Hedging Effectiveness of Constant and Time-varying Hedge Ratios using Futures Contracts: The Case of Ontario and Alberta Feedlot Industries

Transcription

1 Hedging Effectiveness of Constant and Time-varying Hedge Ratios using Futures Contracts: The Case of Ontario and Alberta Feedlot Industries by Di Ai A Thesis presented to The University of Guelph In partial fulfilment of requirements for the degree of Master of Science in Food, Agricultural and Resource Economics Guelph, Ontario, Canada Di Ai, August, 2012

2 ABSTRACT HEDGING EFFECTIVENESS OF CONSTANT AND TIME-VARYING HEDGE RATIOS USING FUTURES CONTRACTS: THE CASE OF ONTARIO AND ALBERTA FEEDLOT INDUSTRIES Di Ai University of Guelph, 2012 Advisor: Professor Getu Hailu This thesis demonstrates that optimal hedge ratios using futures contracts for Ontario and Alberta feedlot operators were low for live cattle and feeder cattle, and shows that the risk reduction from using US based live and feeder cattle futures was lower than 30% in most cases. Abstracting from the mean variance theoretical framework, constant optimal hedge ratios were estimated using OLS and SUR models. Time-varying optimal hedge ratios were estimated by GARCH models and the rolling window technique. Hedging effectiveness was measured by the variance reduction of hedged portfolios compared with a no-hedging position. Results suggested that hedging in the CME futures market reduced corn price variation by more than 50%. However, the optimal hedge ratios for cattle and barley in general were found to be low. Results also indicated that time-varying hedge ratios eliminated more risk than constant hedge ratios at the cost of frequently adjusting hedging positions. Higher optimal hedge ratios were obtained in the pre-bse period than in the post-bse period in both the commodity market and the currency market for joint hedging live cattle and the exchange rate. Both time-varying hedging and joint hedging strategies may generate additional transaction costs, which have negative impacts on the benefit from the potential higher risk reduction.

3 ACKNOWLEDGEMENTS I would never have been able to complete this thesis without the guidance of my advisory committee members, the help from my fellow classmates, and the support from my family and friends, which I would like to take the time and express my gratitude. First and foremost, I would like to thank my advisor, Dr. Getu Hailu, in every possible way for his guidance, caring, patience, and providing me with an excellent atmosphere for doing research. I have learned so much for the past two years, which not only about the way of doing academic work but also about the attitude toward life. His mentorship and friendship have extended more than a thesis during this program, and have had a deep impact on my personal development now and into the future. I am also indebted to all the faculty and staff in the FARE department, for the guidance and help throughout my coursework and completion of my thesis. I would especially like to thank my advisory and examination committees, Dr. Alan Ker, Dr. Alfons Weersink, Dr. Brady Beaton and Dr. Richard Vyn, for all their constructive comments and suggestions; thank Dr. John Cranfield for his guidance particularly in the early stage of my graduate study; thank Dr. Yiguo Sun from Department of Economics for answering my econometric questions; thank Mr. Curtis Boyd and Dr. Michelle Edwards for helping me with the data collection. Throughout my degree, I have relied on my fellow classmates of FARE for advice and encouragement. I really appreciated the support and friendship from them. Finally, I would like to thank my parents, my family and my friends for being by my side all the time. Without the support from you, I would have never been able to finish the thesis on my own. Your constant encouragement and love help me pursue my dream with passion and become a better person. iii

4 Table of Contents List of Tables List of Figures vi viii 1 Introduction Motivation The Economic Problem The Economic Research Problem Purpose and Objectives Objectives Outline of this Study Industry Background The Canadian Cattle Indstury Canada s Cattle Inventory and Trade Ontario and Alberta Beef Industries Cattle and Feed Grain Price Volatilities Cattle Feeding Practice Market Risks A Brief Review of Canadian Agricultural Policy Literature Review Hedging Mechanism and Futures Contracts Theoretical Framework Empirical Models Hedging Effectiveness Innovation of the Study Theory of Hedging Theoretical Framework Mean Variance Framework Hedge Output Price Only without Export Hedge Output Price Only with Export Hedge Output Price Only without Export but Exchange Rate is Uncertain Hedge Output Price and One Input Price without Export Hedge Output Price and Two Input Prices without Export Empirical Framework and Data Description Empirical Models OLS Method SUR Method iv

5 5.1.3 GARCH Method Diagnostic Tests Unit Roots Tests Cointegration Test ARCH Effects Test Hedge Effectiveness Data Description Cattle Production Period Empirical Implementation For Single Commodity Hedging Diagnostic Tests Live Cattle Feeder Cattle Feed: Corn and Barley Empirical Models OLS Method SUR Method GARCH Method Hedging Effectiveness Simultaneous Hedging in Commodity and Currency Futures Markets The Empirical Model For Simultaneous Hedging Diagnostic Tests for Exchange Rate (USD/CAD) Descriptive Statistics Unit Roots Tests for Exchange Rate Series Cointegration Test for Exchange Rate Series ARCH-LM Test for the First Difference of Exchange Rate Series Implementation of the Empirical Model Sensitivity Analysis of λ Futures Contracts Conclusions and Discussions Introduction Summary of Results Policy Implications Limitations of this Study and Suggestions for Future Research Bibliography 96 Appendices 100 Appendix A Appendix B Appendix C v

