Detecting outliers in multivariate volatility models: a wavelet procedure

It is well known that outliers can affect both the estimation of parameters and volatilities when fitting a univariate GARCH-type model. Similar biases and impacts are expected to be found on correlation dynamics in the context of multivariate time series. We study the impact of outliers on the estimation of correlations when fitting multivariate GARCH models and propose a general detection algorithm based on wavelets, that can be applied to a large class of multivariate volatility models. Its effectiveness is evaluated through a Monte Carlo study before it is applied to real data. The method is both effective and reliable, since it detects very few false outliers.info:eu-repo/semantics/publishedVersio

Visualizing Profiles of Large Datasets of Weighted and Mixed Data

This work provides a procedure with which to construct and visualize profiles, i.e., groups of individuals with similar characteristics, for weighted and mixed data by combining two classical multivariate techniques, multidimensional scaling (MDS) and the k-prototypes clustering algorithm. The well-known drawback of classical MDS in large datasets is circumvented by selecting a small random sample of the dataset, whose individuals are clustered by means of an adapted version of the k-prototypes algorithm and mapped via classical MDS. Gower’s interpolation formula is used to project remaining individuals onto the previous configuration. In all the process, Gower’s distance is used to measure the proximity between individuals. The methodology is illustrated on a real dataset, obtained from the Survey of Health, Ageing and Retirement in Europe (SHARE), which was carried out in 19 countries and represents over 124 million aged individuals in Europe. The performance of the method was evaluated through a simulation study, whose results point out that the new proposal solves the high computational cost of the classical MDS with low error.This research was funded by the Spanish Ministry of Economy and Competitiveness, grant number MTM2014-56535-R; and the V Regional Plan for Scientific Research and Technological Innovation 2016-2020 of the Community of Madrid, an agreement with Universidad Carlos III de Madrid in the action of "Excellence for University Professors.

Asymmetry, realised volatility and stock return risk estimates

In this paper we estimate minimum capital risk requirements for short and long positions with three investment horizons, using the traditional GARCH model and two other GARCH-type models that incorporate the possibility of asymmetric responses of volatility to price changes. We also address the problem of the extremely high estimated persistence of the GARCH model to generate observed volatility patterns by including realised volatility as an explanatory variable into the model’s variance equation. The results suggest that the inclusion of realised volatility improves the GARCH forecastability as well as its ability to calculate accurate minimum capital risk requirements and makes it quite competitive when compared with asymmetric conditional heteroscedastic models such as the GJR and the EGARCH.info:eu-repo/semantics/publishedVersio

Previous Crop Impacts on Wheat Variety Performance in Central Kansas During the 2021–2022 Growing Season

The effect of a previous crop and its residue left on the field before the next crop is a consequence of soil water usage and residue quality. We evaluated the grain yield of forty winter wheat varieties, as well as soil bulk density, soil water content, and previous crop’s residue C:N ratio in three neighboring fields near Solomon, KS. Wherein these three fields, winter wheat was no-tilled following a previous crop of either 1) soybean; 2) cover crop mix (legume and cereal); or 3) winter wheat. The mix of cover crops consisted of pearl millet, sorghum sudan, and sunn hemp. Soil samples were taken in October during winter wheat sowing. Four replications of soil measurements for bulk density and water content were taken from the 0- to 16-in. depth at 8-in. intervals. Six replications of 10.8-ft2 quadrats of residue biomass were sampled and evaluated for total nitrogen (N) and carbon (C). There were no significant differences in winter wheat grain yield among the varieties nor among the sites, although yield following soybeans was slightly lower than yield following wheat or cover crops (41 vs. 46 bu/a). Soil bulk density and residue C:N ratio were the lowest when following soybean (i.e., greater soil porosity and faster residue decay), although soil water content was also the lowest. Soil water content at sowing was the greatest when following winter wheat, likely as there were no actively growing summer crops to use precipitation water prior to wheat sowing. Soil water content increased at deeper layers (0–8 in. compared to 8–16 in.) when winter wheat was sown following a cover crop mix or a previous winter wheat crop, but it decreased when following soybean. Preliminary results from this on-farm experiment suggest that winter wheat variety performance was similar across previous crops despite measured differences in residue and soil characteristics. These results may help farmers to decide the benefits of each crop residue based on their cropping system needs

