Bid-Ask Spreads, Volume, and Volatility: Evidence from Livestock Markets

Understanding the determinants of liquidity costs in agricultural futures markets is hampered by a need to use proxies for the bid-ask spread which are often biased, and by a failure to account for a jointly determined micro-market structure. We estimate liquidity costs and its determinants for the live cattle and hog futures markets using alternative liquidity cost estimators, intraday prices and micro-market information. Volume and volatility are simultaneously determined and significantly related to the bid-ask spread. Daily volume is negatively related to the spread while volatility and volume per transaction display positive relationships. Electronic trading has a significant competitive effect on liquidity costs, particularly in the live cattle market. Results are sensitive to the bid-ask spread measure, with a modified Bayesian method providing estimates most consistent with expectations and the competitive structure found in these markets.Bayesian estimation, bid-ask spread determinants, liquidity cost, Livestock Production/Industries, Marketing,

Market Depth in Lean Hog and Live Cattle Futures Markets

Liquidity costs in futures markets are not observed directly because bids and offers occur in an open outcry pit and are not recorded. Traditional estimation of these costs has focused on bidask spreads using transaction prices. However, the bid-ask spread only captures the tightness of the market price. As the volume increases measures of market depth which identify how the order flow moves prices become important information. We estimate market depth for lean hogs and live cattle markets using a Bayesian MCMC method to estimate unobserved data. While the markets are highly liquid, our results show that cost- and risk-reducing strategies may exist. Liquidity costs are highest when larger volumes are traded at distant contracts. For hogs the market becomes less liquid prior to the expiration month. For cattle this occurs during the expiration month when the liquidity risk is also higher. For both markets this coincides with periods of low volume. For the nearby contract highest trading volume occurs at the beginning of the month prior to expiration and lowest trading volume occurs in the expiration month. For both commodities the cumulative effect of volume on price change may lead to liquidity costs higher than a tick.Bayesian MCMC, lean hog futures, liquidity cost, live cattle futures, market depth, market microstructure, Agricultural Finance,

Forecasting Corn Futures Volatility in the Presence of Long Memory, Seasonality and Structural Change

Price volatility in the corn market has changed considerably globalization and stronger linkages to the energy complex. Using data from January 1989 through December 2009, we estimate and forecast the volatility in the corn market using futures daily prices. Estimates in a Fractional Integrated GARCH framework identify the importance of long memory, seasonality, and structural change. Recursively generated forecasts for up to 40-day horizons starting in January 2005 highlight the importance of seasonality, and long memory specifications which perform well at more distant horizons particularly with rising volatility. The forecast benefits of allowing for structural change in an adaptive framework are more difficult to identify except at more distant horizons after a large downturn in volatility.corn price volatility, long memory, seasonality, structural change, forecasting, Agricultural Finance, Risk and Uncertainty,

Estimating Liquidity Costs in Agricultural Futures Markets using Bayesian Methods

Estimation of liquidity costs in futures markets is challenging because bid-ask spreads are usually not observed. Several estimators of liquidity costs exist that use transaction data, but there is little agreement on their relative accuracy and usefulness, and their performance has been questioned. We use a Bayesian method proposed by Hasbrouck which possesses conceptually desirable properties to estimate liquidity costs of six agricultural future contracts. The method builds on Roll's model and uses Markov Chain Monte Carlo estimation. Our Bayesian estimates are lower than more traditional estimates and as anticipated decrease even more when more realistic assumptions such as discreteness are incorporated. The findings demonstrate the need for further research to clarify the usefulness and accuracy of the procedure.Marketing,

STRATEGIC RISK MANAGEMENT BEHAVIOR: WHAT CAN UTILITY FUNCTIONS TELL US?

