A Study on Commodity Market Behaviour, Price Discovery and Its Factors

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1 See discussions, stats, and author profiles for this publication at: A Study on Commodity Market Behaviour, Price Discovery and Its Factors ARTICLE in SSRN ELECTRONIC JOURNAL JANUARY 212 DOI: /ssrn DOWNLOADS 14 VIEWS 86 3 AUTHORS, INCLUDING: Mihir Dash Alliance University 62 PUBLICATIONS 17 CITATIONS SEE PROFILE Available from: Mihir Dash Retrieved on: 24 June 215

2 A STUDY ON COMMODITY MARKET BEHAVIOUR, PRICE DISCOVERY AND ITS FACTORS Mihir Dash Abhishek Solanki Shobana T. ABSTRACT Commodity markets have been gaining importance in recent years, giving participants an opportunity to go for forward contracting and hedging. In particular, derivative markets have attained more than eighteen times in trading volume when compared to the spot markets. This paper provides an overview of the commodity market in India and its participants, and analyses twelve commodities that are traded in MCX (Multi Commodity Exchange), in terms of price discovery of the spot and futures markets using GARCH model. It also analyses the impact of trading volume, inflation and other macroeconomic factors on spot and futures price movements. Keywords: commodity markets, price discovery, GARCH model, trading volume, inflation, macroeconomic factors. INTRODUCTION The commodity market is very dynamic, offering the opportunity of forward contracting and hedging, and witnessing activity almost eighteen times higher in volume as compared to the spot market. However, awareness of the commodity market is less when compared to the stock market. This is mainly due to the huge investment that is required in order to hedge and trade, even though only a small margin amount is required. Three multi-commodity exchanges have been set up in India to facilitate commodity trading for the retail investors. They are the Multi-Commodity Exchange (MCX), the National Commodity and Derivatives Exchange (NCDEX), and the National Multi-Commodity Exchange (NMCE). Commodity prices are typically characterized by substantial volatility. The uncertainty that accompanies price volatility affects traders whose trading strategies are based, in part, on shortterm volatility movements, and investors interested in hedging an underlying diversified portfolio of commodities. In particular, producers need to manage their exposure to fluctuations in the prices for their commodities. They are primarily concerned with fixing prices on contracts to sell their produce; hence the existence of futures markets. METHODOLOGY The primary objectives of the study were to analyse the long term volatility of each commodity, to compare volatility between different commodity groups, to examine the effect of inflation and volume on the prices of the commodities, and to understand the fundamental and technical factors affecting the price of each commodity. The data used for the study consisted of the daily closing prices of twelve commodities in Indian market taken for a period This period was chosen, as the Indian commodity market was revived in 22. The data is taken from the MCX 1, due to very thin trading in the NCDEX. The commodities were categorized into precious metals (gold and silver), base metals (aluminium, 1 Electronic copy available at:

3 copper, nickel, zinc and tin), energy (natural gas and crude oil), and agri-products (channa, wheat and pepper). To examine the volatility dynamics, the study uses the GARCH (1, 1) model (Bollerslev, 1986). The GARCH (1, 1) model is given by: rt t,. In the academic literature, the GARCH (1, 1) process is perceived as a realistic model for volatility. The forecast variance from the GARCH (1, 1) model can be interpreted as a weighted average of three different variance forecasts. The first is a constant variance that corresponds to the long-run average. The second is the forecast that was made in the previous period. The third is the new information that was not available when the previous forecast was made. This could be viewed as a variance forecast based on one period of information. The weights on these three forecasts determine how fast the variance changes with new information and how fast it reverts to its long-run mean. The parameters of the GARCH model are: µ, the long-run mean returns of the commodity; α, the persistence of returns shocks; β, the persistence of volatility shocks; w, the regression constant; and v L, the long-run volatility. In particular, α+β measures the persistence of returns and volatility shocks; if greater than unity, it implies that there is no recovery from shocks, leading to nonstationarity. The GARCH model has been used extensively to model volatility in commodity prices. Many studies have used GARCH modelling to investigate the effect of futures price volatility on spot price volatility (Kamara, 1982; Antoniou and Foster, 1992; and Gulen and Mayhew, 2). Yang et al (25) examined the lead-lag relationship between futures trading activity (volume and open interest) and spot price volatility for major agricultural commodities, finding that unexpected increase in futures trading volume unidirectionally causes an increase in spot price volatility for most commodities. Several studies also examine factors influencing specific commodity markets. Pindyck (24) examined the behavior of natural-gas and crude-oil price volatility using GARCH modeling, but found mixed results concerning their inter-relationships. Ates and Wang (27) examined the role of fundamentals in inter-temporal pricing relations in natural gas and heating oil spot and futures markets. To examine the impact of trading volume and inflation on commodity price volatility, the GARCH(1, 1) model was extended to include factor terms: ' ' ' ', where t and σ t 2 refer to the factor returns and volatility processes. The F-test was used to test the significance of α and β and, thus, the significance of the impact of the factor on commodity price volatility. t t 1 t 1 t 1 t 1 FINDINGS Some general observations from the literature about commodity prices and factors affecting commodity prices are discussed at the outset. Precious Metals Gold and Silver 1. Gold prices and US Dollar are inversely related, as gold is an effective investment when the US Dollar falls. 2. Investors generally buy gold as a hedge against economic, political, or social crises such as investment market declines, national debt, currency failure, inflation, war and social unrest. 3. Changes in the supply of gold affect gold prices. 4. Silver prices follow gold price trends and serves as another effective investment. 5. Silver prices are also affected by other factors such as supply constraints and mine strikes. 2 Electronic copy available at:

4 Base Metals Aluminium, Zinc, Tin, Nickel and Copper 1. Base metal prices are affected mainly by changes in inventory level, global growth and demand in major consuming industries, including the construction, packaging, and transportation industries, and prices of alternative metals & alloys. 2. Political unrest in the producing countries such as South America and North America also affect base metal prices. 3. Any new production also takes years to commission as the scale of mining is large, it takes enormous financing and requires endless environmental permissions and needs extensive infrastructure as well. Energy Crude oil and Natural gas 1. The fundamental cause of crude oil price change is weather/seasonality. 2. Other factors affecting crude oil prices include OPEC output, supply and spare capacities, currency fluctuations, US crude and products inventories data, speculative buying and selling, Middle East supply disruptions; and crude oil prices are extremely sensitive to supply shortages. 3. Natural gas prices tend to follow crude oil prices, and the other factors such as weather, production, inventories, and many a times it is news-driven. 4. Crude oil is a close substitute for natural gas; hence it is been observed that the prices of both are dependent on each other except in few of the cases where the other factors get involved. 5. Natural gas prices rose above what was seen as their historical relationship with crude oil prices in 2, 22, 23 and late 25. In the early 25 and the first half of 26, natural gas prices seemed to fall well below this historical relationship 6. Variation in weather from the seasonal norm also affects natural gas prices, with above normal heating and cooling degree days adding upward pressure on natural gas prices. 7. Because natural gas consumption is seasonal but production is not, natural gas inventories are built during the summer for use in the winter. This seasonality leads to higher winter prices and lower summer prices 8. Inventories above the seasonal norm depress prices while inventories below the seasonal norm boost prices. Disruptions of natural gas production, such as happened during hurricanes Katrina and Rita in US, also boosted the price. Agro-Products Channa, Wheat and Pepper 1. The fundamental factor that affects agro-product prices is the monsoons in India, affecting production and supply. 2. Storage constraints also affect agro-product prices, as they are short-lived. 3. Other factors include prevailing inflation, supply constraint, increasing agricultural costs of production, growing foreign exchange holdings by major food-importing countries, and policies adopted recently by some exporting and importing countries to mitigate their own food price inflation. Spot and Futures Price Trends The trends of spot and futures prices of some of the commodities are shown in the graphs below. The trends of spot and futures prices follow similar patterns in all commodity prices, and suggest a lead-lag relationship between spot and futures prices. 3 Electronic copy available at:

5 Date 15-Apr-6 5-Aug-6 28-Nov-6 22-Mar-7 16-Jul-7 8-Nov-7 3-Mar-8 26-Jun-8 2-Oct-8 14-Feb-9 12-Jun-9 7-Oct-9 2-Feb-1 28-May-1 21-Aug-1 15-Dec-1 8-Apr-11 Oct Jan 1 27 Mar Jun Aug Oct Jan Apr Jul Oct Jan Apr Jul Oct Jan Apr Jul Oct Jan Date 11-Jun-5 18-Oct-5 23-Feb-6 3-Jun-6 6-Nov-6 14-Mar-7 2-Jul-7 28-Nov-7 7-Apr-8 11-Aug-8 18-Dec-8 27-Apr-9 3-Sep-9 11-Jan-1 2-May-1 24-Sep-1 1-Feb-11 Gold -Spot & Future Price Spot Price Future Price 5 Figure 1. Gold Prices Natural Gas - Spot & Future Price Spot Price Future Price Figure 2. Natural Gas Prices Aluminium Spot & Future Price Spot Price Future Price Figure 3. Aluminium prices 4

