Recipient Organization
MONTANA STATE UNIVERSITY
(N/A)
BOZEMAN,MT 59717
Performing Department
Agri Economics
Non Technical Summary
The proposed project will further understanding of the complex, dynamic, and risky market environment faced by agricultural commodity producers, traders, and end-users. Modern econometric and other statistical techniques will be used to assess the joint interaction of buyers, sellers, and policy in agricultural markets, particularly commodity futures markets, and their resulting effect on observed market prices. Research outputs from this project will inform risk management and policy decisions made by agriculture industry stakeholders by providing assessment of the impact of those decisions on output and input markets, as well as other industry stakeholder groups.
Animal Health Component
(N/A)
Research Effort Categories
Basic
100%
Applied
(N/A)
Developmental
(N/A)
Goals / Objectives
This project will provide objective, data-driven analysis of agricultural commodity markets, with a particular emphasis on the following areas and objectives:Price Dynamics: To examine price discovery in futures, forward, and/or cash markets for agricultural commodities, including attribution of observed price shocks and identification of changes in the structure of price discovery across time and space.Trading Dynamics: To examine the role of individual firms and groups of traders, including buyers and sellers of both physical agricultural commodities and their derivative futures contracts in price discovery and price risk management for agricultural commodities.Policy and Management Responses to Price Volatility: To examine the potential or realized effects of changes in governmental policy, regulation, or other institutional parameters on agricultural prices and to assess the effectiveness of hedging, storage, or other strategies as a management response to price variabilityOutput/Input Price Relationships: To evaluate interrelationships between output market prices, input market prices, and policy and management responses. In particular, to study land values as a barometer of agricultural and financial market conditions since land is the most important input in crop production and the most valuable productive asset held by agricultural producers.
Project Methods
Objective 1: Price Dynamics: To examine price discovery in futures, forward, and/or cash markets for agricultural commodities, including attribution of observed price shocks and identification of changes in the structure of price discovery across time and space.Research under this objective will identify the source of commodity price shocks using modern time series econometric methods. Work on price discovery will use price data at the highest frequency available for the given market in question to assess the speed at which information is incorporated in prices and the price leadership of particular markets. Ongoing work (Janzen, Smith, Carter 2017) studies price discovery in US cotton futures and separately identifies changes in price due to two types of speculation: financial trading and inventory holding. Using empirical market microstructure methods, the project will study a transaction-level dataset of wheat futures market prices on four major exchanges and measure the relative price discovery shares of each market. Initial work (Janzen and Adjemian 2016) has compared price discovery between wheat futures markets in the United States and European Union and the considered potential impacts on United States wheat export competitiveness. To the extent that similar data can be procured for other agricultural markets, these methods may be applied there as well.Objective 2: Trading Dynamics: To examine the roles played in price discovery and price risk management by different groups of traders, including buyers and sellers of both physical agricultural commodities and their derivative futures contracts.Data on market positions held by particular trader groups in agricultural commodity futures markets is available from the Commodity Futures Trading Commission (CFTC). Inference about the cause of observed changes in prices and positions is often confounded because prices and positions are jointly determined. To begin to unravel the simultaneity problem present in observed prices and positions, work under this project will examine the potential for dynamic, time-series models of prices and positions to incorporate data on exogenous shifts to commodity market fundamentals. For example, ongoing work (Janzen and Merener 2017) considers growing season weather as an exogenous shock to commodity prices with attendant impacts on both price discovery and trader demand for futures positions. The potential for economic theory to provide additional means of econometric identification may also be explored.Objective 3: Policy and Management Responses to Price Volatility: To examine the potential or realized effects of changes in governmental policy, regulation, or other institutional parameters on agricultural prices and to assess the effectiveness of various price risk mitigation strategies as a management response to price variability.This project will study the price impact of both policies and management strategies. Since these impacts generally occur in a single market, cross-sectional variation in the effect of interest is rarely available. To analyze policy impacts, structural time-series econometric methods will likely be employed. The identification assumptions necessary depend on institutional details of particular markets. In cases where the policy may constitute a one-time shock, event-study methods may be used, in which case econometric identification requires fewer assumptions. To analyze the impact of observed price dynamics on optimal management decision-making, simulation methods may be employed.Objective 4: Output/Input Price Relationships: To evaluate interrelationships between output market prices, input market prices, and policy and management responses. In particular, to study land values as a barometer of agricultural and financial market conditions since land is the most important input in crop production and the most valuable productive asset held by agricultural producers.Through a working agreement between the National Agricultural Statistics Service and Montana State University, the project director has access to the USDA Agricultural Resource and Management Survey (ARMS), the most comprehensive annual evaluation of farm financial data in the United States. This repeated cross-section of a large number of US farms includes measures of farmland values and rental rates. It also includes a significant number of useful covariates to control for productivity factors related to land values.Econometric analysis of this data will consider management responses to agricultural commodity price volatility, which may include forward contracting, specialization and diversification, and organic conversion, and their influence on land values. Initial work on this objective (Fuller and Janzen 2016) uses the ARMS to measure differences in land values between organic and conventional growers controlling for time, region, and profitability effects using ordinary least squares, pseudo-panel regression, and instrumental variables approaches.