6 List of Tables 2.1 Inventories of Cattle Commodities Futures Contracts Description of Variables Descriptive Statistics of Price ( pre-bse / whole time / post-bse ) Production and Hedging Period Descriptive Statistics for Return Unit roots tests for live cattle prices Unit roots tests for live cattle prices and their first differences Unit roots tests for raw prices and their first differences Johansen s test for live cattle price series Unit roots test for live cattle return series P-Values of ARCH-LM Test for live cattle return series Unit roots tests for feeder cattle prices Unit roots tests for feeder cattle prices and their first differences Unit roots tests for feeder cattle prices and their first differences Johansen s test for feeder cattle price series Unit roots test for feeder cattle return series P-Values of ARCH-LM Test for feeder cattle return series Unit roots tests for corn prices and their first difference Unit roots tests for barley prices and their first differences Johansen s test for corn and barley price series Unit roots tests for corn and barley return series P-Values of ARCH-LM Test for corn and barley return series OLS Based Optimal Hedge Ratios SUR Based Optimal Hedge Ratios Hedging Effectiveness - Variance Reduction Description of Variables Descriptive Statistics for Exchange Rate Unit roots tests for exchange rate Unit roots tests for exchange rate Unit roots tests for exchange rate Johansen s test for exchange rate series P-Values of ARCH-LM Test for exchange rate Mean of Dynamic Commodity and Currency Hedge Ratios A.1 Descriptive Statistics of Price Data vi

7 A.2 Unit roots tests for live prices and their first differences A.3 Johansen s test for live cattle price series A.4 Unit roots test for live cattle return series A.5 Unit roots tests for feeder cattle prices and their first differences A.6 Johansen s test for feeder cattle price series A.7 Unit roots test for feeder cattle return series A.8 OLS Based Optimal Hedge Ratios A.9 SUR Based Optimal Hedge Ratios A.10 Hedging Effectiveness: Variance Reduction A.11 Unit roots tests for spot and futures exchange rate and their first difference A.12 Johansen s test for exchange rate series A.13 P-Values for ARCH-LM Test A.14 Mean of Dynamic Commodity and Currency Hedge Ratios A.15 Dynamic Approaches Hedging Effectiveness Comparison B.1 Hedging Effectiveness for GARCH vii

8 List of Figures 2.1 Canadian Cattle and Calves Inventory International Export International Import Ontario and Alberta Live Cattle Spot Price Ontario and Alberta Feeder Cattle Spot Price Ontario Corn Spot Price Alberta Barley Spot Price Canadian dollars per US dollar Optimal Hedge Ratios for Live Cattle Optimal Hedge Ratios for Feeder Cattle Optimal Hedge Ratios for Corn and Barley Optimal Hedge Ratio (rolling window) for (λ = 0.2) Optimal Hedge Ratio (GARCH) for (λ = 0.2) Optimal Hedge Ratio (rolling window) for (λ = 0.2) Optimal Hedge Ratio (GARCH) for (λ=0.2) Sensitivity of λ using ON pre-bse data A.1 Optimal Hedge Ratios for Live Cattle during A.2 Optimal Hedge Ratios for Feeder Cattle during A.3 Optimal Hedge Ratio (rolling window) for (λ = 0.2) A.4 Optimal Hedge Ratio (ARCH) for (λ=0.2) B.1 Optimal Hedge Ratios for Live Cattle during viii

9 B.2 Optimal Hedge Ratios for Live Cattle during B.3 Optimal Hedge Ratios for Feeder Cattle during B.4 Optimal Hedge Ratios for Feeder Cattle during B.5 Optimal Hedge Ratios for Corn and Barley during ix

10 Chapter 1 Introduction 1.1 Motivation One of the central questions in agricultural economics is why producers are not actively engaged in futures markets to manage price risk as predicted by theoretical and empirical literature (e.g., Simmons, 2002; Pannell et al, 2008; Deane and Malcolm, 2006). It is commonly recognized that producers can either use futures markets for pricing cash market transactions (e.g., Working 1948, Wiese 1978, Lake, 1978) or mitigating price risks (e.g., Lubulwa et al, 1996). The literature indicates that hedging with futures contracts will benefit agricultural producers by offsetting their price risk (e.g., Anderson and Danthine, 1983; Lapan and Moschini, 1994). Although the theoretical implications of hedging with futures contracts are well known (e.g., Jarrow and Oldfield, 1981; Pennings, 2000), the empirical evidences on the effectiveness of hedging with futures contracts are mixed (e.g., Carter and Lyons, 1985; Novak and Untershultz, 1996). With the encouragement from governments to proactively manage risks by using market-based risk management tools (e.g., Agricultural Policy Framework, 2002; Growing Forward, 2007), an important research question in addressing price risk management issues is whether market instruments, such as futures contracts, can be effectively used to manage price risk, and whether the futures market contributes positively to overall price discovery in the Canadian beef industry. This thesis determines optimal hedging ratios by using US futures contracts, and examines their effectiveness for Ontario and Alberta feedlot industries. 1

