“Hedging with commodity futures: evidence from the coffee market in Vietnam”

In July 2018, the Vietnam Commodity Exchange (VNX) was transferred into the Mercantile Exchange of Vietnam (MXV) to hedge price risks through futures on international commodity exchanges. This research aimed to verify the efficiency of futures on ICE EU and ICE US under the perspective of hedging for Vietnamese coffee, determine optimal hedging ratios and the optimal number of each futures contract, and investigate the feasibility of introducing domestic commodity exchanges in Vietnam. Using the Vector Error Correction Model (VECM), the results show that (1) Robusta futures with expiration dates of January, March, May, and July on ICE EU are efficient hedging tools, but the adverse result is justified for Arabica futures on ICE US; (2) Robusta futures with the expiration date of January are the best in terms of risk management for Vietnamese coffee market; (3) optimal hedge ratio of Robusta futures of around 34% is much lower than ratios showed by previous researches; (4) in the short term, introducing coffee futures into the domestic commodity exchanges is still not feasible in the short term, but should be considered in the long term in Vietnam. This is the first study providing empirical evidence about the hedging role of futures contracts on ICE EU and ICE US, contributing to enrich the existing empirical evidence on the hedging role of futures for the agricultural sector. and VNX. A few years after, on 8 th June 2018, the Ministry of Industry and Trade issued license No. 486/GP-BCT, allowing the Mercantile Exchange of Vietnam (MXV) to run businesses. On 17 th August 2018, MXV officially launched the nationally centralized commodities exchange, bringing Vietnamese participants’ hedging opportunities through trading on ICE EU and ICE US. The above practice provides us a high motivation to examine if coffee futures trading on international commodity exchanges completes a hedging mission for the domestic spot market. If coffee futures of-fered by ICE EU and ICE US are really useful financial instruments of risk management for Vietnamese participants, optimal hedging ratios and the optimal number of contracts will be determined. In line with this objective, this research applies Vector Error Correction Model (VECM) to investigate if the long-run relationship between the futures price and domestic price exists and which futures are the best hedging tools for Vietnamese producers. Besides, theoretical frame-©


INTRODUCTION
Since 2000, in an attempt to hedge, the Government of Vietnam was committed to developing commodity exchanges for some crucial agricultural products, including coffee, rice, and rubber, such as Buon Ma Thuot Coffee Exchange Center (BECE) and Vietnam Commodity Exchange (VNX). However, trading results on coffee are modest in terms of value and volume, which means very low liquidity on BECE and VNX. A few years after, on 8 th June 2018, the Ministry of Industry and Trade issued license No. 486/GP-BCT, allowing the Mercantile Exchange of Vietnam (MXV) to run businesses. On 17 th August 2018, MXV officially launched the nationally centralized commodities exchange, bringing Vietnamese participants' hedging opportunities through trading on ICE EU and ICE US.
The above practice provides us a high motivation to examine if coffee futures trading on international commodity exchanges completes a hedging mission for the domestic spot market. If coffee futures offered by ICE EU and ICE US are really useful financial instruments of risk management for Vietnamese participants, optimal hedging ratios and the optimal number of contracts will be determined. In line with this objective, this research applies Vector Error Correction Model (VECM) to investigate if the long-run relationship between the futures price and domestic price exists and which futures are the best hedging tools for Vietnamese producers. Besides, theoretical frame-works developed by Hull (2012) are also applied to compute optimal hedging ratio and the optimal number of each kind of futures.
To our best knowledge, the contributions of the study are framed in providing empirical evidence indicating if futures trading of Robusta on ICE EU and Arabica on ICE US plays an important role of hedging for Vietnamese participants, as well as what are the optimal hedging ratio and the optimal number of contracts for each kind of futures that they should mention when trading on international commodity exchanges. These important results will be germane to have more efficient risk management in terms of price.

