor→ ΔFlow

ΔFlow→ ΔShibor

Δ Effrr → ΔFlow

ΔFlow→ Δ Effrr

2.53 3.02 3.60 3.56

(0.282) (0.389) (0.463) (0.615)

0.82 (0.662) 5.67 (0.129) 10.82** (0.029) 11.10** (0.049)

1.42 1.93 3.66 3.83

(0.493) (0.586) (0.455) (0.573)

0.38 1.03 2.72 7.29

(0.826) (0.794) (0.606) (0.200)

1.64 (0.441) 3.67 (0.300) 11.51** (0.021) 12.12** (0.033)

3.65 3.76 3.67 3.94

(0.161) (0.288) (0.452) (0.557)

0.83 (0.660) 5.72 (0.126) 11.14** (0.025) 11.32** (0.045)

1.71 2.22 4.05 4.28

(0.424) (0.529) (0.399) (0.510)

0.39 1.07 2.75 7.02

(0.823) (0.785) (0.601) (0.219)

1.49 (0.474) 3.39 (0.335) 11.08** (0.026) 11.68** (0.039)

2.22 (0.329) 2.57 (0.462) 4.17 (0.3840 12.39** (0.030)

0.94 (0.627) 5.76 (0.124) 11.60** (0.021) 11.96** (0.035)

1.72 2.49 4.52 4.57

(0.423) (0.478) (0.341) (0.470)

0.31 0.94 2.88 6.46

(0.856) (0.817) (0.578) (0.264)

1.69 (0.430) 3.67 (0.300) 11.57** (0.021) 12.05** (0.034)

5.05 5.63 7.28 8.21

0.85 (0.653) 5.52 (0.137) 11.06** (0.026) 11.29** (0.046)

1.99 2.37 4.85 4.50

(0.370) (0.499) (0.303) (0.479)

0.46 1.28 3.47 6.81

(0.793) (0.735) (0.482) (0.235)

1.42 (0.491) 3.11 (0.375) 10.52** (0.033) 11.05** (0.050)

(0.080) (0.131) (0.122) (0.145)

Notes: (1) **and*** represent 5% and 1% significance level, and Figures in () indicate the probability of Chi-sq testing. (2)This table shows the Granger causality testing result among variables, where a significant value of the estimated coefficient means that there exists a lead-lag relationship between the two variables.

positions rely on CFTC's data, but such data themselves have a problem of classification, making it hard to define what is speculation or excessive speculation. The structural vector autoregressive approach uses data from long intervals, e.g., monthly, but use of long-interval data may reduce the probability of identifying causalities among data (Schwarz and Szakmary, 1994). This paper contributes to the literature by introducing an alternative method for judging the role of speculators based on the logic that any position changes would bring about money demand and money flow. Compared to the conflicting results on speculators’ roles, our paper presents firm evidence that speculation cannot drive up commodity prices through the money flow channel. Meanwhile, the subsample estimation results demonstrate a significant negative impact of market liquidity on commodity prices for most groups. Although limited by data availability, we are unable to explore the relationship between them before the 2008 international

subsample cointegration and ARDL-ECM estimation, so this paper does not support the popular viewpoint that speculators destabilize commodity prices. Prior literature on the relationship between speculation and commodity prices generates conflicting results by using different methodology, including the return rate relativity method (Masters, 2008; Tang and Xiong, 2012), Granger causality test based on trader's position (Buyuksahin and Harris, 2011; Sanders and Irwin, 2011), speculation index method (Working, 1960) and structural vector autoregressive model method (Kilian, 2009; Kilian and Murphy, 2014; Han and Yin, 2012). Most of these approaches are criticized by Fattouh et al. (2013). The literature on the return rate relativity method notes the correlation among commodity prices, asset price, and growth of commodity index funds, but the reason for the increase and the nature of the correlation are not clear. The speculation index or studies based on traders’ Table 9 Full-Sample Granger Causality Test: Price and Interest Rate. Δ Day

