Evaluating the Dynamic Nature of Market Risk

This study examines the systematic risk present in major crops for the United States and three corn-belt states. An index of commodities is used in conjunction with cash receipts to generate dynamic estimates of the systematic risk for each crop and state. In our study, we find that beta estimates from a time varying parameter model (FLS) and OLS formulation are substantially different. From our graphs of betas over time, one gains insight into the changing nature of risk and the impact of institutional and macroeconomic events. Systematic risk is shown to increase for most crops over the analyzed period with significant changes in volatility after the collapse of the Bretton Woods Accord.Systematic risk, flexible least squares, single index model, farm policy, macroeconomics, Agribusiness, Agricultural Finance, Consumer/Household Economics, Demand and Price Analysis, Farm Management, Financial Economics, Institutional and Behavioral Economics, Marketing, Risk and Uncertainty,

Generalized Euler Angle Parameterization for U(N) with Applications to SU(N) Coset Volume Measures

In a previous paper (math-ph/0205016) an Euler angle parameterization for SU(N) was given. Here we present a generalized Euler angle parameterization for U(N). The formula for the calculation of the volume for U(N), CP(N) as well as other SU(N) and U(N) cosets will also be given. In addition, the mixed and pure state product measures for N-dimensional density matrices under this parameterization will also be derived.Comment: 26 pages, no figures; minor edits, to be published in J. Geom. Phy

Data-Driven Generalized Integer Aperture Bootstrapping for Real-Time High Integrity Applications

A new method is developed for integer ambiguity resolution in carrier-phase differential GPS (CDGPS) positioning. The method is novel in that it is (1) data-driven, (2) generalized to include partial ambiguity resolution, and (3) amenable to a full characterization of the prior and posterior distributions of the three-dimensional baseline vector that results from CDGPS. The technique is termed generalized integer aperture bootstrapping (GIAB). GIAB improves the availability of integer ambiguity resolution for high-integrity, safety-critical systems. Current high-integrity CDGPS algorithms, such as EPIC and GERAFS, evaluate the prior risk of position domain biases due to incorrect integer ambiguity resolution without further validation of the chosen solution. This model-driven approach introduces conservatism which tends to reduce solution availability. Common data-driven ambiguity validation methods, such as the ratio test, control the risk of incorrect ambiguity resolution by shrinking an integer aperture (IA), or acceptance region. The incorrect fixing risk of current IA methods is determined by functional approximations that are inappropriate for use in safety-of-life applications. Moreover, generalized IA (GIA) methods incorrectly assume that the baseline resulting from partial ambiguity resolution is zero mean. Each of these limitations is addressed by GIAB, and the claimed improvements are validated by Monte Carlo simulation. The performance of GIAB is then optimized by tuning the integer aperture size to maximize the prior probability of full ambiguity resolution. GIAB is shown to provide higher availability than EPIC for the same integrity requirements.Aerospace Engineering and Engineering Mechanic