Jump and variance risk premia in the S&P 500

We analyze the risk premia embedded in the S&P 500 spot index and option markets. We use a long time-series of spot prices and a large panel of option prices to jointly estimate the diffusive stock risk premium, the price jump risk premium, the diffusive variance risk premium and the variance jump risk premium. The risk premia are statistically and economically significant and move over time. Investigating the economic drivers of the risk premia, we are able to explain up to 63 % of these variations.

Gaze stabilization testing: The efficiency and test-retest reliability of user operated control versus the parameter estimation (PEST) algorithm

The primary objective of this research study is to determine which form of testing, the PEST algorithm or an operator-controlled condition is most accurate and time efficient for administration of the gaze stabilization test

Sanitation assessment of wastewater treated by stabilization ponds for potential reuse in agricultural irrigation sanitation assessment

Wastewater reuse has become an important alternative to agricultural irrigation; on the other hand, it poses concern with regard to public health. Total coliform and Escherichia coli concentration, presence of helminth eggs and Salmonella, and physical-chemical parameters were evaluated in raw and treated wastewater. Chemical and biochemical oxygen demand removal efficiency was 74.6 and 77.9%, respectively. As for organic nitrogen, total phosphorus, and total suspended solids, total efficiency removal was 17.4, 12.5, and 32.9%, respectively. The average density of total coliforms and E. coli was 3.5 x 10(9) and 1.8 x 10(8) MPN/100 mL and 1.1 x 10(7) MPN/100 mL and 3.9 x 10(5) MPN/100 mL for raw and treated wastewater, respectively. Ascaris eggs were observed in 80.8% of the samples collected, and viable eggs in 42.3% of the samples. Salmonella was detected in 36.4% of the samples. The values observed in treated wastewater did not show the adequate bacteriological quality, as recommended by World Health Organization (Geneva, Switzerland). Therefore, additional measures should be taken to achieve an improved microbiological and parasitological quality.

A model for the stabilization of atomic hydrogen centers in borate glasses

A previously proposed model describing the trapping site of the interstitial atomic hydrogen in borate glasses is analyzed. In this model the atomic hydrogen is stabilized at the centers of oxygen polygons belonging to B-O ring structures in the glass network by van der Waals forces. The previously reported atomic hydrogen isothermal decay experimental data are discussed in the light of this microscopic model. A coupled differential equation system of the observed decay kinetics was solved numerically using the Runge Kutta method. The experimental untrapping activation energy of 0.7 x 10(-19) J is in good agreement with the calculated results of dispersion interaction between the stabilized atomic hydrogen and the neighboring oxygen atoms at the vertices of hexagonal ring structures. (C) 2009 Elsevier B.V. All rights reserved.

On the Stabilization of Gold Nanoparticles over Silica-Based Magnetic Supports Modified with Organosilanes

The immobilization of gold nanoparticles (Au NPs) on silica is made possible by the functionalization of the silica surfaces with organosilanes. Au NPs could only be stabilized and firmly attached to silica-support surfaces that were previously modified with amino groups. Au NPs could not be stabilized on bare silica surfaces and most of the NPs were then found in the solution. The metal-support interactions before and after the Au NP formation, observed by X-ray absorption fine structure spectroscopy (XAFS), indicate a stronger interaction of gold-(III) ions with amino-modified silica surfaces than with the silanol groups in bare silica. An amino-modified, silica-based, magnetic support was used to prepare an active Au NP catalyst for the chemoselective oxidation of alcohols, a reaction of great interest for the fine chemical industry.