11 The cattle industry represents a major sector of economic activity in Canadian agriculture. In January 2012, Canadian cattle inventory was 12,515,000 head. Within Canada, Alberta is the largest beef and cattle production province, which contributes nearly 40% of the total inventory. Alberta is followed by Saskatchewan and Ontario, which account for approximately 20% and 14%, respectively, of the total Canadian inventory. In 2009, 2010, and 2011, cattle cash receipts contributed 5.8 billion, 6.15 billion, and 6.49 billion dollars, respectively, to Canada s economy, accounting for almost 15% of total farm cash receipts (Canfax, Statistics Canada). The revenue from beef production contributed more than 20 billion dollars per year during the same period. Canada was the ninth largest production nation with 2.3% of the world s beef supply in Canada had 1.2% of the world s 992 million cattle and ranked 11th in the list of world s largest cattle inventories in 2011 (U.S. Department of Agriculture). The cattle industry is divided into three main sectors: cow-calf, feedlot and packing. At the cow-calf stage, the primary function is to produce calves from breeding stock. At the feedlot stage, the primary function is to grow feeder cattle into live cattle. At the packing stage, the primary function is to slaughter cattle and market beef to retailers (Grier, 2005). This study focuses on the feedlot stage. In feedlots, producers revenues are generated from the sale of finished cattle (i.e., live cattle) to packers. Primary cost factors are feed grain and the feeder cattle that are placed on feed grain. The costs of feeder cattle and feed grain roughly account for 70% and 15%, respectively, of the total costs (Grier, 2005). However, both the output (i.e., live cattle) and input (i.e., feeder cattle and feed grain) prices have been through large fluctuations over the past decade; in particular, cattle price variation increased after the BSE outbreak in 2003, which will be showed in Figure 2.4 to 2.7 in Chapter 2. 1 Producers can use risk management tools to deal with price volatility caused by market factors. In terms of risk management, hedging is a very popular tool to manage market risks, and research conducted on hedging strategies for different products is extensive. Although there is much literature on using hedging to manage price risks in the beef industry, most of it is based on the US market (e.g., Leuthold and Peterson, 1987; Noussinov and Leuthold, 1999). There are only few studies examining hedging cattle in Canada, and their results are mixed (e.g., Carter and Lyons, 1985; 1 Catastrophic events like BSE are beyond producers control, which are usually addressed by government support programs. 2

12 Novak and Untershultz, 1996). Due to the development of futures markets in recent years, offshore hedging has become commonplace. US futures markets can be an option for Canadian producers to manage price and exchange rate risks. Because the beef industry has undergone a structural change since the outbreak of BSE and a higher USD/CAD exchange rate variability since 2003 (Frank et al, 2011), this study uses more recent data to examine hedging Ontario and Alberta cattle and feed grain prices as well as exchange rate in the US futures market, in particular to compare the hedging effectiveness before and after the BSE outbreak in The Economic Problem The volatility of cattle and feed grain prices may cause significant economic losses to producers when the risks are not well managed. Additionally, considering the large trade with the U.S. (Canfax, Statistics Canada, 2011), the appreciation of the Canadian dollar compared to the US dollar since 2003 has had a negative impact on exports and has further decreased producers revenue. The price and exchange rate risks, like other market risks, are caused by the change in supply and demand (Grier, 2005), which are beyond producers control with their husbandry practice in feedlots. With a tendency toward a decline in government support programs (e.g., direct subsidy programs) (Barichello, 1995), producers are forced to identify these risks and use private risk management strategies such as forward and futures contracts to alleviate losses by themselves. The competitive position of the Canadian beef industry partly depends on the ability of producers to manage price and exchange rate risks. Cattle producers and producer associations are the primary owners of this risk management problem. If these risks are managed properly, cattle producers can have relatively stable revenue even during bad years. The Canadian government can indirectly benefit from producers proactive risk management, because it does not have to subsidize a large amount to the beef industry. 3

