The (de)merits of minimum-variance hedging: application to the crack spread

We study the empirical performance of the classical minimum-variance hedging strategy, comparing several econometric models for estimating hedge ratios of crude oil, gasoline and heating oil crack spreads. Given the great variability and large jumps in both spot and futures prices, considerable care is required when processing the relevant data and accounting for the costs of maintaining and re-balancing the hedge position. We find that the variance reduction produced by all models is statistically and economically indistinguishable from the one-for-one “naïve” hedge. However, minimum-variance hedging models, especially those based on GARCH, generate much greater margin and transaction costs than the naïve hedge. Therefore we encourage hedgers to use a naïve hedging strategy on the crack spread bundles now offered by the exchange; this strategy is the cheapest and easiest to implement. Our conclusion contradicts the majority of the existing literature, which favours the implementation of GARCH-based hedging strategies.

Quantitative genetic divergence and standing genetic (co)variance in thermal reaction norms along latitude

Although the potential to adapt to warmer climate is constrained by genetic trade-offs, our understanding of how selection and mutation shape genetic (co)variances in thermal reaction norms is poor. Using 71 isofemale lines of the fly Sepsis punctum, originating from northern, central, and southern European climates, we tested for divergence in juvenile development rate across latitude at five experimental temperatures. To investigate effects of evolutionary history in different climates on standing genetic variation in reaction norms, we further compared genetic (co)variances between regions. Flies were reared on either high or low food resources to explore the role of energy acquisition in determining genetic trade-offs between different temperatures. Although the latter had only weak effects on the strength and sign of genetic correlations, genetic architecture differed significantly between climatic regions, implying that evolution of reaction norms proceeds via different trajectories at high latitude versus low latitude in this system. Accordingly, regional genetic architecture was correlated to region-specific differentiation. Moreover, hot development temperatures were associated with low genetic variance and stronger genetic correlations compared to cooler temperatures. We discuss the evolutionary potential of thermal reaction norms in light of their underlying genetic architectures, evolutionary histories, and the materialization of trade-offs in natural environments.

Sodomy laws and gender variance in Tahiti and Hawai‘i

In both Hawaiian and Tahitian, the central meaning of mahu denotes gender-variant individuals, particularly male-bodied persons who have a significant investment in femininity. However, in Hawai‘i, unlike Tahiti, the word mahu is now more commonly used as an insult against gay or transgender people. The negative connotation of the term in Hawaiian indexes lower levels of social acceptability for mahu identity on O‘ahu (Hawai‘i’s most populous island) as compared to Tahiti. The article argues that these differences are partly due to a historical legacy of sexually repressive laws. The article traces the history of sodomy laws in these two Polynesian societies and argues that this history supports the hypothesis that sodomy laws (in conjunction with such social processes as urbanisation and Christianisation) are partially to blame for the diminished social status of mahu on O‘ahu. A different social and legal history in Tahiti accounts for the fact that the loss of social status experienced by Tahitian mahu has been lesser than that of their Hawaiian counterparts.

Polynomial-based noise variance estimation for MIMO-SCBT systems

In this paper, we present a polynomial-based noise variance estimator for multiple-input multiple-output single-carrier block transmission (MIMO-SCBT) systems. It is shown that the optimal pilots for noise variance estimation satisfy the same condition as that for channel estimation. Theoretical analysis indicates that the proposed estimator is statistically more efficient than the conventional sum of squared residuals (SSR) based estimator. Furthermore, we obtain an efficient implementation of the estimator by exploiting its special structure. Numerical results confirm our theoretical analysis.

Fashion magazines: design implications of focus and perception variance between Chinese and UK females

A growing segment of Chinese women are willing to spend a high percentage of their income on fashion related products, however there appears to be concern over the quality of Chinese fashion magazines. Concern can be focused in two major issues: i) fashion magazine design, and ii) pictorial and textual distribution of content. This paper investigates how human factors (i.e. social norms and individual differences) influence fashion magazine design/format preferences, and investigates the difference in readership patterns between British and Chinese Women. Our study identifies significant differences between UK and Chinese readership; which has an impact on magazine viewing patterns and content preference.

Forecasting exchange rate volatility using conditional variance models selected by information criteria

This paper uses appropriately modified information criteria to select models from the GARCH family, which are subsequently used for predicting US dollar exchange rate return volatility. The out of sample forecast accuracy of models chosen in this manner compares favourably on mean absolute error grounds, although less favourably on mean squared error grounds, with those generated by the commonly used GARCH(1, 1) model. An examination of the orders of models selected by the criteria reveals that (1, 1) models are typically selected less than 20% of the time.