Δ Effrr → Δ Price

Nonferrous metals prince index 2 0.29 (0.865) 3 0.34 (0.952) 4 10.57** (0.032) 5 11.21 (0.070) Metal copper 2 0.93 (0.629) 3 0.82 (0.844) 4 11.96** (0.018) 5 12.14** (0.033) Metal aluminum 2 3.09 (0.213) 3 3.57 (0.312) 4 6.16 (0.187) 5 6.33 (0.275) Natural rubber 2 0.31 (0.857) 3 1.01 (0.799) 4 1.50 (0.826) 5 1.62 (0.898)

Δ Price→ ΔEffrr

Δ Shibor→ Δ Price

Δ Price→ ΔShibor

ΔShibor→ ΔEffrr

ΔEffrr→ ΔShibor

6.74** (0.035) 7.95** (0.047) 11.57** (0.021) 11.82** (0.037)

0.71 1.37 1.55 1.64

(0.701) (0.712) (0.818) (0.897)

2.95 3.01 4.07 4.07

(0.229) (0.390) (0.397) (0.539)

2.96 3.48 4.29 7.04

(0.228) (0.324) (0.368) (0.218)

0.082 (0.960) 0.21 (0.977) 0.45 (0.979) 1.41 (0.923)

2.76 (0.251) 8.16** (0.043) 11.74** (0.019) 12.00** (0.035)

0.67 1.03 1.31 1.35

(0.717) (0.795) (0.860) (0.930)

2.08 2.35 2.86 2.97

(0.353) (0.503) (0.581) (0.704)

3.03 3.67 4.39 7.21

(0.220) (0.300) (0.356) (0.206)

0.075 (0.963) 0.20 (0.978) 0.44 (0.979) 1.58 (0.904)

0.13 5.20 8.64 8.35

0.086 (0.958) 0.47 (0.926) 0.65 (0.957) 0.38 (0.996)

2.51 2.62 3.11 3.66

(0.285) (0.454) (0.540) (0.600)

2.82 3.50 4.04 7.02

(0.244) (0.321) (0.401) (0.220)

0.072 (0.965) 0.11 (0.991) 0.43 (0.980) 1.39 (0.925)

1.01 3.07 3.17 3.66

7.71** (0.021) 7.97** (0.047) 10.15** (0.038) 11.60** (0.041)

3.02 3.89 4.77 8.03

(0.221) (0.274) (0.312) (0.154)

0.16 0.26 0.53 1.77

(0.939) (0.158) (0.071) (0.138)

15.17*** 32.37*** 31.08*** 32.30***

(0.001) (0.000) (0.000) (0.000)

(0.604) (0.381) (0.530) (0.599)

(0.923) (0.968) (0.970) (0.880)

Notes: (1) ** and *** represent 5% and 1% significance level, and Figures in () indicate the probability of Chi-sq testing. (2) This table shows the Granger causality testing result among variables Shibor, Effrr and Price. A significant value of the estimated coefficient means that there exists a lead-lag relationship between the two variables. 10

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have a much stronger impact on commodity prices and money flow than do the weekend effects. Furthermore, we find that weekend effects occur on the international interest rate in the subsample case and lower the interest rate by 0.5–0.7%. Gurrola and Herrerias (2011) reports the existence of weekend effects in the Mexican interest rate futures market, but to our knowledge, limited studies discuss the weekend effects of the interest rate itself and we contribute to this discussion. 8. Conclusions Based on China's daily money flow data to the commodity financial market, this paper empirically explores the roundabout from interest rates to commodity prices and gives new evidence regarding speculators’ roles on the commodity market. By using money flow as the flow channel and market liquidity as the stock channel, we use the ARDL model to explore the cointegration and short-term dynamic relationship between variables, with an emphasis of factors of the 2008 international financial crisis, weekends and public holidays. Where the ARDL model cannot identify a relationship or a cointegration relationship doesn’t exist, we perform Granger causality tests to provide supplementary evidence. Firstly, it is found that the money flow to the commodity financial market is negatively driven by interest rates, as a rise in the interest rate drives investors to withdraw their capital from the commodity financial market. There is a roundabout transmission from the international interest rate to market liquidity, to price movement and then to money flow around the 2008 international financial crisis. Such transmission proceeds mainly through commodity groups of a higher degree of internationalization. Secondly, neither the flow channel measured with money flow nor the stock channel measured with market liquidity demonstrates a significant positive influence on commodity prices. Specifically, it is highly robust that money flow does not influence changes in commodity prices; a positive influence on commodity prices is also not observed on market liquidity. The Granger causality test gives evidence only that the price of certain commodity groups can induce changes in market liquidity. Our analysis therefore does not support the popular belief that speculation drives up commodity price fluctuation. Finally, holiday effects positively influence money flow and commodity price, while weekend effects positively influence commodity price only in the aftermath of the 2008 international financial crisis but negatively influence international interest rates. The international financial crisis factor mainly has had a negative influence on the international interest rate and through the latter has transmitted such negative influences to China's commodity market. How monetary policy interacts with commodity prices and what roles speculation plays in such interactions have been the topics of public policy debates, particularly upon a hike or sharp fluctuation in commodity prices. This paper, from a money-flow perspective, presents new evidence on the roundabout transmission from interest rates to commodity and that speculation does not drive up commodity prices. These new findings remind policymakers to, upon drastic fluctuations in the commodity market, focus main attentions on external risk factors that influence the demand-supply balance and expectations about price changes. Future studies should extend the sample range when data are available and consider the importance of possible structural changes, and nonlinear and quantile relationships between variables by introducing new methodology.

