INTRADAY AND END-OF-DAY PRICE BEHAVIORS OF AGRICULTURAL COMMODITY DERIVATIVES

• Sponsoring Institution: National Institute of Food and Agriculture
• Project Status: COMPLETE
• Funding Source: HATCH
• Reporting Frequency: Annual
• Accession No.: 1008150
• Project Start Date: Oct 1, 2015
• Project End Date: Oct 21, 2017
• Program Code: [(N/A)]- (N/A)

Recipient Organization
SOUTH DAKOTA STATE UNIVERSITY
PO BOX 2275A
BROOKINGS,SD 57007

Performing Department
Economics

Non Technical Summary
Agricultural commodity market participants, including farmers and agribusinesses (elevators and food processors), often employ agricultural commodity futures and options to manage price changes, or price risk. Market participants buy or sell futures contracts to lock in a price for some future date, thus protecting themselves against price fluctuations. For instance, corn farmers may sell corn futures contracts in anticipation of harvest. Similarly, food processors who are concerned about price increases can use futures contracts to fix their input costs. Market participants may also buy or sell options on commodity futures to mitigate the adverse effect of price change. Depending on their view of production outlook or market conditions, they can use call or put options to set a price ceiling or floor, or to reduce rollover risk from one month to another, or to seek reward from dramatic price changes etc.Price risk can be small random or unpredictable large changes in price. Market participants have a better understanding of the small random component (or price diffusion), but not the large unpredictable component, or price jumps. The commodity bubble/burst around 2008 and the unprecedented drought in 2012 highlight the importance of risk management under price jumps in agricultural commodity markets, and more fundamentally the underlying price dynamics. Price jumps have significant impact on farmers and agribusinesses' livelihood. Therefore, detection of unusual price jumps is of practical importance to market participants. With proper account for price jumps we will develop more realistic models for commodity futures and options that guide farmers and agribusinesses on how much they should pay. Finally, market participants will decide when to trade futures and options. An important aspect of trading is the cost (in addition to commissions) to participate in the market: the so-called bid-ask spread. The project will shed light on how the bid-ask spreads of agricultural commodity futures and options behave at intraday and daily intervals and what factors contribute to such spreads.

Animal Health Component

50%

Research Effort Categories

Basic

50%

Applied

50%

Developmental

0%

Classification

Knowledge Area (KA)	Subject of Investigation (SOI)	Field of Science (FOS)	Percent
603	1510	3010	50%
603	1820	3010	50%

Knowledge Area
603 - Market Economics;

Subject Of Investigation
1510 - Corn; 1820 - Soybean;

Field Of Science
3010 - Economics;

Keywords

price jumps

jump detection

intraday price

levy process

option pricing

corn

soybeans

Goals / Objectives
Goal:The overall goal of this project is to address the scientific and practical needs of developing flexible models for agricultural commodities that are robust to different market environments at intraday and daily time intervals.Objectives: The proposed project has the following five objectives.Objective 1: to describe the statistics of commodity price movement based on historical intraday and daily data;Objective 2: to document jump behavior in the agricultural commodity market using intraday transaction data;Objective 3: to solve for derivative prices and develop hedging strategies with jumps based on the empirical evidence from the previous stage;Objective 4: to determine the bid-ask spread and price discovery of agricultural futures and options using intraday data;Objective 5: to derive the implications for risk management practices and analyze the relevance of these practices to farmers and agricultural businesses in South Dakota.We will focus on corn and soybean futures and options traded in the CME Globex electronic market. The empirical evidence for jumps in Objective 2 will inform the subsequent model building in Objective 3. Over the duration of the project, at least two graduate students will be involved in the research on selected commodities, which will serve as the student's master theses. Underserved and minority students will be given priority to work on and/or get trained from the project. Collaborators both from the university and outside the university will be invited to bring in extra expertise. External funding from both the government agencies, such as Risk Management Agency of USDA, and other organizations that see the potential of the research, such as commodity processors, will be sought to implement and expand the project.

Project Methods
This research examines the evidence for price jumps in agricultural commodities, and the price behavior of their futures and options at intraday and daily intervals. Regarding price jumps, little published evidence exists related to jump detection at the intraday level in agricultural commodities, despite some scant evidence of price jumps based on parametrical models of end-of-day options (Koekebakker and Lien 2004; Schmitz et al 2014). We will apply non-parametric measures to detect jumps in agricultural commodity futures, including corn and soybeans, at the intraday level. Regarding the daily price behavior of futures and options, the existing models for agricultural commodity options lacks the flexibility of coping with frequent small and infrequent large jumps simultaneously. We propose a Levy-based options pricing model that can accommodate both types of jumps. Regarding intraday price behavior of futures and options, we plan to contribute to the literature by characterizing intraday, instead of aggregated daily, bid-ask spread and its determinants for an expanded list of agricultural commodities: corn and soybeans, during and after the financial crisis in 2008. More detailed description of methods is provided in the attached proposal.

