The Relative Performance of In-Sample and Out-of-Sample Hedging Effectiveness Indicators

Hedging effectiveness is the proportion of price risk removed through hedging. Empirical hedging studies typically estimate a set of risk minimizing hedge ratios, estimate the hedging effectiveness statistic, apply the estimated hedge ratios to a second group of data, and examine the robustness of the hedging strategy by comparing the hedging effectiveness for this "out-of-sample" period to the "in-sample" period. This study focuses on the statistical properties of the in-sample and out-of-sample hedging effectiveness estimators. Through mathematical and simulation analysis we determine the following: (a) the R2 for the hedge ratio regression will generally overstate the amount of price risk reduction that can be achieved by hedging, (b) the properly computed hedging effectiveness in the hedge ratio regression will also generally overstate the true amount of risk reduction that can be achieved, (c) hedging effectiveness estimated in the out-of-sample period will generally understate the true amount of risk reduction that can be achieved, (d) for equal numbers of observations, the overstatement in (b) is less that the understatement in (c), (e) both errors decline as more observations are used, and (f) the most accurate approach is to use all of the available data to estimate the hedge ratio and effectiveness and to not hold any data back for hedge strategy validation. If structural change in the hedge ratio model is suspected, tests for parameter equality have a better statistical foundation that do tests of hedging effectiveness equality.out-of-sample, post sample, hedging, effectiveness, forecasts, simulation, Agribusiness, Agricultural Finance, Demand and Price Analysis, Farm Management, Financial Economics, Marketing, Research Methods/ Statistical Methods, Risk and Uncertainty,

Hedge Effectiveness Forecasting

This study focuses on hedging effectiveness defined as the proportionate price risk reduction created by hedging. By mathematical and simulation analysis we determine the following: (a) the regression R2 in the hedge ratio regression will generally overstate the amount of price risk reduction that can be achieved by hedging, (b) the properly computed hedging effectiveness in the hedge ratio regression will also generally overstate the amount of risk reduction that can be achieved by hedging, (c) the overstatement in (b) declines as the sample size increases, (d) application of estimated hedge ratios to non sample data results in an unbiased estimate of hedging effectiveness, (e) application of hedge ratios computed from small samples presents a significant chance of actually increasing price risk by hedging, and (f) comparison of in sample and out of sample hedging effectiveness is not the best method for testing for structural change in the hedge ratio regression.out of sample, post sample, hedging, effectiveness, forecasts, simulation, Agricultural Finance,

Computing Bayesian predictive distributions: The K-square and K-prime distributions

The computation of two Bayesian predictive distributions which are discrete mixtures of incomplete beta functions is considered. The number of iterations can easily become large for these distributions and thus, the accuracy of the result can be questionable. Therefore, existing algorithms for that class of mixtures are improved by introducing round-off error calculation into the stopping rule. A further simple modification is proposed to deal with possible underflows that may prevent recurrence to work properly

Singular random matrix decompositions: distributions.

Assuming that Y has a singular matrix variate elliptically contoured distribution with respect to the Hausdorff measure, the distributions of several matrices associated to QR, modified QR, SV and Polar decompositions of matrix Y are determined, for central and non-central, non-singular and singular cases, as well as their relationship to the Wishart and Pseudo-Wishart generalized singular and non-singular distributions. We present a particular example for the Karhunen-Lòeve decomposition. Some of these results are also applied to two particular subfamilies of elliptical distributions, the singular matrix variate normal distribution and the singular matrix variate symmetric Pearson type VII distribution