13 1.3 The Economic Research Problem Cattle producers face price and exchange rate risks, and previous studies have suggested that producers can use futures contracts to manage these risks (e.g., Carter and Loyns, 1985). However, the evidence on the level of optimal hedge ratio and on the effectiveness of using futures market tools for Canadian agricultural commodities in general and the beef industry in particular is limited. The results of the few relevant studies are mixed. For instance, Carter and Loyns (1985) concluded that hedging with futures might not outperform a no-hedging situation. In contrast, Novak and Unterschultz (1996) found that using cattle and exchange rate futures was effective in reducing the price risk for Western feedlots. To investigate whether hedging is a good tool for risk management in Ontario and Alberta feedlots, particularly after the industry went through the structural change in 2003, this study explores hedging cattle, corn and exchange rate in the US futures market as well as hedging barley in Canada s futures market. This study investigates a risk management problem in the Canadian beef industry. It focuses on using hedging to manage price and exchange rate risks. Standard futures contracts from the Chicago Mercantile Exchange (CME) are used to hedge cattle (i.e., live cattle and feeder cattle), corn, and exchange rate; futures contracts from the Intercontinental Exchange (ICE Futures Canada) are used to hedge barley. Weekly cattle and feed grain spot price data of Ontario and Alberta feedlots from 1997 to 2010 are obtained from Canfax, Statistic Canada. The exchange rate data during the same time period are obtained from Thomson Financial Datastream. The modeling of risk management strategy is based on the mean variance framework. This research aims to answer the following questions: 1. What are the optimal hedge ratios using CME and ICE futures contracts for Ontario and Alberta feedlots? 2. Do time varying optimal hedge ratios perform better than constant optimal hedge ratios? 3. How much variation can be reduced by hedging in the futures markets compared with a no-hedging position? 4. Are futures contracts effective tools to manage Canadian cattle and feed price risks as well as exchange rate risk? 4

14 1.4 Purpose and Objectives The purpose of this study is to explore whether hedging using US futures contracts is an effective tool to manage price and exchange rate risks in Ontario and Alberta feedlot industries, and to compare the hedging effectiveness of different hedging strategies Objectives 1. To describe the Canadian cattle industry background as well as current price and exchange rate risk management practices in Ontario and Alberta feedlot industries. 2. To document hedging strategies from previous literature and build a theoretical framework. 3. To analyze the properties of time series data used in this study by checking descriptive statistics, stationarity, cointegration and ARCH effects. 4. To obtain the optimal hedge ratios by estimating the OLS, the SUR and the GARCH models as well as using the rolling window technique. 5. To evaluate the effectiveness of hedging strategies by comparing the risk reduction of hedged portfolios with a no-hedging position. 6. To provide feedlot operators with price and exchange rate risk management suggestions under various scenarios. 1.5 Outline of this Study Chapter Two discusses the industry background and Chapter Three reviews previous literature. Theoretical frameworks under different scenarios are built and comparative analysis are illustrated in Chapter Four. Chapter Five introduces empirical models for estimating single commodity optimal hedge ratios and diagnostic tests for time series data. Historical data are used to estimate the empirical models in Chapter Six, and the hedging effectiveness is compared among various hedging 5

15 strategies associated with different models. Chapter Seven incorporates exchange rate into the discussion, and demonstrates simultaneous hedging in the commodity and the currency futures markets. Chapter Eight concludes with a summary of the results from previous chapters, discusses the limitations of this study, and provides suggestions for future research. 6

16 Chapter 2 Industry Background This chapter starts with an introduction of the Canadian cattle industry, and then introduces the background of the Ontario and Alberta cattle industries. It briefly discusses the cattle feeding practice and categorizes various market risks that producers face. A review of relevant agricultural policies is presented at the end of this chapter. 2.1 The Canadian Cattle Indstury Canada s Cattle Inventory and Trade Figure 2.1 shows that the inventory of cattle and calves has been relatively stable over the last forty years. In terms of the last twenty years from early 1990s to 2010, the inventory increased from 11million to 15 million head. There was a slight decline after 2005, but the total inventory was still more than 12 million head by

17 Figure 2.1: Canadian Cattle and Calves Inventory Source: Statistics Canada From the demand side, trade is an important part of Canada s beef and cattle production, in particular with the U.S. The agricultural trade between Canada and the U.S. increased by more than four times between early 1980s and early 2000s. One of the most dynamic, in terms of trade, sub-sectors of agriculture is the livestock and beef industry (Miljkovic and Paul, 2003). According to Red Meat Yearbook, US beef and veal exports to Canada increased from 16,812 thousand pounds of carcass weight in 1983 to 226,325 thousand pounds of carcass weight in 2003, or an increase of more than thirteen times during the twenty-year period (Economic Research Service, U.S. Department of Agriculture). Canada ranked as the 5th or 6th largest beef exporter in the world for the past four years (Canfax, Statistics Canada, 2011). More than 75% of the beef and cattle exports went to the US market, and more than 65% of Canada s imports were from the U.S (Statistics Canada, 2010). On a net basis, Canada is an exporter. Figures 2.2 and 2.3 present the international export and import situations based on a six-month period for Canada as well as Ontario and Alberta separately during the past decade. 8