Fig. 3. Long-term equilibrium among variables. Notes: (1) The arrow means the significant impact and its direction between variables, and arrow with slash means that no significant impact exists. (2) “P” means that for at least two of five commodities, the long-term equilibrium exists.

Fig. 4. Granger causality test results. Notes: (1) The arrow means the direction of Granger causality relationship. (2) “P” means that for at least two of five commodities there exists the Granger causality relationship and the figure is the lagged period.

financial crisis, evidence is there, however, to prove that the rise of market liquidity does not drive up commodity prices. Commodity prices dropped sharply in the wake of the 2008 international financial crisis and then recovered in fluctuations with the stimulus of monetary policies. The negative relationship shown in the subsample estimations may have reflected the above price movement trend. The investors’ positive feedback trading model given by De Long (1990) and Tokic (2011) emphasizes the importance of investors’ price trending trading, and the right logic should be that the investors’ actions are driven by the price, instead of the price trend driven by investment. 7.3. Weekend effect and holiday effect Earlier studies on how holiday factors impact China's monetary asset prices focus mainly on the stock market, such as Cao et al. (2007) on the effects of the Spring Festival and Yi and Liu (2005) on the effects of public holidays. Fan and Zhang (2002) prove that weekend effects exist on China's security market. Recent literature on commodity financial markets (Liu and Zhang, 2013; Liu et al., 2012) also identifies evidence of weekend and public holiday effects. In comparison with the prior literature, this paper provides further evidence that weekend effects and holiday effects exist in the commodity financial market and estimates the strength of such effects. It is found that the weekend effects act fairly significantly on the international interest rate and commodity prices in the subsample case, with impacts being negative for the former and positive for the latter. The holiday effects act robustly on money flow and commodity prices, with impacts being positive for both. We find that the holiday effects Appendix A See Appendix Tables A1–A9 and Fig. A.1.

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Table A.1 Descriptive Statistics. Variable

Shibor

Effrr

Input

Output

Liquidity

Flow

Index_SHFE

Price_Al

Price_Cu

Price_Nr

Price_Br

Mean Median Max Min Obs

1.98 1.87 8.53 0.80 1174

1.79 0.20 5.41 0.05 1174

605.46 393.05 6621.64 0.00 1174

556.41 373.15 4941.16 7.83 1174

240.96 217.21 666.45 30.38 872

1.57 1.04 19.58 0.00 1174

3257.51 3445.94 4376.63 1385.80 1174

16797.84 16620.00 21560.00 10210.00 1175

57135.82 60605.00 74830.00 24000.00 1174

22747.43 21810.00 42640.00 9865.00 1174

4260.58 4168.00 5450.00 3436.00 573

Table A.2 ADF unit root test. Variables

Horizontal Value

First Difference

Variables

Horizontal Value

First Difference

Index_SHFE Price_Al Price_Cu Price_Nr Price_Br

-1.359 -1.795 -1.283 -0.502 -2.416

-23.322*** -30.095*** -31.465*** -28.434*** -21.325***

Shibor Effrr Liquidity Flow

-6.215*** -2.673** -1.874 -29.883**

-15.284*** -23.614*** -7.883*** -15.892***

Note: ** represent 5% significance level. *** represent 1% significance level. Table A.3 Full-sample ARDL Model F Test. Dependent variables