6 Date 21-Jul-4 18-Jan-5 18-Nov-5 11-May-6 31-Oct-6 24-Apr-7 15-Oct-7 8-Apr-8 26-Sep-8 2-Mar-9 11-Sep-9 5-Mar-1 25-Aug-1 14-Mar-11 Date 27-Jul-5 17-Dec-5 27-Feb-6 5-May-6 1-Jul-6 2-Sep-6 1-Jul-9 4-Sep-9 1-Nov-9 16-Jan-1 24-Mar-1 29-May-1 31-Jul-1 6-Oct-1 9-Dec-1 14-Feb-11 2-Apr-11 Wheat - Spot Price & Future Price Future Price Spot Price Figure 4. Wheat Prices The next set of graphs compare commodity futures prices within the categories: precious metals, energy, base metals, and agro-products. Gold Vs Silver Future Price of Silver Future Price of Gold Figure 5. Comparison of Gold and Silver Prices Gold and silver futures prices show similar trends, except towards the end of the period. There are several common factors determining the prices of gold and silver in the spot market such as festival days, auspicious days (for example, Akshaya Tirutham), which result in their co-movement. 5

7 Date 14-Oct-6 8-Mar-7 17-Jul-7 27-Nov-7 8-Apr-8 14-Aug-8 26-Dec-8 7-May-9 16-Sep-9 27-Jan-1 7-Jun-1 15-Oct-1 23-Feb-11 Date 1-Nov-6 14-Mar-7 18-Jul-7 23-Nov-7 29-Mar-8 29-Jul-8 28-Nov-8 7-Mar-9 15-Jun-9 2-Oct-9 27-Jan-1 31-May-1 5-Nov-1 14-Mar-11 Energy Comparison Crude oil Future Price Natural Gas Future Price Figure 6. Comparison of Energy Prices Crude oil prices seem to be more volatile than natural gas prices, and there does not seem to be much co-movement in crude oil and natural gas prices. Base Metals Comparison Aluminium Future Price Copper Future Price Zinc Future Price Tin Future Price Nickel Future Price Figure 7. Comparison of Base Metals Prices While comparing the base metals, nickel and tin futures prices seem to follow each other, while zinc and aluminium also follow similar trends, with a possible lead-lag relationship between them. 6

8 GARCH estimates The GARCH estimation for the sample commodities yielded the results shown in the tables below: GARCH_ Spot price volatility commodity µ ω α β v L α+β GOLD.1369% % SILVER.% %.9999 CRUDE OIL.197% % NATURAL GAS.5361% %.9999 ALUMINIUM.% %.9999 COPPER.% %.99 NICKEL.128% % ZINC.% % TIN % %.9999 CHANNA.1% % WHEAT.% %.99 PEPPER.% %.99 GARCH_ Futures price volatility commodity µ ω α β v L α+β GOLD.1% %.9999 SILVER.8683% % CRUDE OIL.% %.9999 NATURAL GAS.1942% % ALUMINIUM.% % COPPER.% %.9999 NICKEL.132% % ZINC.% % TIN.2589% % CHANNA.183% %.8284 WHEAT.% %.9999 PEPPER.233% % The GARCH estimates indicate that long-run mean returns of tin, natural gas, silver, channa, crude oil, and gold were appreciable, while that of aluminium, copper, zinc, and wheat were negligible. The long-run volatility of zinc and gold were low, while that of the other commodities were relatively high, especially that of channa and natural gas. The ARCH coefficient for most commodities was close to 2%; for spot price volatility, α varied between 13.61% (for nickel) to 23.64% (for crude oil), while for futures price volatility, α varied from.% (for channa) to 39.34% (for pepper). The GARCH coefficient for most commodities was close to 8%; for spot price volatility, β varied between 48.52% (for gold) to 85.86% (for channa), while for futures price volatility, β varied from 52.8% (for pepper) to 97.29% (for silver). The overall persistence for most commodities was close to 99%; for spot price volatility, α+β varied between 7.98% (for gold) to 99.99% (for several commodities), while for futures price volatility, α+β varied from 75.95% (for nickel) to 99.99% (for several commodities). Thus, both spot and futures price volatility were significantly responsive to shocks. Effect of trading volume: The effect of trading volume on commodity futures price volatility was tested using an augmented GARCH(1, 1) model. The results are shown in the following table. commodity F-statistic p-value GOLD CRUDE OIL COPPER