18 Figure 2.2: International Export Source: Cattle Statistics 2011, Statistics Canada Figure 2.2 showed that there were no exports between July 2003 and June 2005 because of the BSE outbreak in May The ban on Canadian live cattle was not lifted until July 2005 (Statistics Canada). Except for this period, provincial exports were relatively stable: Alberta exported 200 thousand to 300 thousand head, and Ontario exported approximately 100 thousand head each half year. However, the export amount of the whole country changed substantially over the time measured. Canada typically exported about 1.1 million head of cattle each year before the outbreak of BSE (Grier, 2005), and the exports have recovered to more than 500 thousand head each half year since July Two export peaks occurred, during the second half year of 2002 and the first half year of 2008; more than 900 thousand head were exported in each of these time periods. Canada went through an export decline after 2008, and the exports decreased to between 500 and 600 thousand head each half year by

19 Figure 2.3: International Import Source: Cattle Statistics 2011, Statistics Canada Figure 2.3 showed the import situation over the past decade for Canada as well as Alberta and Ontario. For Canada and Alberta, the situation changed drastically as of During 2000 to 2002, Canada imported approximately 120 thousand head in the first half year and at least 180 thousand head in the second half year. Alberta imported at least 45 thousand head in the first half year and double that number for the second half year. From 2003, the imports by Canada and Alberta substantially decreased to less than 30 thousand head for Canada and less than 10 thousand head for Alberta each half year, and the trend continued until On the other hand, Ontario s imports remained relatively stable from 2000 to Approximately 30 thousand head were imported before 2003, and about 15 thousand head were imported each half year after Ontario and Alberta Beef Industries Ontario and Alberta play important roles in cattle production within the Canadian cattle industry. Ontario is the second largest live cattle production province with approximately 20% of Canada s total production (Frank et al, 2011). There are more than 600 thousand head of cattle born in Ontario each year. From the demand side, most cattle are slaughtered within the province rather 10

20 than exported. In terms of trade, the interprovincial imports of cattle are almost 10 times as many as the interprovincial exports. However, the international trade flow is opposite to the interprovincial. Ontario exports much more than it imports from other countries. The difference between international exports and imports has become larger, increasing to over 100 thousand head each year (Cattle Statistics, 2011). Alberta is the largest cattle-producing province in Canada. It leads the nation in cattle and calf inventories with nearly 40% of the national total. Annual exports of Alberta beef and cattle are valued at approximately 1.4 billion dollars (Agriculture and Rural Development Statistics). The majority of export sales of both live cattle and beef from Alberta goes to the United States, which accounts for nearly 80% of all exports. Beef cattle production is Alberta s largest agricultural sector, which contributes more than 30% of Alberta total farm production income (Alberta Cattle Feeders Association). In terms of cattle, they can be categorized into many types based on gender, production stages and other characteristics (e.g., weight). As mentioned previously, this study focuses on feeder cattle and live cattle, which are the stages that are frequently traded at markets and their prices are determined by market factors (Grier, 2005). Ontario s and Alberta s cattle inventories account for more than half of Canada s total inventories. In particular, feeder cattle and live cattle inventories from the two provinces have accounted for more than 75% and 88% of Canada total inventories, respectively, since 2010 (Canfax, Statistics Canada). Table 2.1 shows the latest cattle inventory information for Canada as well as for Ontario and Alberta. Table 2.1: Inventories of Cattle Jan 1, 2012 Percentage July 1, 2011 Percentage Jan 1, 2011 Percentage Canada 12, % 13, % 12, % Ontario 1, % 1, % 1, % Alberta 4, % 5, % 4, % Notes: the unit for cattle is thousand head. Source: Statistics Canada Cattle and Feed Grain Price Volatilities During the process of growing feeder cattle into live cattle, producers need to purchase feed grain for feeder cattle to improve the quality of their meat. The most extensively used feed grain in Ontario is corn and in Alberta is barley. About 60% of the area planted to corn in Canada is located in 11

21 Ontario. Along with being the largest producer of corn, Ontario is the largest corn consuming province in Canada; more than half of corn is used as livestock feed (Statistics Canada). Alberta is Canada s largest barley growing province. About 75% to 80% of the Alberta crop is used as feed and most is for cattle (Department of Agriculture and Rural Development, Government of Alberta). Higher grain prices since late 2007 have had a deleterious effect on producers. Figures 2.4 to 2.7 showed the price trends for cattle and feed grain in the Ontario and Alberta cash markets for the past fourteen years. Neither cattle price nor feed grain price were stable over this time period. Figure 2.4: Ontario and Alberta Live Cattle Spot Price Source: Canfax, Statistics Canada Ontario s and Alberta s live cattle spot prices showed a similar trend over the past fourteen years. Ontario s prices were slightly higher than Alberta s. For both provinces, they were between 70 to 130 dollars per hundred weight (i.e., $/cwt) most of the time. The prices showed an upward trend between 1997 to early The outbreak of BSE dragged the price down to almost $30/cwt in the second half year of 2003, which was equal to only one third of the average price. After the BSE outbreak, the price recovered to approximately $90/cwt on average by