Dummy-excluded model Index

Al **

Flow

Effrr Price

Nr **

44.186 Yes 4.640** Yes 1.588 No 47.431** Yes

45.065 Yes 4.958** Yes 1.333 No 47.640** Yes

Shibor

Dummy-included model Cu **

44.473 Yes 4.723** Yes 2.235 No 44.103** Yes

Index **

Al **

44.656 Yes 4.837** Yes 0.967 No 49.170** Yes

Nr **

30.193 Yes 3.000 Uncertain 1.583 No 31.986** Yes

Cu **

29.676 Yes 2.809 Uncertain 1.738 No 28.412** Yes

29.995** Yes 2.922 Uncertain 1.293 No 34.254** Yes

29.811 Yes 2.878 Uncertain 2.282 No 33.173** Yes

Notes: (1)*, ** and *** represent the significance level of 10%, 5% and 1% for the F statistic value, respectively. (2) Yes, No or Uncertain is determined in accordance with Pesaran and Shin (1995), means that cointegration exists, does not exist, or we cannot determine the existence of cointegration, respectively. (3) This table shows the full-sample F test result. Table A.4 Selection of the lag period of the full-sample ARDL model. Dependent variable

Flow

Principle

SBC AIC

Shibor

SBC AIC

Price

SBC AIC

Dummy-excluded model

Dummy-included model

Index

Al

Nr

Cu

Index

Al

Nr

Cu

(2,0,0,0) 0.9384 (2,2,0,0) 0.9358# (1,0,0,0) 0.0997 (3,0,0,0) 0.0994# (2,0,0,0) 0.0182 (3,0,0,4) 0.0181#

(2,0,0,0) 0.9381 (2,0,2,0) 0.9372# (1,0,0,0) 0.0997 (3,0,0,0) 0.0994# (1,0,0,1) 0.0044 (3,0,0,2) 0.0044#

(2,0,0,0) 0.9370 (2,2,0,0) 0.9352# (1,0,0,0) 0.0997 (3,0,0,0) 0.0994# (1,0,0,0) 0.0164 (3,0,0,0) 0.0163#

(2,0,0,0) 0.9383 (2,0,0,0) 0.9383# (1,0,0,0) 0.0997 (3,0,0,0) 0.0994# (1,0,0,0) 0.017 (3,0,0,4) 0.0169#

(2,0,0,0,0,2,0) 0.9260 (2,2,0,0,2,2,1) 0.9215# /

(2,0,0,0,0,2,0) 0.9256 (2,0,0,0,2,2,1) 0.9230# /

(2,0,0,0,0,0,0) 0.9298 (2,1,0,0,2,2,1) 0.9217# /

(2,0,0,0,0,2,0) 0.9254 (2,0,0,0,2,2,1) 0.9227# /

/

/

/

/

(1,0,0,0,0,0,0) 0.0180 (2,0,0,0,0,0,1) 0.0180#

(1,0,0,1,0,0,2) 0.0044 (1,0,0,2,1,1,2) 0.0043#

(1,0,0,0,0,0,0) 0.0161 (1,0,2,0,1,0,0) 0.0161#

(1,0,0,0,0,0,0) 0.0168 (1,0,2,0,0,0,1) 0.0167#

Notes: (1) The figures in () are options of lag periods for each respective variable. (2)# indicates the selected lad period on the basis of SBC and AIC comparison.