LITERATURE REVIEW
Since forwarding transactions of agricultural products have become common solutions for risk management in the international markets (Herrmann, 1993), several studies examine the role of futures hedging on prices. However, the research results are not homogeneous with different underlying commodities in various markets. Janet (1995) questions the futures hedging roles by examining the impact of the futures market on spot prices based on daily observations of the high and low prices of futures, weekly observations of the highest and lowest spot prices of wheat and corn exported in Chicago and mean-variance framework. Thus, the high volume of stockpiled agricultural products would reduce the fluctuation of spot prices by more than 50%. By contrast, the futures market increases the variance of spot prices, enlarging the wheat and corn spot price gap. Larson et al. (1998) justify futures' role in the economy and their impacts on the volatility of underlying prices in developing countries. Besides, by strongly showing that state interventions in the commodity market to stabilize prices are very costly, ineffective, and accompanied by negative effects when these price stabilization programs fail, the authors encourage countries to promote futures markets as a mechanism to disperse price risks.
Using the same approach methods, Larson et al. (1998) and Morgan (2000) figure that policies launched by some less developed countries (LDCs), which aim to reduce the negative impact of price volatility in commodity markets, tend to fail because they cannot strongly support small producers and farmers due to some main barriers such as lack of infrastructure, high cost, lack of knowledge, credit risks, basic risks, and low liquidity. Hence, these countries can implement solutions such as supporting policies, technology, infrastructure, etc. In particular, international organizations, governments, and the private sectors should play crucial roles in approaching these risk management tools. Choudhry (2009) agrees that commodity futures facilitate the transfer of risks from keepers of physical goods (hedgers) to investors or speculators in the market.
In terms of the research methodology, it can be seen that previous researches find the correlation between futures prices and spot prices by separately applying different kinds of cointegration, including VECM, VAR, GARCH, or mostly combined methods. Hudson et al. (1996) collect 81 observations per year for 4 consecutive years and then apply Vector Error Correction Model (VECM) to show that cotton spot prices and futures prices are not correlated, which means that futures market and spot market experience a weak relationship. Using the same approach, Arfaoui (2018) justifies the longterm equilibrium relationship between crude oil spot prices and futures prices on the NYCE from 2007 to 2015. The speed of adjustment to the longterm equilibrium is not high, but faster for refined oil on the spot market. Dividing the GARCH model into four different versions such as bivariate GARCH, bivariate BEKK GARCH, bivariate GARCH-X, and bivariate BEKK GARCH-X, Choudhry (2009) compares the hedging effect of stockpiled goods against non-stockpiled goods based on futures prices and spot prices of seven agricultural products, includ-ing maize, coffee, wheat, sugar, soy, livestock, and live pigs. The results reveal the outstanding performance of the through estimated hedge ratios calculated by the GARCH-X model in all stages, supporting Hudson et al. (1996) and Yang et al. (2001). However, different from Yang and Awokuse's (2003) research results, Choudhry (2009) points out that hedging effectiveness between stockpiled and non-stockpiled goods is quite similar. Nicolau and Palomba (2015) focus on the multivariate VAR model to figure that dynamic interactions between spot prices and futures prices considerably depend on each commodity market's characteristics and justify that spot and futures prices were always correlated. Yang and Awokuse (2003) choose both VECM and two-variable GARCH as a research framework to justify that futures contracts played an effective hedging role in stockpiled goods. Jackson and Woodruff (2016) apply VECM and GARCH to give empirical evidence about close relationships between spot prices on the domestic coffee market in Ugandan and futures prices on international exchanges. Using OLS, VAR, and VECM models, Wang and Chidmi (2011) determine hedge ratios for cotton in various countries, thereby justify whether cotton futures on the NYCE are a hedging tool. Similarly, Wibowo (2017) investigates the effectiveness of hedging strategies by using three models to estimate the optimal hedge ratio, including OLS, VCEM, and TARCH. Yang et al. (2005) apply all four models, including OLS, VAR, VECM, and multivariate GARCH, based on two approaches: risks -profits comparison and maximizing benefits to compare the hedging effects of optimal ratios. Besides the hedging role of futures, researchers are also interested in the feasibility of launching it on domestic commodity exchanges. The first study is realized by Black (1986) who shows that the success of the futures contracts strongly depends on the characteristics of the underlying assets and is strictly related to the scope of the cash market, price volatility, the risk reduction effect, and liquidity costs. Tashjian (1995) explores how to design successful futures from two perspectives, such as properties of futures and optimal forms of contracts. Rutten (1998) assesses the potential of futures for tropical plywood. Pennings and Leuthold (1999) examine the correlation between trading volume and hedging effectiveness by taking into account both fundamental and market depth risks and the relationship between underlying characteristics and the probability of using the futures contract. Brorsen and Fofana (2001) emphasize that a dynamic cash market is necessary for the success of futures contract since this variable perfectly predicts the futures contract existence, while other variables, such as vertical integration, homogeneity, and buyer concentration, are also important to explain differences in trading volumes and open interest rates between futures markets.
Based on studies of Black (1986) and Pennings and Meulenberg (1998), Bergfjord (2007) measures the volume of future contracts (V) through contract size (FCZ) and cash market size (CS),