22 Figure 2.5: Ontario and Alberta Feeder Cattle Spot Price Source: Canfax, Statistics Canada In contrast to the huge impact on live cattle, the BSE outbreak had less of an influence on feeder cattle. The price change in 2003 was not as dramatic as for live cattle. Both Ontario s and Alberta s feeder cattle prices increased from 1997 and attained a peak at After 2001, the price started declining and the lowest prices occurred around 2003 to After 2005, the price rose again and stayed at $105/cwt on average. The price variation for feeder cattle was larger than for live cattle except in 2003, and the difference between the highest price and lowest price was almost $100/cwt during the past fourteen years. 13

23 Figure 2.6: Ontario Corn Spot Price Source: Nesstar Web Retrieval, University of Guelph Library In terms of feed grain, Ontario corn prices were between $2.5/bushel and $4/bushel from 1997 to The price decreased after 2005, and hit the bottom at approximately $2/bushel during 2005 to One of the factors that potentially contributed to low corn prices was market-distorting subsidies in other countries, such as the U.S., to encourage over-production (Vyn and Marchand, 2005). Starting from late 2007, international markets for major grains experienced a period of tight supplies, strong demand and high prices (Schnepf, 2008). Agricultural commodity prices rose sharply during 2007 and jumped precipitously in early 2008, and the corn price surpassed $6/bushel in Increasing demand for food from emerging economies (e.g., China, India), competition between biofuels and food production, high fuel prices, and increasing climatic shocks such as droughts and floods are the main contributors to the high grain prices (Schnepf, 2008). The price went back down to $4/bushel after During the past fourteen years, the highest corn price was almost three times as much as the lowest price, and high price volatility was found after

24 Figure 2.7: Alberta Barley Spot Price Source: Alberta Grain Commission Similar to corn prices, barley prices were also volatile over the past fourteen years, and the price volatility has increased since Both Lethbridge barley and Edmonton barley spot prices are used as a proxy for the Alberta barley price. Lethbridge barley had slightly higher prices than Edmonton barley over the time measured, and the difference has become smaller. The first price peak was in 2003, when the barley price reached $200/ton. Afterwards, the price decreased and hit the bottom at approximately $110/ton during 2005 to early Increasing demand for food from emerging economies and competition between biofuels and food production also pushed barley price up around 2008, and the highest price attained more than $250/ton. After 2008, the prices decreased to between $145/ton to $180/ton. Besides volatile prices, cattle producers also face a volatile exchange rate. Because of the key role that the exchange rate plays in cattle and feed grain price discovery in Canada (Grier, 2005), in the trade with the U.S., and in the gain or loss from hedging in commodity futures markets, it is important to study how the exchange rate risk has changed over the time period measured for commodity prices. Figure 2.8 presented the exchange rate of the Canadian dollar compared to the US dollar over the past fourteen years. From 1997 to 2002, the Canadian dollar depreciated slightly, and the value of one US dollar was around 1.5 times as the value of one Canadian dollar. From 2003 to 2008, the Canadian dollar strengthened sharply, and it was at par with the US dollar by 15

25 2008. The Canadian dollar depreciated again for a short time in 2009, and it was back to the same value with the US dollar by Figure 2.8: Canadian dollars per US dollar Source: Thomson Financial Data stream 2.2 Cattle Feeding Practice Beef cattle are cattle raised for meat production. From the time cattle are born till they are ready for slaughter, there are three distinct phases. First, the cow-calf stage covers the period from conception through weaning. Calves are generally weaned between eight to ten months of age. Second, calves are raised for approximately one year on a ranch by a stocker-grower, where they are fed low-cost feeds such as forage crops and roughage. At this stage, the cattle are feeder cattle and are ready for the third stage. Third, cattle are confined in a feedlot and fed high cost feed, such as grain and protein concentrates. The cattle grow and the quality of their meat improves. At the end of this process, feeder cattle have become live cattle and are ready for slaughter (Kolb and Overdahl, 2006). This study focuses on the third phase, which includes two inputs and one output in this procedure. The inputs are feeder cattle and feed grains; specifically corn is chosen as the feed grain for 16