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Table A.5 Subsample ARDL model F test. Dependent variable

Dummy-excluded model Index

Al **

Flow

24.853 Yes 2.487 No 3.338 No 6.694** Yes 24.302** Yes

Shibor Liquidity Effrr Price

Dummy-included model Nr **

23.730 Yes 1.977 No 5.178** Yes 6.948** Yes 20.962** Yes

Cu **

Index **

23.889 Yes 2.104 No 2.944 No 6.741** Yes 24.905** Yes

24.293 Yes 2.476 No 3.467 No 6.839** Yes 23.466** Yes

Al **

17.409 Yes 1.738 No 3.941** Yes 5.569** Yes 17.152** Yes

Nr **

Cu **

16.826 Yes 1.459 No 4.813** Yes 5.611** Yes 14.834** Yes

16.773 Yes 1.516 No 3.822** Yes 5.434** Yes 18.356** Yes

17.043** Yes 1.732 No 4.031** Yes 5.609** Yes 17.401** Yes

Notes: (1)*, ** and *** represent the significance level of 10%, 5% and 1% for the F statistic value, respectively. (2) Yes, No or Uncertain is determined in accordance with Pesaran and Shin (1995), means that cointegration exists, does not exist, or we cannot determine the existence of cointegration, respectively. (3) This table shows the subsample F test result. Table A.6 Subsample cointegration estimation: Dummy-excluded model. Dependent variables

Price

Flow

liquidity

Shibor

Effrr

4.474 (7.520) -

-

Al

-

Nr

-

Cu

-

2.360 (5.734) -0.112 (0.077) -0.136* (0.078) -0.145** (9.077) -0.121 (0.077) -0.318 (0.213) -0.332 (0.225) -0.318 (0.207) -0.316 (0.200) -0.002*** (0.001) -0.0001 (0.0004) -0.003 (0.002) -0.003 (0.002)

-3.490** (1.770) -0.064 (0.090) -0.135 (0.092) -0.150 (0.091) -0.088 (0.091) -

Index

1070.700 (1764.600) 11.719*** (3.706) 5.818 (14.408) -0.953 (2.253) 8.210*** (2.891) 7.711 (5.800) 85.517* (44.620) 3.627 (5.896) -2.475 (6.918) -

Liquidity

Al

Flow

Index Al Nr Cu

Effrr

Index Al Nr Cu

Price

0.165 (0.138) 0.174 (0.147) 0.161 (0.134) 0.1749 (0.1309) 0.000 (0.0009) -0.0000 (0.0003) -0.0005 (0.001) 0.0008 (0.001)

0.006 (0.031) -0.014 (0.032) -0.018 (0.031) -0.0007 (0.031) -0.331*** (0.024) -0.333*** (0.026) -0.330*** (0.024) -0.329*** (0.023) -0.002*** (0.0006) -0.0004** (0.0002) -0.001 (0.0007) -0.002** (0.0007)

-0.007*** (0.002) -0.001** (0.0005) -0.005** (0.002) -0.007*** (0.002)

Notes: (1) ** and *** represent 5% and 1% significance level, and the figures in () are standard errors. (2) This table shows the subsample cointegration relationship among variables excluding dummies, and a significant value of the estimated coefficient means that this variable has a significant impact on dependent variable.

Appendix B ARDL model used in this paper With reference to Zou (2014), we establish an ARDL (p,q1,q2,…,qk)of the following structure: k

φ(L, p) yt = ∑i = 1 βi (L, qi)xit + δωt + ut φ(L, p) = 1 − φ1L − φ2 L2−⋯−φp L p βi (L, qi) = 1 − βi1L − βi2 L2−⋯−φiq i L qi

(A.1)

where p stands for yt'sdegree of lag; qi for the degree of lag of number i independent variable xit , i = 1,2,…,k; L stands for the lag operator and is defined as Lyt=yt-1; ωt is the deterministic test pattern at column 1 line s and represents the exogenous variable of fixed degree of lag. First, we estimate ARDL models in number of (m+1)k+1, assuming p (=0,1,2,…,m), qi (=0, 1, 2, …, m) , i (=1,2,…,k). Then, we select from the (m+1)k+1 models by use of the AIC principle and SBC principle. After the ARDL model is determined, the long-run relationship among various variables can be estimated. Nevertheless, prior to ARDL modeling, the ARDL-ECM model needs to be set up to determine whether a long-term robust relationship exists among the variables. The Error Correction Model (ECM) for multiple variables ARDL (p,q1,q2,…) is as follows:

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Table A.7 Subsample ARDL-ECM estimation results: Dummy-excluded model. Liquidity model Variable

ecm(-1)

dLiquidity(-1)

dShibor

dEffrr

dEffrr(-1)

dPrice

dPrice(-1)

dPrice(-2)

dPrice(-3)

Al

-0.003** [-2.274]

0.528*** [16.636]

-0.036* [-1.915]

-0.062*** [-3.568]

-0.034** [-1.964]

1.009** [2.565]

-1.722** [-2.727]

-1.295** [-2.512]

-1.538*** [-4.030]

ecm(-1) -0.754*** [-12.782] -0.739*** [-12.468] -0.748*** [-12.566] -0.751*** [-12.740]

dLiquidity(-1)

dLliquidity(-2)

dPrice(-2)

1.372** [2.489] 1.125** [2.052] 1.386** [2.527]

dShibor -0.768** [2.719] -0.767** [-2.801] -0.737** [-2.680] -0.714** [-2.621]

dPrice(-1) -5.938*** [-4.037]

-1.064* [-1.682] -1.037* [-1.645] -1.290** [-2.051]

dFlow(-3) 0.099*** [2.642] 0.101** [2.663] 0.099** [2.603] 0.098** [2.614]

ecm(-1) -0.042*** [-4.207] -0.040*** [-3.960] -0.044*** [-4.344] -0.045*** [-4.441]

DLiquidity(-1) 0.306*** [3.743] 0.310*** [3.790] 0.297*** [3.636] 0.309*** [3.764]

DLiquidity -0.297*** [-3.596] -0.295*** [-3.596] -0.288*** [-3.488] -0.293*** [-3.549]

dShibor

dEffrr(-3) -0.113** [-3.106] -0.111** [-3.081] -0.109** [-3.011] -0.111** [-3.041]

dPrice

dPrice(1)

2.548** [2.986]

-2.175** [-2.567]

ecm(-1) -1.047*** [-18.405] -0.860*** [-13.829] -0.691*** [-12.644] -0.778*** [-11.436]

dLiquidity 0.041*** [2.871] 0.009** [2.445] 0.027** [2.203] 0.038*** [2.835]

dLiquidity(-1)

dEffrr

dEffrr(-3) 0.016** [2.585]

dPrice(-1) -0.066* [-1.741] -0.016*** [-4.277] -0.105** [-2.193] -0.119** [-1.963]

Flow Model Index Al Nr Cu

11.617** [2.036]

Effrr Model Index Al Nr Cu

-0.014* [-1.660]

Price Model Index Al Nr Cu

-0.028** [-1.974] -0.025* [-1.652]

dLiquidity(-2) -0.040** [-2.773]

0.005** [2.676] -0.003** [-2.139] -0.036** [-2.662]

0.018*** [3.052]

dPrice(-2)

-0.088** [-2.323] -0.094** [-2.489] -0.122** [-2.444]

Notes: (1) *, **and*** represent 10%, 5% and 1% significance level, and Figures in [] are t statistics of the estimated coefficient. (2)This subsample result table shows the impact of error correction term ecm (-1) and the different lagged term of variables excluding dummies on Flow, Shibor, and Price, respectively. (3) The significant value of the estimated coefficient of ecm (-1) indicates the rate of convergence to equilibrium, and a significant estimated value of the lagged term means that a shortterm impact on dependent variable exists. Table A.8 Subsample Granger causality test: The role of money flow. Δ Day

Δ Price→ ΔFlow

Nonferrous Metals Price Index 2 12.42***（0.002） 3 11.51***（0.009） 4 11.65***（0.020） 16.25***（0.006） 5 Metal Copper 2 3.32（0.190） 3 2.75（0.431） 4 3.27（0.513） 5 5.41（0.367） Metal Aluminum 2 4.27（0.118） 3 4.46（0.216） 4 4.98（0.289） 5 8.83（0.116） Natural Rubber 2 0.15（0.929） 3 0.39（0.942） 4 0.51（0.973） 5 0.80（0.977） Screw-thread Steel 2 0.88（0.643） 3 7.77（0.051） 4 7.72（0.102） 5 9.31（0.097）