A Variance-Minimizing Filter for Large-Scale Applications

A truly variance-minimizing filter is introduced and its per for mance is demonstrated with the Korteweg– DeV ries (KdV) equation and with a multilayer quasigeostrophic model of the ocean area around South Africa. It is recalled that Kalman-like filters are not variance minimizing for nonlinear model dynamics and that four - dimensional variational data assimilation (4DV AR)-like methods relying on per fect model dynamics have dif- ficulty with providing error estimates. The new method does not have these drawbacks. In fact, it combines advantages from both methods in that it does provide error estimates while automatically having balanced states after analysis, without extra computations. It is based on ensemble or Monte Carlo integrations to simulate the probability density of the model evolution. When obser vations are available, the so-called importance resampling algorithm is applied. From Bayes’ s theorem it follows that each ensemble member receives a new weight dependent on its ‘ ‘distance’ ’ t o the obser vations. Because the weights are strongly var ying, a resampling of the ensemble is necessar y. This resampling is done such that members with high weights are duplicated according to their weights, while low-weight members are largely ignored. In passing, it is noted that data assimilation is not an inverse problem by nature, although it can be for mulated that way . Also, it is shown that the posterior variance can be larger than the prior if the usual Gaussian framework is set aside. However , i n the examples presented here, the entropy of the probability densities is decreasing. The application to the ocean area around South Africa, gover ned by strongly nonlinear dynamics, shows that the method is working satisfactorily . The strong and weak points of the method are discussed and possible improvements are proposed.

Variance in male reproductive success and sexual size dimorphism in pinnipeds: testing an assumption of sexual selection theory

The theory of evolution by sexual selection for sexual size dimorphism (SSD) postulates that SSD primarily reflects the adaptation of males and females to their different reproductive roles. For example, competition among males for access to females increases male body size because larger males are better able to maintain dominant status than smaller males. Larger dominant males sire most offspring while smaller subordinate males are unsuccessful, leading to skew in reproductive success. Therefore, species with male-biased SSD are predicted to have greater variance in male reproductive success than those in which both sexes are similar in size. We tested this prediction among the Pinnipedia, a mammalian group with a great variation in SSD. From a literature review, we identified genetic estimates of male reproductive success for 10 pinniped taxa (eight unique species and two subspecies of a ninth species) that range from seals with similarly sized males and females to species in which males are more than four times as large as females. We found no support for a positive relationship between variance in reproductive success and SSD among pinnipeds after excluding the elephant seals Mirounga leonina and Mirounga angustirostris, which we discuss as distinctive cases. Several explanations for these results are presented, including the revival of one of Darwin's original ideas. Darwin proposed that natural selection may explain SSD based on differences in energetic requirements between sexes and the potential for sexual niche segregation. Males may develop larger bodies to exploit resources that remain unavailable to females due to the energetic constraints imposed on female mammals by gestation and lactation. The importance of this alternative explanation remains to be tested.

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.

Analysis of genotypic variation in genes associated with virulence in Aggregatibacter actinomycetemcomitans clinical isolates

Background and Objective: Although certain serotypes of Aggregatibacter actinomycetemcomitans are associated more with aggressive periodontitis than are other serotypes, the correlation between distinct lineages and virulence traits in this species is poorly understood. This study aimed to evaluate the polymorphism of genes encoding putative virulence factors of clinical isolates, and to correlate these findings with A. actinomycetemcomitans serotypes, genotypes and periodontal status of the hosts. Material and Methods: Twenty-six clinical isolates from diverse geographic populations with different periodontal conditions were evaluated. Genotyping was performed using pulse-field gel electrophoresis. Polymorphisms in the genes encoding leukotoxin, Aae, ApaH and determinants for serotype-specific O polysaccharide were investigated. Results: The isolates were classified into serotypes a-f, and exhibited three apaH genotypes, five aae alleles and 25 macrorestriction profiles. Two serotype b isolates (7.7%), obtained from Brazilian patients with aggressive periodontitis, were associated with the highly leukotoxic genotype; these isolates showed identical fingerprint patterns and aae and apaH genotypes. Serotype c, obtained from various periodontal conditions, was the most prevalent among Brazilian isolates, and isolates were distributed in two aae alleles, but formed a genetically distinct group based on apaH analysis. Cluster analysis showed a close relationship between fingerprinting genotypes and serotypes/apaH genotypes, but not with aae genotypes. Conclusion: Apart from the deletion in the ltx promoter region, no disease-associated markers were identified. Non-JP2-like strains recovered from individuals with periodontal disease exhibited considerable genetic variation regarding aae/apaH genotypes, serotypes and XhoI DNA fingerprints.

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.

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.