26 Ontario and barley is for Alberta. The output is live cattle. Because cattle are non-storable products, there is a clearly measurable inventory of cattle in feedlots. The inventory is fluid, composed of cattle in many different weight groups and the composition is changing daily. In contrast, the feed grains are storable. Although the majority of beef cattle in Ontario are finished on a corn-based diet and in Alberta are finished on a barley-based diet, producers in both regions may alter the mix of feed grains according to changes in feed prices. Four commodities are hedged in this study. Live cattle and feeder cattle are hedged for both Ontario and Alberta. Corn and barley, as the feed grain inputs, are hedged for Ontario and Alberta, respectively. Because cattle are non-storable and feed grains are storable, the hedging performance can be very different for the two-category products. 2.3 Market Risks In feedlots, producers face different layers of risks, which can be categorized into three types in general. First, normal variations in production, which do not require any policy response and should be directly managed by producers as part of their normal business strategy. Second, catastrophic events, which are infrequent and beyond the capacity of producers or markets to manage, such as BSE, require governments involvement. To deal with these risks, governments usually launch support programs, such as the AgriRecovery program in Canada. Third, market risks, the focus of this study, occur due to the changes in supply and demand, which are beyond the control of producers husbandry skills but they can manage the risks by using market tools. This is where insurance and futures markets come in (Anton, Kimura and Martini, 2011). Market price volatility is an important source of market risk for producers, in particular when prices are low. It has become a major policy concern because of the evolution of global commodity markets (OECD, 2009). Individual farmers cannot influence price at the market level, but they can manage it. In Canadian cattle markets, the first determinant of price is the overall North American market conditions. It is particularly reflected in the US commodity price, which is primarily derived by the balance of supply and demand of beef and cattle in North America. The second key driver of price in Canada is the exchange rate between the Canadian dollar and the US dollar. The third driver is 17

27 the basis risk, which is determined by the cost of transportation, the supply and demand conditions in local markets, etc (Grier, 2007). 2.4 A Brief Review of Canadian Agricultural Policy Over the past century, Canada has operated a variety of agricultural support programs. The current use and expenditure profiles in Business Risk Management (BRM) and regulatory instruments for supply management suggest that the primary objective is profitability (Skogstad, 2011). The objectives for Canada s agriculture have been similar, while the instruments to achieve the objectives have varied over time. From 1940s to early 1980s, the main goals of stabilizing food production while simultaneously preventing domestic price inflation were achieved through regulatory and expenditure instruments. For instance, the federal government made price support under the Agricultural Stabilization Act (ASA) mandatory for producers of nine commodities in It was supplemented by the 1976 Western Grain Stabilization Act (WGSA), which provided prairie grain farmers with a voluntary, contributory scheme to stabilize their net returns from exports of grains. From the mid-1980s to mid-1990s, the advancing competitiveness goals were achieved through market-oriented instruements. For instance, Growing Together was launched in 1989 under the themes of more market responsiveness, greater self-reliance, regional diversity, increased environmental sustainability, food safety as well as quality protection. From 1997 onward, the two policy landmarks were the Agricultural Policy Framework in 2002 and the Growing Forward in These agreements were five-year government commitment of funding, and explicitly linked the goals of business risk management to public goods such as food safety and environmental protection (Skogstad, 2011). The agricultural policy goals include achieving sustainable agriculture growth, fostering rural opportunities, realizing long-term financial security, attaining resource and environmental sustainability, maintaining a safe, high quality food supply, etc (Barichello, 1995). There are different support programs used as instruments to achieve these policy goals. Direct Subsidy Programs, which include various output subsidies and input subsidies (e.g., Western Grain Transportation Act (WGTA), Feed Freight Assistance Program (FFA), Credit Subsidies), have been used in particular at the early stage. For instance, WGTA had its origins back to 1897 and was eliminated in

28 Because the Canadian government intends to reduce direct subsidy, many subsidy programs are currently being terminated or adjusted to be complied with other policies (Barichello, 1995). Another kind of support programs is Safety Net Programs, beginning in 1958 with Agricultural Stabilization Act, which are primary instruments of overall agricultural policy in Canada. They have contributed to a set of larger policy objectives for Canadian agriculture, including helping producers adjust to market signals and managing risks in a non-distorting fashion (Barichello, 1995). Safety net programs in Ontario include: Net Income Stabilization Account (NISA), Crop Insurance (CI), Market Revenue Insurance (MRI) program, Self Directed Risk Management (SDRM) Program, Ontario Farm Income Disaster Program (OFIDP), Research and Development Program (R&D Fund) and Wildlife Damage Compensation (OMAFRA, 2003). In the past decade, the Agricultural Policy Framework was implemented as an inter-government agreement over the period 2003 to Business Risk Management was one of the five pillars of this agreement, which encouraged producers to be proactive to reduce business risks. The Growing Forward agreement, implemented over the period 2008 to 2012, succeeded the Agricultural Policy Framework, still seeking a profitable agriculture sector in managing risks by implementing business risk management programs. These Business Risk Management programs, including AgriInvest, AgriStability, AgriInsurance, AgriRecovery and Advance Payments Programs (APP), have provided protection for different types of losses as well as cash flow options (AAFC, 2008). AgriInvest provides coverage for small declines, AgriStability offers protection from margin declines greater than 15 percent, and AgriInsurance offers protection for production losses related to specific crops or commodities caused by hail, drought, flooding, disease and other factors. AgriRecovery provides a process for governments to quickly determine whether or not assistance beyond existing programming is needed when specific disaster strikes, which is only offered when existing programs cannot respond to the disaster. The Advance Payments Program (APP) provides help to producers who want to market their commodities at a later date when prices are better or if they need additional cash flow (AAFC, 2009). Growing Forward 2 was released this year, effective starting in April Growing Forward 2 aims to help the industry position itself to respond to future opportunities and challenges with an emphasis on industry capacity and self-reliance (AAFC, 2012). Reviewing the policy, the political priority has been given to budget cuts at the federal and provincial levels in order to reduce government deficits and ensure a viable and competitive agricultural sector (Barichello, 1995). The 19