ΔFlow→ Δ Price

ΔShibor→ ΔFlow

ΔFlow→ Δ Shibor

ΔEffrr → ΔFlow

ΔFlow→ Δ Effrr

2.89（0.236） 3.98（0.264） 9.31（0.054） 9.99（0.076）

0.14（0.933） 2.73（0.434） 4.63（0.327） 8.06（0.153）

3.55（0.169） 3.57（0.312） 3.90（0.420） 6.31（0.277）

10.20***（0.006） 11.73***（0.008） 12.85**（0.012） 12.92**（0.024）

4.99（0.083） 3.88（0.275） 3.86（0.426） 4.12（0.532）

2.95（0.229） 2.66（0.447） 2.91（0.574） 3.53（0.619）

0.28（0.868） 2.92（0.404） 5.00（0.287） 7.97（0.158）

4.53（0.104） 4.48（0.214） 4.76（0.313） 7.78（0.169）

3.95（0.139） 4.18（0.243） 2.32（0.677） 12.47**（0.029）

26.57***（0.000） 30.92***（0.000） 29.52***（0.000） 29.85***（0.000）

0.36（0.835） 0.64（0.888） 0.99（0.912） 1.63（0.898）

0.35（0.841） 2.95（0.400） 5.10（0.277） 7.87（0.164）

4.10（0.129） 4.30（0.231） 4.59（0.332） 7.32（0.198）

4.03（0.134） 4.20（0.240） 2.73（0.604） 12.00**（0.035）

27.88***（0.000） 29.89***（0.000） 30.54***（0.000） 30.98***（0.000）

2.21（0.331） 2.56（0.465） 3.26（0.515） 3.85（0.571）

0.45（0.800） 2.90（0.407） 4.79（0.310） 7.62（0.178）

4.17（0.124） 4.60（0.204） 5.13（0.275） 8.32（0.139）

3.73（0.155） 3.86（0.277） 2.00（0.735） 10.83（0.055）

27.10***（0.000） 27.67***（0.000） 26.73***（0.000） 26.14***（0.000）

1.08（0.582） 1.37（0.713） 3.73（0.444） 3.99（0.550）

0.34（0.843） 3.45（0.327） 5.13（0.275） 8.51（0.130）

4.10（0.129） 4.49（0.214） 4.44（0.350） 6.81（0.235）

10.20***（0.006） 11.73***（0.008） 12.85**（0.012） 12.92**（0.024）

4.99（0.083） 3.88（0.275） 3.86（0.426） 4.12（0.532）

Notes: (1) ** and *** represent 5% and 1% significance level, and Figures in () indicate the probability of Chi-sq testing. (2) This table shows the subsample Granger causality testing result among variables. A significant value of the estimated coefficient means that there exists a lead-lag relationship between the two variables.

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Table A.9 Subsample Granger Causality test: Money flow, market liquidity and interest rate. ΔDay

ΔEffrr→ Δ Price

Nonferrous Metals Price Index 2 0.31（0.857） 3 0.51（0.917） 4 10.33**（0.035） 5 11.81**（0.038） Metal Copper 2 0.012（0.994） 3 0.033（0.998） 4 17.35***（0.002） 5 18.63***（0.002） Metal Aluminum 2 6.96**（0.031） 3 20.35***（0.000） 4 22.59***（0.000） 5 23.02***（0.000） Natural Rubber 2 0.75（0.686） 3 1.17（0.761） 4 4.07（0.397） 5 4.74（0.448） Screw-Thread Steel 2 1.38（0.503） 3 4.71（0.194） 4 5.92（0.205） 5 5.79（0.327）