Research design
The study is designed with three specific steps. Firstly, the research examines the futures price hedging role through the correlation between the spot price and futures price on ICE EU and ICE US. Secondly, the optimal hedge ratios are calculated if futures strongly support risk management. Finally, the study investigates the feasibility of introducing coffee futures exchange into the domestic exchange market.

Data source
Both primary and secondary data are used in this research. The secondary data include FOB Ho Chi Minh price and the daily trading price of coffee futures with different expiration dates on the ICE US for Arabica and the EU ICE for Robusta. There are 6 Robusta futures contracts on the ICE EU with expiration dates in 2020, namely January, March, May, July, September, and November (Table 2) and 5 Arabica futures contracts, which expire in March, May, July, September, and December ( Table 3). All of the above secondary data are collected from Thomson Reuters Datastream. The re-

Methods of data analysis
• Verifying the correlation between spot prices and futures prices of coffee.
The research estimates the correlation between the spot price and futures prices of coffee through 6 steps (see Figure 1).
Step 1. Stationarity test (unit root test) The study applies the Augmented Dickey-Fuller test to test price data stationarity. There are three basic regression models: • No constant, no trend:   Step 2. Determining optimal lag To determine the optimal lag length, the study uses all of the different criteria, such as Akaike information criterion (AIC), Schwarz information criterion (SC), FPE criterion (final prediction error), and Hannan-Quinn information criterion (HQ). It then selects the one with the lowest statistic value as the optimal lag length.
Step 3. Co-integration test The hypothesis H 0 will be rejected if the Trace and Max Eigenvalue statistic value is more than 5% critical value. If futures contract price does not cointegrate with the spot price, the study will perform the additional step 6.
Step 4. Vector Error Correction Model (VECM) An estimated VECM is as follows: Step 5. Stability test of VECM model The study ensures the model's stability by using the inverse roots of the characteristic AR polynomial test and CUSUM test. Besides, the study verifies the autocorrelation of residuals through VEC residual serial correlation LM tests on EViews 10 software. If the results show that p-value at lag order h less than the significance of 5%, the hypothesis H 0 is accepted.
Step 6. Granger causality test In the case of futures contract price do not have a cointegration with the spot price, the study will perform the Granger causality test to consider the short-term causal relationship between the coffee futures price and coffee spot price in Vietnam.
There are bivariate regressions of the form: • Evaluating the feasibility of introducing coffee futures contracts on the domestic commodity exchange.
The research evaluates the feasibility of introducing coffee futures contracts on domestic commodity exchange by summarizing average scores of 4 main determinants, such as spot price volatility criteria, liquidity, homogeneity, and objective factors.  Tables 4 and 5 give statistics about spot prices and futures prices for Robusta and Arabica accordingly. It can be seen that max, min, and mean values of coffee spot prices always smaller than those of futures prices on ICE EU and ICE US. Coffee futures prices also have a significantly higher standard deviation than that of spot prices, which means that the latter does not fluctuate as futures prices. Moreover, this index slightly goes down according to the maturity of futures contracts. In other words, the longer maturity is, the smaller the standard deviation is.