29 government has gradually reduced its direct involvement in subsidy, and has introduced business risk management programs to help producers plan their own risk management strategies since However, for the most part of current programs triggered after a loss has occurred, attention should be given to risk management tools that are more proactive in nature. According to a Managing Risk in Agriculture report released by OECD countries in 2011, the agricultural policy reform process seeks to implement less distorting forms of support while improving farm revenues. However, these reforms have increased the exposure of producers to price risks, particularly where price support has been reduced. In addition, the current programs lack of flexibility for differences in production and provincial realities. The policy trend and the exposure to price risks have contributed to the growing awareness of the need for agricultural risk management for producers at the individual level. In addition, risk managment tools (e.g., futures contracts) can benefit producers indirectly as price discovery instruments. 20

30 Chapter 3 Literature Review Chapter 3 starts with introducing hedging mechanism and futures contracts, and then documents previous literature to summarize theoretical frameworks, empirical models and measurements of hedging effectiveness. It also analyzes the strengths and weaknesses of different frameworks and models, identifies the gap in previous literature, and concludes with the innovations of this study. 3.1 Hedging Mechanism and Futures Contracts Derivative markets have become increasingly important in the world of finance and investments. For people who want to buy or sell assets in the future to trade with each other, futures exchanges provide platforms to make it available worldwide. People using futures exchanges can be geneally categorized into speculators, arbitrageurs and hedgers (Hull, 2005). A person who enters into futures markets can play two roles at the same time. For instance, producers who enter into futures markets may be motivated by hedging risks, and there are speculative opportunities for them to take concurrently. Futures contracts, forward contracts and options are alternatives in futures markets, and they can be used to reduce the risks from unfavourable future movements in commodities prices and foreign exchange rates. Hedging by using futures contracts is based on the principle that cash market price and futures market price tend to move up and down together. The movement is not necessarily identical, but it usually is close enough to lessen the risk of a loss in the cash market by taking an opposite position in 21

31 the futures market. However, with a hedging position, the producer also gives up the opportunity to benefit from a favorable price movement in order to obtain protection against an unfavorable price movement (CBoT, 2004). A perfect hedge is one that completely eliminates the risk, but it is not always the case. Hence previous studies of hedging are actually studies of constructing different hedging strategies to obtain the optimal hedge ratios and examining to what extent risk reduction can be achieved. This study hedges cattle and feed grain prices by using futures contracts without daily settlement. Futures contracts are standardized as to quantity, quality, delivery time and place. Price is the only variable (CBoT, 2004). Except for barley, all the futures contracts are from the Chicago Mercantile Exchange (CME) futures market. The CME Group is the world s most diverse derivatives marketplace offering the widest range of benchmark futures and options products available for hedgers worldwide. The CME futures market is located in the U.S. Canadian producers, who are offshore hedgers, need to take exchange rate into consideration when participating in the US futures market. According to CME futures contracts, the inter-stage between calf and killable cattle is indicated as feeder cattle. Feeder cattle are young animals sent to feedlots for finishing into live cattle, and the latter is the basis of CME live cattle futures contracts. Because both feeder cattle and live cattle are traded in the futures market, the dual contracts traded on cattle raise interesting issues about storage and carrying charges in the futures market. Cattle, meeting the feeder cattle contract specifications can be carried forward and delivered against the live cattle contracts. Cattle gain in value because they gain weight as they mature. The decision to slaughter feeder cattle, or to carry them forward for delivery as live cattle, depends on the spread between the two futures contracts and the cost of feeding the cattle over the period (Kolb and Overdahl, 2006). In Canada, the major futures market is the Intercontinental Exchange (ICE Futures Canada). The ICE Futures Canada was established as the Winnipeg Grain Exchange in 1887, operating as a cash market for wheat, oats and barley. Over years, this commodity exchange expanded into a futures market (Mann, 2010). In this study, live cattle, feeder cattle, corn and exchange rate are traded in the CME, while barley is hedged in the ICE Futures Canada. Barley can also be cross hedged in the CME by using corn futures contracts, in the condition that their price movements are highly correlated. Cross hedging barley was performed by previous studies (Caldwell et al, 1982), and it is examined in this study to compare with hedging barley in the ICE. Table 3.1 presents part 22