HEDGING CLASS I MILK: THE "ACCELERATION" AND "MOVER" EFFECT

A volatile closing basis prevents class I hedgers from locking in a minimum price. The closing basis is composed of an "acceleration" and "mover" effect. The mover effect always works to the producer's advantage unlike the acceleration effect. This research discusses hedging strategies to minimize the acceleration effect.Marketing,

RISK BALANCING STRATEGIES IN THE FLORIDA DAIRY INDUSTRY: AN APPLICATION OF CONDITIONAL VALUE AT RISK

Legislation has prompted changes in milk price volatility. Milk price volatility impacts the producer's exposure to business risk which is compound by the firms financial risk. Financial risk is a function of the firms capital structure. In the short run it is difficult for the producer to significantly change the firms capital structure and therefore balance increased business risk with reduced financial risk. The producer can however reduce financial and business risk by using futures contracts to lock in a price for milk produced. The producer's risk preferences dictate the producer's hedge ratio. Using the return on equity as a profitability measure and the conditional value at risk as a risk measure the optimal hedge ratio is derived for various probabilities of negative returns on equity.Conditional Value at Risk, cVaR, Risk Management, Futures, Dairy, Agricultural and Food Policy, Livestock Production/Industries, Risk and Uncertainty,

A Sample-Path Approach to Optimal Position Liquidation

We consider the problem of optimal position liquidation with the aim of maximizing the expected cash flow stream from the transaction in the presence of temporary or permanent market impact. We use a stochastic programming approach to derive trading strategies that differentiate decisions with respect to observed market conditions. The scenario set consists of a collection of sample paths representing possible future realizations of state variable processes (price of the security, trading volume etc.) At each time moment the set of paths is partitioned into several groups according to specified criteria, and each group is controlled by its own decision variable(s), which allows for adequate representation of uncertainties in market conditions and circumvents anticipativity in the solutions. In contrast to traditional dynamic programming approaches, the presented formulation admits incorporation of different types of constraints in the trading strategy, e.g. risk constraints, various decision-making policies, etc. Numerical results and optimal trading patterns for different forms of market impact are presented