The validity of the utility concept, particularly in an expected utility framework, has been questioned because of its inability to predict revealed behavior. In this paper we focus on the global shape of the utility function instead of the local shape of the utility function. We examine the extent of heterogeneity in the global shape of the utility function of decision makers and test whether its shape predicts strategic risk management behavior. We assess the utility functions and relate them to strategic decisions for portfolio managers (N = 104) and hog farmers (N = 239). The research design allows us to examine the robustness of our results and the extent to which the results can be generalized. Furthermore, we assess the shape of the utility functions for these decision makers applying two different methods. This allows us to further test the robustness of our empirical results. If there exists a relationship between the shape of the utility function and strategic decisions, both methods should yield the same result. The empirical results indicate that the global shape of the utility function differs across decision makers (fully concave or convex versus S-shaped), and that the global shape predicts strategic decisions (e.g., asset allocation strategy in the case of portfolio managers; type of production process employed in the case of hog farmers). These findings support the notion that the often criticized concept of utility is a useful concept when studying actual behavior, and highlight the importance of considering decision-maker behavior over a wide outcome range when examining strategic behavior.Risk and Uncertainty,

Intermediate Volatility Forecasts Using Implied Forward Volatility: The Performance of Selected Agricultural Commodity Options

Options with different maturities can be used to generate an implied forward volatility, a volatility forecast for non-overlapping future time intervals. Using five commodities with varying characteristics, we find that the implied forward volatility dominates forecasts based on historical volatility information, but that the predictive accuracy is affected by the commodity's characteristics. Unbiased and efficient corn and soybeans market forecasts are attributable to the well-established volatility during crucial growing periods. For soybean meal, wheat, and hogs volatility is less predictable, and investors appear to demand a risk premium for bearing volatility risk.Marketing,

Spatial Aggregation and Weather Risk Management

Previous studies identify limited potential efficacy of weather derivatives in hedging agricultural exposures. In contrast to earlier studies which investigate the problem at low levels of aggregation, we find using straight forward temperature contracts that better weather hedging opportunities exist at higher levels of spatial aggregation. Aggregating production exposures reduces idiosyncratic (i.e. localized or region specific) risk, leaving a greater proportion of the total risk in the form of systemic weather risk which can be effectively hedged using weather derivatives. The aggregation effect suggests that the potential for weather derivatives in agriculture may be greater than previously thought, particularly for aggregators of risk such as re/insurers.weather derivatives, spatial aggregation, corn, yield risk, crop insurance, hedging, Risk and Uncertainty,

Unobserved Heterogeneity: Evidence and Implications for SMEs' Hedging Behavior

Financial research indicates that several firm characteristics are related to the use of derivatives. Less attention has been paid to the role of the characteristics of managers, which are particularly important when studying derivative usage of small and medium sized enterprises (SMEs). In this paper we focus on the influence of manager's level of education, the manager's decision-making unit, and the fundamental determinants of risk management - managerial risk attitude and managerial risk perception - on SMEs' commodity derivative usage. In empirical studies to date, the heterogeneity of derivative users has been neglected. We propose a generalized mixture regression model that estimates the relationship between commodity derivative usage and a set of explanatory variables across segments of an industry. Accounting for unobserved heterogeneity reveals that segments of the industry have different determinants of derivative use. Moreover, the heterogeneity at the segment level appears to mask significant effects at the aggregate level, most notably the effects of risk attitude and risk perception.Marketing,

MEAT-PACKER CONDUCT IN FED CATTLE PRICING: MULTIPLE-MARKET OLIGOPSONY POWER

The exercise of market power across multiple geographic fed cattle markets is measured with an econometric model which links behavior of the margin between boxed beef and regional fed cattle prices to an oligopsony model of multiple-market conduct. The game theoretic economic model suggests that for market power to be exercised in a single market a discontinuous pricing strategy must be followed. Total market power is enhance if meat-packers coordinate this pricing strategy across geographic markets. Tests reject independence of pricing conduct across geographic markets which suggests multiple-market power is present. The extent of the market power also is consistent with the economic model. More market power is exercised across regions with the same meat-packing firms. However, the magnitude of the market power is small and decreased between the early and late 1980s.Demand and Price Analysis,

PRICE FORECASTING WITH TIME-SERIES METHODS AND NONSTATIONARY DATA: AN APPLICATION TO MONTHLY U.S. CATTLE PRICES

The forecasting performance of various multivariate as well as univariate ARIMA models is evaluated in the presence of nonstationarity. The results indicate the importance of identifying the characteristics of the time series by testing for types of nonstationarity. Procedures that permit model specifications consistent with the systemÂ’s dynamics provide the most accurate forecasts.Demand and Price Analysis, Livestock Production/Industries,