Progress 10/01/15 to 10/21/17

Outputs
Target Audience:The target audiences for the project include farmers, agribusinesses, agricultural investors, policy makers, students and researchers in South Dakota and other states that produce similar commodities. The on-campus audiences, such as students, visitors from the groups of farmers, agribusinesses and policy makers, are engaged through First Dakota National Bank eTrading Education Lab and classroom presentations. The off-campus audiences, such as farmers, agricultural investors and researchers, are reached through SDSU Economics Staff Papers, Economics Commentators, and professional conferences. Financial and economic researchers are reached through professional papers and journal articles. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?Four graduate students and five undergraduate studentswere employed by First Dakota National Bank eTrading Education Lab, for which PI is a leader, to support students learning of agricultural commodity markets at South Dakota State University. The skills learned and developed in the lab as assistant included proficient use of a host of software programs, such as Bloomberg terminal/database, ThinkorSwim trading software, spreadsheet and etc., presentations to various audience such as students, parents, lab donors, legislative staff, and other lab visitors, interpersonal communications, and time management skills. Yu Chen, MS Economics, was a lab TA from August 2015 to December 2016. Chad TeSlaa, MS Economics, was a lab TA from August 2015 to May 2017. Nikita Medvedev, MS Economics, is currently a lab TA since August 2017. Richard Mulder, MS Economics, is currently a lab TA since August 2017 as a graduate student and was a lab assistant from January 2017 to May 2017 as an undergraduate student. Justin Price, BS Economics-Business, was a lab assistant from August 2015 to May 2017. Jessica Boesch, BS Economics, is currently a lab assistant since May 2017. Charles Stephenson, BS Economics-Business, is currently a lab assistant since May 2017. Pierce Plucker, BS Economics-Business, is currently a lab assistant since May 2017. How have the results been disseminated to communities of interest?The results were published in two research articles at quality peer-reviewed academic journals including Journal of Empirical Finance and Journal of Accounting and Finance. All research papers were also presented at a professional conference and/or a university seminar. What do you plan to do during the next reporting period to accomplish the goals? Nothing Reported

Impacts
What was accomplished under these goals? Objective 1: to describe the statistics of commodity price movement based on historical intraday and daily data (100% complete); We examined daily futures prices of corn, soybeans and wheat and commodity-based ETPs (exchange traded products) from 2007 to 2016. Futures price data were obtained from commodity exchange (Chicago Mercantile Exchange) and ETPs price data were from stock exchanges (New York Stock Exchange and NASDAQ). We focused our research on the co-movement of commodity prices and volatility connections between exchanges. Empirical results based on statistical models (vector-error-correction (VEC) model with BEKK GARCH) showed that (1) the contribution of ETPs to price discovery was much lower than that of the underlying futures market; (2) there was bidirectional volatility spillover between the ETP markets and their underlying commodities markets. We analyzed the intraday futures trade and quote data of corn, soybeans and wheat from 2008 to 2015. A downward price trend during the sample period was identified. Another finding is that trading volumes at the 2-min interval before 2012 were larger than after 2012, an evidence of trade fragmentation over time. Objective 2: to document jump behavior in the agricultural commodity market using intraday transaction data (80% complete); We computed realized volatility (RV) and bi-power variation (BV) of corn futures prices using intraday transaction data at 5-minute interval from 1987 to 2010. We found that RV is systematically higher than BV, implying positive price jumps over time. We identified seven major price jumps, including drought in 1988 and 1996, hot and dry weather in 1991 and 2002, accommodative weather in 1998 and 1999, and the burst of commodity bubble in 2008. Nearly all significant price jumps were attributed to extreme weather or macroeconomic events. We further proposed a jump diffusion model to forecast realized volatility by adjusting model-free implied volatility for risk premium. We compared to other existing models and found that the adjusted volatility measure is an unbiased forecast of realized volatility, and is informationally efficient and has better predictive power than alternative measures. This research was published in a high quality journal. Additional tests for price jumps based on a new dataset (2008-2015) were not completed due to the early termination of the overall AES project. Objective 3: to solve for derivative prices and develop hedging strategies with jumps based on the empirical evidence from the previous stage (50% complete); We developed two preliminary mathematical models that accommodate price jumps. One model with Poisson-type price jumps was calibrated to the market data and the results supported the price jumps. The other newly proposed model is based on the Levy process, which allows for infinite number of jumps. This alternative model has yet to be applied to market data. The additional empirical testing was not completed due to the early termination of the overall AES project. Objective 4: to determine the bid-ask spread and price discovery of agricultural futures and options using intraday data (100%); We analyzed the price impact of trades in the electronic corn, soybeans and wheat futures markets. The research was presented at the NCCC-134 conference on "Applied Commodity Price Analysis, Forecasting and Market Risk Management" in April 2017. Using the CME intraday electronic trade and quote data from 2008 to 2015, we found support for square-root temporary impact of trading volume that lasts 10 minutes for corn and wheat, 16 minutes for soybeans. We also found evidence for post-trade permanent impact with a decaying effect in the form of -1/2 power of time. The permanent impact lasted 8 minutes for corn and wheat, 6 minutes for soybeans. Objective 5: to derive the implications for risk management practices and analyze the relevance of these practices to farmers and agricultural businesses in South Dakota (100%). We tested the effectiveness of the accumulator as a risk management tool for agricultural (corn and soybeans) producers. The research was presented at the NCCC-134 conference on "Applied Commodity Price Analysis, Forecasting and Market Risk Management" in April 2017. The accumulator contracts were relatively new, yet potentially important risk management tool in the agricultural industry. Using historical data from 2009 to 2017, we found that the producer accumulator portfolio offered corn and soybean producers the best risk adjusted return to hedge production during this time-frame, relative to eight alternative marketing strategies. Our findings suggest producers to implement the producer accumulator during non-growing season. OVERALL IMPACT My research on the inter-commodity market linkage confirm that the futures market is still the dominant venue for price discovery, although the financialization of commodity markets via Exchanged Traded Products is also relevant. It reaffirms the importance of futures markets for the agricultural industry, both agricultural producers and agribusinesses. My research on intraday price behavior in grains markets enhances our understanding the cost and time dimension of liquidity from both trading and regulatory perspectives. The empirical evidence of price jumps found in my research highlights the importance of new risk management tool. My research on the producer accumulator recommends best practices of using such contracts to reduce risk for farmers and agricultural businesses in South Dakota.