Dynamical self-stabilization of the Mott insulator: Time evolution of the density and entanglement entropy of out-of-equilibrium cold fermion gases

The time evolution of the out-of-equilibrium Mott insulator is investigated numerically through calculations of space-time-resolved density and entropy profiles resulting from the release of a gas of ultracold fermionic atoms from an optical trap. For adiabatic, moderate and sudden switching-off of the trapping potential, the out-of-equilibrium dynamics of the Mott insulator is found to differ profoundly from that of the band insulator and the metallic phase, displaying a self-induced stability that is robust within a wide range of densities, system sizes and interaction strengths. The connection between the entanglement entropy and changes of phase, known for equilibrium situations, is found to extend to the out-of-equilibrium regime. Finally, the relation between the system`s long time behavior and the thermalization limit is analyzed. Copyright (C) EPLA, 2011

Likelihood ratio tests for variance components in linear mixed models

Although the asymptotic distributions of the likelihood ratio for testing hypotheses of null variance components in linear mixed models derived by Stram and Lee [1994. Variance components testing in longitudinal mixed effects model. Biometrics 50, 1171-1177] are valid, their proof is based on the work of Self and Liang [1987. Asymptotic properties of maximum likelihood estimators and likelihood tests under nonstandard conditions. J. Amer. Statist. Assoc. 82, 605-610] which requires identically distributed random variables, an assumption not always valid in longitudinal data problems. We use the less restrictive results of Vu and Zhou [1997. Generalization of likelihood ratio tests under nonstandard conditions. Ann. Statist. 25, 897-916] to prove that the proposed mixture of chi-squared distributions is the actual asymptotic distribution of such likelihood ratios used as test statistics for null variance components in models with one or two random effects. We also consider a limited simulation study to evaluate the appropriateness of the asymptotic distribution of such likelihood ratios in moderately sized samples. (C) 2008 Elsevier B.V. All rights reserved.

Ion pair stabilization effects on a series of procaine structural analogs

In this work, a series of 10 structural procaine analogs have been synthesized in order to investigate the structural features affecting the stability of ion pair formation and its influence on the lipophilicity of ionizable compounds. The structural variation within this series was focused on the terminal nitrogen substituents and on the intermediate chain linkage nature. The hydrophobic parameters log P(n) and log P(i) (partition coefficient of the neutral and ionic species, respectively), as well as the ionization constants pK(a) and pK(a)(oct), were obtained from log D-pH profiles measured at pH values ranging from 2 to 12. The difference between log P(i) and log P(n) values (i.e. difflog P) of each prepared compound was considered a measure of the stability of ion pair formation. In this set, the difflog P values varied nearly over one log unit, ranging from -2.40 to -3.37. It has been observed that the presence of hydrogen bonding groups (especially donor) and low steric hindrance around the terminal amine ionizable group increases the relative lipophilicity of the ionic species as compared to the corresponding neutral species. These results were interpreted as due to the increased stability of ion pairs of the compounds bearing these structural features. (C) 2010 Elsevier B.V. All rights reserved.

Genetic Control of Residual Variance for Teat Number in Pigs

The genetic improvement in litter size in pigs has been substantial during the last 10-15 years. The number of teats on the sow must increase as well to meet the needs of the piglets, because each piglet needs access to its own teat. We applied a genetic heterogeneity model on teat numberin sows, and estimated medium-high heritability for teat number (0.5), but low heritability for residual variance (0.05), indicating that selection for reduced variance might have very limited effect. A numerically positive correlation (0.8) between additive genetic breeding values for mean and for variance was found, but because of the low heritability for residual variance, the variance will increase very slowly with the mean.

Novel Statistical Methods in Quantitative Genetics : Modeling Genetic Variance for Quantitative Trait Loci Mapping and Genomic Evaluation