Data description
Source: Thomson Reuters Datastream.

Robusta
By regression with intercept and trend, the result shows that all the series of spot prices in Vietnam and the series of futures prices have trend and intercept (Appendix 2). Then, different criteria like AIC, SC, HQ show that prices of Robusta in Vietnam and all Robusta futures contract prices have an optimal lag of 1 (Appendix 3). The Trace and Max-Eigenvalue tests (Appendix 4) indicate a cointegration (long-term relationship) at the 5% significance level between Robusta spot prices and futures with expirations of January, March, May, and July. However, there is no cointegration relationship at the 5% significance level between Robusta spot prices and futures with September and November expirations. Therefore, the study continues to perform the Granger causality test to estimate the causal relationship between Robusta futures contracts' prices expire in September and November with spot prices. The study then uses the series after the first differencing to estimate the VECM model (Appendix 5). Table 6 summarizes the VECM results, showing the overall relationship and the long-term relationship between price volatility of futures contracts expire in January, March, May, and July with price volatility of Robusta spot prices.
It can be seen that there is an optimal lag of 1 for Robusta prices series at the beginning, while after the first differencing, this number is 0. Moreover, the volatility of futures prices and spot prices is unpredictable and less affected by the price changes in the short term.
The main feature of the VECM is finding any imbalance and redirecting system variables back to equilibrium. Figure 3 shows that the coefficient ω of contracts expiring in January, March, May, and July is from -1 to 0, which means that Robusta futures with expirations of January, March, May, and July can serve as tools of hedging price risks for Robusta in Vietnam.
Appendix 6 shows of stability test for VECM. AR characteristic tests for all VECM models show that all the inverse roots of the characteristic AR polynomial have modules less than 01 and lie inside the unit circle, which means that all VECM models ensure stability and sustainability. The CUSUM tests also show that the cumulative sum of recursive residuals' movement is always between the two critical lines of 5%, showing the coefficient's stability in VECM models.
After testing the stability of VECM, the research continues to test the autocorrelation of residuals. than 5% significance level. Therefore, the hypothesis H 0 is accepted, which means that there is no autocorrelation at lag order h (with h = 1). In other words, there is no autocorrelation at lag order 1 of VECM. Moreover, testing the Granger causality between September and November futures prices with Robusta's spot prices in Vietnam indicates a Granger causality of volatility in futures prices for spot prices (with a p-value of 0.0114 and 0.0282). However, the opposite is not true because the p-value of 0.8011 and 0.8470 for September and November futures accordingly are more than 5% significance level (Appendix 7).

Arabica
The ADF test shows that these series do not satisfy the 5% significance level in the range of data collected on Arabica futures and spot prices. After the first differencing, all of the series have no trend and no intercept and satisfied the 5% significance level (Appendix 9). Spot prices and all futures prices have a lag of 1 (Appendix 10). New series have a lag of 0. Trace and Max Eigenvalue tests show no significant cointegration at 5% significance level between spot prices and futures prices (Appendix 11). Therefore, the VECM model cannot be applied.

Estimated VECM Cointegrating equation (long-run model)
Futures JAN 2020

The optimal hedge ratio and the optimal number of contracts
The study estimated the correlation coefficient by processing on EViews 10 software between the spot prices and futures prices of Robusta, which expire in January, March, May, and July (Appendix 8). Using the formula proposed by Hull (2015), optimal hedge ratios are calculated as follows: • Robusta futures with the expiration date of January: • the optimal number of Robusta futures, which expire in January: • the optimal number of Robusta futures, which expire in March: • the optimal number of Robusta futures, which expire in May: ** 55 0.03361 ; • the optimal number of Robusta futures, which expire in July:

The feasibility of introducing coffee futures exchange into the domestic exchange market
In-depth interviews show that Vietnamese experts highlight the extremely crucial role of liquidity for the success of futures on the domestic commodity exchange. Following this factor, there are spot price volatility and objective factors. A significant change in spot prices strongly encourages coffee producers and traders to seek efficient tools to hedge their price risks. Moreover, the role of Government is essential in the context of developing countries like Vietnam.
Based on 4 criteria such as volatility in spot prices, liquidity, homogeneity, and objective factors, experts evaluate coffee futures feasibility on domestic commodity exchange in Vietnam. It can be seen that Vietnamese coffee experiences high volatility in both spot and futures prices (with Source: Authors' calculation. 4 points), which means a high need for hedging risks from Vietnamese coffee producers. The homogeneity factor is quite good, with 3.5 points. However, liquidity only obtains 2.5 points even though this is the most essential and decisive factor for the success of a futures contract (Figure 4). In other words, according to Vietnamese experts, launching coffee futures on domestic commodity exchange is not feasible in Vietnam. Secondly, the January contract is the best in terms of hedging tools. For futures which have no cointegration with spot prices like contracts for September and November, the research continues to test the Granger causality at a significance level of 5% and figures that these futures experience a short-term causal impact on spot prices, but the opposite was not true. This means that Vietnamese Robusta does not have enough power to affect international markets' coffee prices at the end of the year.

DISCUSSION
These findings are appropriate to Vietnam production situations, where production values always peak at the beginning of the years. Some provinces in the Central Highlands, such as Gia Lai, KonTum, Dak Lak, Dak Nong, are the main areas where Robusta grows up and develops. The harvest season usually takes place from October to December or one month later. At that point, the domestic market supply is abundant and frequently reaches a peak in January. While a few big producers wait for higher prices by temporarily stockpiling, most farmers are obliged to immediately sell coffee since they have to pay to banks due to their loans or agents which supply fertilizers and pesticides. Therefore, the first months of the years always experience very high export values of coffee in Vietnam ( Figure 5).
In terms of Arabica, this kind of coffee grown up at an altitude of 1,000 m above sea level, in which the most delicious product requires an altitude of 1,500 m. Being quite sensitive to the climate, this kind of coffee strictly requires temperature and average annual rainfall, and high cultivation and harvest techniques, while most Vietnamese farmers struggle to have money to invest in Arabica. Therefore, only some Vietnamese producers are interested in this kind of coffee, which leads to the fact that Arabica's   T1  T2  T3  T4  T5  T6  T7  T8  T9  T10  T11  T12 production values are very low in Vietnam, and then Arabica is not the main product for exportation in Vietnam. That is why the research does not find any relationship between Arabica spot prices and futures prices on ICE US.
Thirdly, the optimal hedging ratios are approximately 34% for Robusta futures with expiration dates of January, March, and May, which are much less than the results computed by previous research (  Besides, Vietnamese coffee is always ranked at a very high position globally in terms of export value. Thus, developing domestic commodity exchanges will be an indispensable trend in Vietnam when one needs an organization that manages and regulates commodity prices in the market.

CONCLUSION
The introduction of the Mercantile Exchange of Vietnam (MXV) in August 2018 remarked the appearance of the nationally centralized commodities market in Vietnam. It was a very good sign for a growing maturity in the national financial market. The study provides the first empirical evidence about the hedging role of futures contracts on ICE EU and ICE US for Vietnamese participants from January 2018 to December 2019. Using Vector Error Correction Model (VECM), the research demonstrates that there is only a long-run relationship between spot price in the Vietnamese market and futures prices in January, March, May, and July on ICE Europe for Robusta. Besides, the research contributes to enrich the existing empirical evidence on the hedging role of futures for the agricultural sector. The study results are very significant when futures are still not popular for Vietnamese participants in agriculture.
However, it is seen that the research is executed in a short period from 2018 for only 5 futures contracts and only focus on the most important role of hedging. Therefore, the research results cannot fully reflect the total nature of coffee trading on Vietnam's commodity market. There is a need for further follow-up studies with deep analysis, highly specific recommendations, and a longer (richer) sample of the data about different kinds of coffee futures contracts and especially about the feasibility of developing futures trading for coffee in the domestic market.