9 The results indicate that the effect of trading volume on futures price volatility was significant for all three commodities (gold, crude oil and copper). Effect of inflation: The effect of inflation on commodity futures price volatility was also tested using an augmented GARCH(1, 1) model. The results are shown in the following table. commodity F-statistic p-value GOLD CRUDE OIL NATURAL GAS PEPPER The results indicate that inflation had a significant effect on the crude oil futures price volatility, but no significant effect on futures price volatility of gold, natural gas, and pepper. DISCUSSION The study explores commodity prices from several different angles. First, there is the possibility of lead-lag relationships between commodity spot and futures prices, as seen from the similarity of their trend patterns. Yang et al (25) had examined such lead-lag relationships for agricultural commodities, and had found that changes in futures trading volume uni-directionally causes increase in spot price volatility. Further studies can address the issue of spot-futures volatility effects for other commodities. Another possibility is that of inter-relationship of spot and futures prices and trading volume, for the same commodity, and between substitute and/or complementary commodities. Particularly interesting is the dynamics between gold & silver, crude oil & natural gas, the base metals, and agricultural commodities. Many studies have analysed the effect of futures price volatility on spot price volatility (Kamara, 1982; Antoniou and Foster, 1992; and Gulen and Mayhew, 2), and some studies have addressed inter-commodity volatility impacts (for example, Pindyck, 24, studying the behavior of natural gas and crude oil volatility). Again, there is vast scope for further research in this area. The GARCH modelling of commodity price volatility yielded some mixed results. The ARCH and GARCH coefficients for spot prices were close to 2% and 8%, respectively, except for the GARCH coefficient of gold spot prices, which was considerably lower (48.52%), suggesting that gold spot prices had lower volatility persistence than any of the other commodities. However, the ARCH and GARCH coefficients for futures prices were very erratic. The ARCH coefficients of channa futures (.%), nickel futures (.7%), and silver futures (.42%) were considerably lower than that of other commodities, while the ARCH coefficient of pepper futures (39.34%) was considerably higher. The GARCH coefficient of pepper futures (52.8%) was considerably lower than that of other commodities, while the GARCH coefficient of silver futures (97.29%) was considerably higher. Overall, the volatility persistence of most commodity futures prices was 9% and above, except for nickel futures (75.95%) and channa futures (82.8%). However, the long-run mean returns and long-run volatility estimates from the GARCH model do not seem congruous. In particular, the long-run mean returns estimate for tin spot prices (3.95%) is extremely high. Also, the long-run volatility of channa spot prices (6.32%) and natural gas spot prices (3.8%) is considerably high. This suggests that the GARCH formulation used may not be appropriate for Indian commodity markets. There is scope for further research focusing on the appropriateness of 8

10 more sophisticated models for Indian commodity markets, and perhaps for specific commodities and/or commodity classes. Another area explored in the study is the impact of trading volume and inflation on commodity price volatility for selected commodities. While trading volume was found to have significant impact on volatility, inflation was found to have significant impact on crude oil price volatility only. However, the results may depend on the GARCH formulation used, and would thus have to be investigated after the appropriate GARCH model is determined. The study has some limitations. The research period selected for the study was 23-11, which includes the turbulent period of 27-8, the aftermath of the global financial crisis, which witnessed some unusual volatility. This may have affected the results. The sample selected for the study was small, only twelve commodities from the available fifty-nine. Also, the factor testing was performed for only a few of the commodities. There is vast scope to extend the study to include more commodities, particularly agricultural commodities, and analyse the impact of more macroeconomic factors on commodity price volatility. REFERENCES J. C. Hull, Options, Futures and Other Derivatives Bollerslev, T. (1986), Generalised Autoregressive Conditional Heteroskedasticity, Journal of Econometrics, vol. 31, pp Kamara, A. (1982), Issues in Futures Markets: A Survey, Journal of Futures Markets, vol. 2, pp Antoniou, A. and Foster, A.J. (1992), The Effect of Futures Trading on Cash Price Volatility: Evidence for Brent Crude Oil Using GARCH, Journal of Business Finance & Accounting, vol. 19, pp Gulen, H. and Mayhew, S. (2), Stock Index Futures Trading and Volatility in International Equity Markets, Journal of Futures Markets, vol. 2, pp Yang, J., Balyeat, R.B., and Leatham, D.J. (25), Futures Trading Activity and Commodity Cash Price Volatility, Journal of Business Finance & Accounting, vol. 32, Issue 1/2, pp Ates, A. and Wang, G.H.K. (27), Price Dynamics in Energy Spot and Futures Markets: The Role of Inventory and Weather, presented at Financial Management Association Annual Meeting. Pindyck, R. (24), Volatility in Natural Gas and Oil Markets, The Journal of Energy and Development, Vol. 3, No. 1, pp