This thesis develops and evaluates statistical methods for different types of genetic analyses, including quantitative trait loci (QTL) analysis, genome-wide association study (GWAS), and genomic evaluation. The main contribution of the thesis is to provide novel insights in modeling genetic variance, especially via random effects models. In variance component QTL analysis, a full likelihood model accounting for uncertainty in the identity-by-descent (IBD) matrix was developed. It was found to be able to correctly adjust the bias in genetic variance component estimation and gain power in QTL mapping in terms of precision.  Double hierarchical generalized linear models, and a non-iterative simplified version, were implemented and applied to fit data of an entire genome. These whole genome models were shown to have good performance in both QTL mapping and genomic prediction. A re-analysis of a publicly available GWAS data set identified significant loci in Arabidopsis that control phenotypic variance instead of mean, which validated the idea of variance-controlling genes.  The works in the thesis are accompanied by R packages available online, including a general statistical tool for fitting random effects models (hglm), an efficient generalized ridge regression for high-dimensional data (bigRR), a double-layer mixed model for genomic data analysis (iQTL), a stochastic IBD matrix calculator (MCIBD), a computational interface for QTL mapping (qtl.outbred), and a GWAS analysis tool for mapping variance-controlling loci (vGWAS).

Estimation of genetic variance for macro- and micro-environmental sensitivity using double hierarchical generalized linear models

Background: Genetic variation for environmental sensitivity indicates that animals are genetically different in their response to environmental factors. Environmental factors are either identifiable (e.g. temperature) and called macro-environmental or unknown and called micro-environmental. The objectives of this study were to develop a statistical method to estimate genetic parameters for macro- and micro-environmental sensitivities simultaneously, to investigate bias and precision of resulting estimates of genetic parameters and to develop and evaluate use of Akaike’s information criterion using h-likelihood to select the best fitting model. Methods: We assumed that genetic variation in macro- and micro-environmental sensitivities is expressed as genetic variance in the slope of a linear reaction norm and environmental variance, respectively. A reaction norm model to estimate genetic variance for macro-environmental sensitivity was combined with a structural model for residual variance to estimate genetic variance for micro-environmental sensitivity using a double hierarchical generalized linear model in ASReml. Akaike’s information criterion was constructed as model selection criterion using approximated h-likelihood. Populations of sires with large half-sib offspring groups were simulated to investigate bias and precision of estimated genetic parameters. Results: Designs with 100 sires, each with at least 100 offspring, are required to have standard deviations of estimated variances lower than 50% of the true value. When the number of offspring increased, standard deviations of estimates across replicates decreased substantially, especially for genetic variances of macro- and micro-environmental sensitivities. Standard deviations of estimated genetic correlations across replicates were quite large (between 0.1 and 0.4), especially when sires had few offspring. Practically, no bias was observed for estimates of any of the parameters. Using Akaike’s information criterion the true genetic model was selected as the best statistical model in at least 90% of 100 replicates when the number of offspring per sire was 100. Application of the model to lactation milk yield in dairy cattle showed that genetic variance for micro- and macro-environmental sensitivities existed. Conclusion: The algorithm and model selection criterion presented here can contribute to better understand genetic control of macro- and micro-environmental sensitivities. Designs or datasets should have at least 100 sires each with 100 offspring.

Genetic heterogeneity of within-family variance of body weight in Atlantic salmon (Salmo salar)

BACKGROUND: Canalization is defined as the stability of a genotype against minor variations in both environment and genetics. Genetic variation in degree of canalization causes heterogeneity of within-family variance. The aims of this study are twofold: (1) quantify genetic heterogeneity of (within-family) residual variance in Atlantic salmon and (2) test whether the observed heterogeneity of (within-family) residual variance can be explained by simple scaling effects. RESULTS: Analysis of body weight in Atlantic salmon using a double hierarchical generalized linear model (DHGLM) revealed substantial heterogeneity of within-family variance. The 95% prediction interval for within-family variance ranged from ~0.4 to 1.2 kg2, implying that the within-family variance of the most extreme high families is expected to be approximately three times larger than the extreme low families. For cross-sectional data, DHGLM with an animal mean sub-model resulted in severe bias, while a corresponding sire-dam model was appropriate. Heterogeneity of variance was not sensitive to Box-Cox transformations of phenotypes, which implies that heterogeneity of variance exists beyond what would be expected from simple scaling effects. CONCLUSIONS: Substantial heterogeneity of within-family variance was found for body weight in Atlantic salmon. A tendency towards higher variance with higher means (scaling effects) was observed, but heterogeneity of within-family variance existed beyond what could be explained by simple scaling effects. For cross-sectional data, using the animal mean sub-model in the DHGLM resulted in biased estimates of variance components, which differed substantially both from a standard linear mean animal model and a sire-dam DHGLM model. Although genetic differences in canalization were observed, selection for increased canalization is difficult, because there is limited individual information for the variance sub-model, especially when based on cross-sectional data. Furthermore, potential macro-environmental changes (diet, climatic region, etc.) may make genetic heterogeneity of variance a less stable trait over time and space.