Publications

• Type: Conference Papers and Presentations Status: Published Year Published: 2017 Citation: Wang, Z., Mishra, S. and Elliott, L. (2017). Trade Impact in the Electronic Grain Futures Markets. NCCC-134 Conference on Applied Commodity Price Analysis, Forecasting and Market Risk Management. St. Louis, Missouri, July 1, 2017.
• Type: Conference Papers and Presentations Status: Published Year Published: 2017 Citation: TeSlaa, C., Elliott, M., Elliott, L. and Wang, Z.(2017). Performance of the Producer Accumulator in Corn and Soybean Commodity Markets. NCCC-134 Conference on Applied Commodity Price Analysis, Forecasting and Market Risk Management. St. Louis, Missouri, June 1, 2017.
• Type: Theses/Dissertations Status: Accepted Year Published: 2017 Citation: TeSlaa, C. (2017). Performance of Producer Accumulator Contract in Corn and Soybean Markets. MS in Economics Thesis, South Dakota State University.
• Type: Theses/Dissertations Status: Accepted Year Published: 2016 Citation: Chen, Y. (2016). Price Discovery and Volatility Spillover Effects: The Agricultural ETPs and Their Underlying Commodities. MS in Economics, South Dakota State University.
• Type: Journal Articles Status: Published Year Published: 2016 Citation: Graham, A. and Wang, Z. (2016). Volatility Transmission: A Linkage between Grain Markets and Food Companies. Journal of Accounting and Finance, 16(4), 136-148.
• Type: Journal Articles Status: Published Year Published: 2015 Citation: Wu, F., Meyers, R., Guan, Z. and Wang, Z. (2015). Risk-adjusted implied volatility and its performance in forecasting realized volatility in corn futures prices, Journal of Empirical Finance, 34, December 2015, 260-274.
• Type: Conference Papers and Presentations Status: Published Year Published: 2015 Citation: Graham, A. and Wang, Z. Volatility Transmission: A Linkage between Grain Markets and Food Companies. Financial Management Association Annual Conference, October 15, 2015, Orlando, FL.

Progress 10/01/15 to 09/30/16

Outputs
Target Audience:The target audiences for the project include farmers, agribusinesses, agricultural investors, policy makers, students and researchers in South Dakota and other states that produce similar commodities. The on-campus audiences, such as students, visitors from the groups of farmers, agribusinesses and policy makers, are engaged through First Dakota National Bank eTrading Education Lab and classroom presentations. The off-campus audiences, such as farmers, agricultural investors and researchers, are reached through SDSU Economics Staff Papers, Economics Commentators, and professional conferences. Financial and economic researchers are reached through professional papers and journal articles. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?Two graduate students and one undergraduate student were employed by First Dakota National Bank eTrading Education Lab, for which PI is a leader, to support students learning of agricultural commodity markets at South Dakota State University. They all obtained professional presentation skills and lab management skills. Yu Chen (MS Economics, 2015-present) made substantial progress on his thesis on price discovery of agricultural commodity futures; learned data processing and statistical modeling skills; significantly improved his professional writing skills. How have the results been disseminated to communities of interest?The results were published in two research articles at quality peer-reviewed academic journals including Journal of Empirical Finance and Journal of Accounting and Finance. The results were also presented at a professional conference. What do you plan to do during the next reporting period to accomplish the goals? Complete two research proposals newly accepted by NCCC-134 conference and seek possible publication at peer reviewed journals; Present research results to both academic conference and seminar/workshop that are of interest to stakeholders; Advise students on agricultural economics and finance research.

