A generalized multi-period mean-variance portfolio optimization with Markov switching parameters

In this paper, we deal with a generalized multi-period mean-variance portfolio selection problem with market parameters Subject to Markov random regime switchings. Problems of this kind have been recently considered in the literature for control over bankruptcy, for cases in which there are no jumps in market parameters (see [Zhu, S. S., Li, D., & Wang, S. Y. (2004). Risk control over bankruptcy in dynamic portfolio selection: A generalized mean variance formulation. IEEE Transactions on Automatic Control, 49, 447-457]). We present necessary and Sufficient conditions for obtaining an optimal control policy for this Markovian generalized multi-period meal-variance problem, based on a set of interconnected Riccati difference equations, and oil a set of other recursive equations. Some closed formulas are also derived for two special cases, extending some previous results in the literature. We apply the results to a numerical example with real data for Fisk control over bankruptcy Ill a dynamic portfolio selection problem with Markov jumps selection problem. (C) 2008 Elsevier Ltd. All rights reserved.

Performance of a multi-level TDR system exposed to tropical field conditions - A time-space comparison with tensiometry

Time-domain reflectometry (TDR) is an important technique to obtain series of soil water content measurements in the field. Diode-segmented probes represent an improvement in TDR applicability, allowing measurements of the soil water content profile with a single probe. In this paper we explore an extensive soil water content dataset obtained by tensiometry and TDR from internal drainage experiments in two consecutive years in a tropical soil in Brazil. Comparisons between the variation patterns of the water content estimated by both methods exhibited evidences of deterioration of the TDR system during this two year period at field conditions. The results showed consistency in the variation pattern for the tensiometry data, whereas TDR estimates were inconsistent, with sensitivity decreasing over time. This suggests that difficulties may arise for the long-term use of this TDR system under tropical field conditions. (c) 2008 Elsevier B.V. All rights reserved.

Enantioselective analysis of mirtazapine and its two major metabolites in human plasma by liquid chromatography-mass spectrometry after three-phase liquid-phase microextraction

A three-phase liquid-phase microextraction (LPME) method using porous polypropylene hollow fibre membrane with a sealed end was developed for the extraction of mirtazapine (MRT) and its two major metabolites, 8-hydroxymirtazapine (8-OHM) and demethylmirtazapine (DMR), from human plasma. The analytes were extracted from 1.0 mL of plasma, previously diluted and alkalinized with 3.0 mL 0.5 mol L-1 pH 8 phosphate buffer solution and supplemented with 15% sodium chloride (NaCl), using n-hexyl ether as organic solvent and 0.01 moL L-1 acetic acid solution as the acceptor phase. Haloperidol was used as internal standard. The chromatographic analyses were carried out on a chiral column, using acetonitrile-methanol-ethanol (98:1:1, v/v/v) plus 0.2% diethylamine as mobile phase, at a flow rate of 1.0 mL min(-1). Multi-reaction monitoring (MRM) detection was performed by mass spectrometry (MS-MS) using a triple-stage quadrupole and electrospray ionization interface operating in the positive ion mode. The mean recoveries were in 18.3-45.5% range with linear responses over the 1.25-125 ng mL(-1) concentration range for all enantiomers evaluated. The quantification limit (LOQ) was 1.25 ng mL(-1). Within-day and between-day assay precision and accuracy (2.5, 50 and 100 ng mL(-1)) showed relative standard deviation and the relative error lower than 11.9% for all enantiomers evaluated. Finally, the method was successfully used for the determination of mirtazapine and its metabolite enantiomers in plasma samples obtained after single drug administration of mirtazapine to a healthy volunteer. (c) 2007 Elsevier B.V. All rights reserved.