Genetic heterogeneity of residual variance : estimation of variance components using double hierarchical generalized linear models

Background: The sensitivity to microenvironmental changes varies among animals and may be under genetic control. It is essential to take this element into account when aiming at breeding robust farm animals. Here, linear mixed models with genetic effects in the residual variance part of the model can be used. Such models have previously been fitted using EM and MCMC algorithms. Results: We propose the use of double hierarchical generalized linear models (DHGLM), where the squared residuals are assumed to be gamma distributed and the residual variance is fitted using a generalized linear model. The algorithm iterates between two sets of mixed model equations, one on the level of observations and one on the level of variances. The method was validated using simulations and also by re-analyzing a data set on pig litter size that was previously analyzed using a Bayesian approach. The pig litter size data contained 10,060 records from 4,149 sows. The DHGLM was implemented using the ASReml software and the algorithm converged within three minutes on a Linux server. The estimates were similar to those previously obtained using Bayesian methodology, especially the variance components in the residual variance part of the model. Conclusions: We have shown that variance components in the residual variance part of a linear mixed model can be estimated using a DHGLM approach. The method enables analyses of animal models with large numbers of observations. An important future development of the DHGLM methodology is to include the genetic correlation between the random effects in the mean and residual variance parts of the model as a parameter of the DHGLM.

Inheritance beyond plain heritability : variance-controlling genes in Arabidopsis thaliana

The phenotypic effect of a gene is normally described by the mean-difference between alternative genotypes. A gene may, however, also influence the phenotype by causing a difference in variance between genotypes. Here, we reanalyze a publicly available Arabidopsis thaliana dataset [1] and show that genetic variance heterogeneity appears to be as common as normal additive effects on a genomewide scale. The study also develops theory to estimate the contributions of variance differences between genotypes to the phenotypic variance, and this is used to show that individual loci can explain more than 20% of the phenotypic variance. Two well-studied systems, cellular control of molybdenum level by the ion-transporter MOT1 and flowering-time regulation by the FRI-FLC expression network, and a novel association for Leaf serration are used to illustrate the contribution of major individual loci, expression pathways, and gene-by-environment interactions to the genetic variance heterogeneity.

The Brazilian 1994 stabilization plan: an analytical view

The Real Plan has succeeded in stabilizing the Brazilian inflation. The consumer price inflation has been reduced from 11260 percent per year, in June 1994, to an estimate of 8 percent in 1997. The lower inflation resulted in a remarkable income distribution, and in an increased private consumption. The plan managed to control the inflationary effects of the increased demand with some traditional measures: A more liberalized economy, a moving (and overvalued) exchange rate band, high interest rate differentials, and a tight domestic credit policy. The government has, so far failed to accomplish the fiscal adjustment. The price stabilization has largely depended on the current account deficit. However, macroeconomic indicators do not present reasons for concern about the current account sustainability, in the medium-run. The economy may be trapped in a low-growth vicious cycle, represented by a stop-and-go trend, due to the two-way endogencity between domestic saving and growth. Economic growth depends on policies in increase the public sector saving, to secure the privatization of the State enterprises, and to promote investments. The major problem for the government action is, as always, in the political sphere. Approximately 80 percent of the Central Government net revenue are allocated to the social sectors. Consequently, the fiscal reform will hue to deal with the problem of re-designing the public sector’s intervention in the social area. Most probably, it will be inevitable to cut the social area budget. This is politically unpleasant.