Impacts
What was accomplished under these goals? Objective 1: Describe the statistics of commodity price movement based on historical intraday and daily data (60% complete). We examined daily futures prices of corn, soybeans and wheat and commodity-based ETPs (exchange traded products) from 2007 to 2016. Futures price data were obtained from commodity exchange (Chicago Mercantile Exchange) and ETPs price data were from stock exchanges (New York Stock Exchange and NASDAQ). We focused our research on the co-movement of commodity prices and volatility connections between exchanges. Empirical results based on statistical models (vector-error-correction (VEC) model with BEKK GARCH) showed that (1) the contribution of ETPs to price discovery was much lower than that of the underlying futures market; (2) there was bidirectional volatility spillover between the ETP markets and their underlying commodities markets. The practical implication of the finding is that the futures market is still the dominant venue for price discovery, although the ETP market is relevant. The intraday price behavior was investigated separately and the accomplishment related to such research is described in combination with Objective 2 as follows. Objective 2:Document jump behavior in the agricultural commodity market using intraday transaction data (40% complete). We computed realized volatility (RV) and bi-power variation (BV) of corn futures prices using intraday transaction data at 5-minute interval from 1987 to 2010. We found that RV is systematically higher than BV, implying positive price jumps over time. We identified seven major price jumps, including droughts in 1988 and 1996, hot and dry weather in 1991 and 2002, accommodative weather in 1998 and 1999, and the burst of commodity bubble in 2008. Nearly all significant price jumps were attributed to extreme weather or macroeconomic events. We further proposed a jump diffusion model to forecast realized volatility by adjusting model-free implied volatility for risk premium. We compared to other existing models and found that the adjusted volatility measure is an unbiased forecast of realized volatility, and is informationally efficient and has better predictive power than alternative measures. This research was published in a high quality journal. Objective 3:Solve for derivative prices and develop hedging strategies with jumps based on the empirical evidence from the previous stage (10% complete). We developed preliminary mathematical models that accommodate price jumps. The models have yet to be applied to market data. Further modeling and empirical testing will be conducted in the coming years. Objective 4: Determine the bid-ask spread and price discovery of agricultural futures and options using intraday data (20%). We proposed to study the price impact of trades in the electronic corn, soybeans and wheat futures markets. The research proposal has been accepted by NCCC-134 on "Applied Commodity Price Analysis, Forecasting and Market Risk Management" for presentation in April 2017. We employed trade and quote data stamped to the second from 2008 to 2015 obtained from the CME Group for preliminary analysis. Early results pointed to both concave and convex impacts of trades. The proposed research will help understand the cost and time dimension of liquidity from both trading and regulatory perspectives. Objective 5:Derive the implications for risk management practices and analyze the relevance of these practices to farmers and agricultural businesses in South Dakota (10%). We proposed to test the effectiveness of the accumulator as a risk management tool for agricultural producers, corn and soybeans in particular. The accumulator contracts were relatively new, yet potentially important risk management tool in the agricultural industry. The research proposal has been accepted by NCCC-134 for presentation in April 2017. The research results will inform best practices of using the accumulator contracts for risk management by farmers and agricultural businesses in South Dakota.

Publications

• Type: Journal Articles Status: Published Year Published: 2015 Citation: Wu, F., Meyers, R., Guan, Z. and Wang, Z. (2015). Risk-adjusted implied volatility and its performance in forecasting realized volatility in corn futures prices, Journal of Empirical Finance, 34, December 2015, 260-274.
• Type: Journal Articles Status: Published Year Published: 2016 Citation: Graham, A. and Wang, Z. (2016). Volatility Transmission: A Linkage between Grain Markets and Food Companies. Journal of Accounting and Finance, 16(4), 136-148.
• Type: Conference Papers and Presentations Status: Published Year Published: 2015 Citation: Graham, A. and Wang, Z. Volatility Transmission: A Linkage between Grain Markets and Food Companies. Financial Management Association Annual Conference, 2015, Orlando, FL.
• Type: Theses/Dissertations Status: Under Review Year Published: 2016 Citation: Chen, Y. (2016). Price Discovery and Volatility Spillover Effects: The Agricultural ETPs and Their Underlying Commodities. MS in Economics, South Dakota State University.