Δ Price→ ΔEffrr

ΔFlow→ ΔLiquidity

ΔLiquidity→ ΔFlow

ΔLiquidity→ ΔPrice

Δ Price→ ΔLiquidity

0.46（0.796） 7.40（0.060） 15.19***（0.004） 17.29***（0.004）

2.75（0.254） 5.32（0.150） 14.23***（0.007） 12.54**（0.028）

1.17（0.556） 3.98（0.263） 4.80（0.308） 5.92（0.314）

0.23（0.892） 2.53（0.470） 5.11（0.276） 5.58（0.349）

3.30（0.192） 5.89（0.117） 6.43（0.169） 5.67（0.340）

0.37（0.831） 12.23***（0.001） 19.21***（0.001） 24.06***（0.000）

3.19（0.203） 5.57（0.135） 15.02***（0.005） 14.00**（0.016）

1.03（0.598） 3.82（0.282） 4.96（0.292） 6.37（0.272）

0.60（0.741） 1.80（0.614） 5.73（0.220） 5.60（0.347）

2.06（0.356） 9.15**（0.027） 8.01（0.091） 8.50（0.131）

5.59（0.061） 13.10***（0.004） 14.31***（0.006） 19.13***（0.002）

3.26（0.196） 5.73（0.126） 15.73***（0.003） 13.82**（0.017）

1.03（0.597） 3.86（0.277） 5.34（0.254） 7.16（0.209）

0.88（0.643） 1.05（0.789） 5.54（0.236） 5.80（0.326）

2.65（0.265） 9.07**（0.028） 7.29（0.122） 6.80（0.236）

1.58（0.453） 3.56（0.313） 5.22（0.265） 6.99（0.222）

3.29（0.193） 4.73（0.192） 15.27***（0.004） 13.50**（0.019）

1.22（0.545） 4.06（0.255） 4.91（0.296） 6.48（0.262）

0.98（0.613） 5.43（0.143） 5.61（0.231） 4.96（0.421）

3.06（0.216） 2.67（0.446） 1.68（0.795） 0.88（0.972）

1.58（0.454） 3.21（0.361） 4.52（0.340） 5.23（0.389）

3.40（0.183） 4.49（0.213） 15.75***（0.003） 12.31**（0.031）

1.10（0.578） 4.60（0.204） 6.42（0.170） 8.77（0.118）

3.92（0.141） 4.60（0.203） 5.69（0.223） 5.55（0.353）

1.22（0.543） 1.89（0.595） 1.61（0.806） 1.52（0.911）

Notes: (1) **and*** represent 5% and 1% significance level, and Figures in () indicate the probability of Chi-sq testing. (2)This table shows the subsample Granger causality testing result among variables. A significant value of the estimated coefficient means that there exists a lead-lag relationship between the two variables.

Fig. A.1. Interest Rates, SHFE's Money Flow and Market Liquidity Index: October 2006-July 2011. Notes: (1) The right vertical axis denotes Liquidity. (2) Flow is the ratio of money input/output of SHFE. Source: WIND Database and SHFE. p

q1

q2

ΔYt = β0 + ∑i = 1 β1i ΔYt − 1 + ∑i = 0 β2i ΔX1t − i + ∑i = 0 β3i ΔX2t − i+… + δ1Yt − 1 + δ2 X1t − 1 + δ3 X2t − 1+…+εt

(A.2)

δ2 andδ3 for the long-term dynamic relationship or cointegration; β0 , β1i , β2i andβ3i for the short-term Where Δ stands for the difference operator; δ1, dynamic relationship; and εt for the white-noise process. Based on the F statistic, the unified significance test is performed on the below hypothesis. The original hypothesis is that a robust long-term relationship does not exist among the variables. (A.3)

H0 : δ1 = δ2 = δ3 = …=0 The optional hypothesis is:

H1: At least one of δ1, δ2 , δ3. … is not 0

(A.4)

If the original hypothesis is proved true, then cointegration does not exist among the variables. If the optional hypothesis is proved true, then cointegration exists among the variables. What should be noted is that whether a variable is I(0) or I(1) process, the F statistic is submissive to a nonstandard distribution. Therefore, determination should not be made with reference to F statistic threshold values under normal standards. Pesaran and Pesaran (1997) prepare a table of F statistic threshold values corresponding to different numbers of regressive items. If the F (Y|X1,X2,…) value exceeds the upper threshold, the original hypothesis is rejected, representing that cointegration exists among the original variables, and the independent variables X1, X2 have a long-term influence on dependent variable Y. If F(Y|X1,X2,…) is smaller than the lower threshold, the original hypothesis is accepted, representing that cointegration does not exist among the original variables. If F(Y|X1,X2, X3) is between the upper and lower 15

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Z. Sun et al.

threshold values, it is not possible to determine whether cointegration exists among the variables.

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