Wavelet-Based Detection of Outliers in Poisson INAR(1) Time Series

The presence of outliers or discrepant observations has a negative impact in time series modelling. This paper considers the problem of detecting outliers, additive or innovational, single, multiple or in patches, in count time series modelled by first-order Poisson integer-valued autoregressive, PoINAR(1), models. To address this problem, two wavelet-based approaches that allow the identification of the time points of outlier occurrence are proposed. The effectiveness of the proposed methods is illustrated with synthetic as well as with an observed dataset

Co-circulation of SARS-CoV-2 Alpha and Gamma variants in Italy, February and March 2021

Background. Several SARS-CoV-2 variants of concern (VOC) have emerged through 2020 and 2021. There is need for tools to estimate the relative transmissibility of emerging variants of SARS-CoV-2 with respect to circulating strains.AimWe aimed to assess the prevalence of co-circulating VOC in Italy and estimate their relative transmissibility.Methods. We conducted two genomic surveillance surveys on 18 February and 18 March 2021 across the whole Italian territory covering 3,243 clinical samples and developed a mathematical model that describes the dynamics of co-circulating strains.Results. The Alpha variant was already dominant on 18 February in a majority of regions/autonomous provinces (national prevalence: 54%) and almost completely replaced historical lineages by 18 March (dominant across Italy, national prevalence: 86%). We found a substantial proportion of the Gamma variant on 18 February, almost exclusively in central Italy (prevalence: 19%), which remained similar on 18 March. Nationally, the mean relative transmissibility of Alpha ranged at 1.55-1.57 times the level of historical lineages (95% CrI: 1.45-1.66). The relative transmissibility of Gamma varied according to the assumed degree of cross-protection from infection with other lineages and ranged from 1.12 (95% CrI: 1.03-1.23) with complete immune evasion to 1.39 (95% CrI: 1.26-1.56) for complete cross-protection.Conclusion. We assessed the relative advantage of competing viral strains, using a mathematical model assuming different degrees of cross-protection. We found substantial co-circulation of Alpha and Gamma in Italy. Gamma was not able to outcompete Alpha, probably because of its lower transmissibility

SARS-CoV-2 transmission patterns in educational settings during the Alpha wave in Reggio-Emilia, Italy

: Different monitoring and control policies have been implemented in schools to minimize the spread of SARS-CoV-2. Transmission in schools has been hard to quantify due to the large proportion of asymptomatic carriers in young individuals. We applied a Bayesian approach to reconstruct the transmission chains between 284 SARS-CoV-2 infections ascertained during 87 school outbreak investigations conducted between March and April 2021 in Italy. Under the policy of reactive quarantines, we found that 42.5% (95%CrI: 29.5-54.3%) of infections among school attendees were caused by school contacts. The mean number of secondary cases infected at school by a positive individual during in-person education was estimated to be 0.33 (95%CrI: 0.23-0.43), with marked heterogeneity across individuals. Specifically, we estimated that only 26.0% (95%CrI: 17.6-34.1%) of students and school personnel who tested positive during in-person education caused at least one secondary infection at school. Positive individuals who attended school for at least 6 days before being isolated or quarantined infected on average 0.49 (95%CrI: 0.14-0.83) secondary cases. Our findings provide quantitative insights on the contribution of school transmission to the spread of SARS-CoV-2 in young individuals. Identifying positive cases within 5 days after exposure to their infector could reduce onward transmission at school by at least 30%

Accurate minimum capital risk requirements: A comparison of several approaches

In this paper we estimate, for several investment horizons, minimum capital risk requirements for short and long positions, using the unconditional distribution of three daily indexes futures returns and a set of short and long memory stochastic volatility and GARCH-type models. We consider the possibility that errors follow a t-Student distribution in order to capture the kurtosis of the returns' series. The results suggest that accurate modelling of extreme observations obtained for long and short trading investment positions is possible with an autoregressive stochastic volatility model. Moreover, modelling futures returns with a long memory stochastic volatility model produces, in general, excessive volatility persistence, and consequently, leads to large minimum capital risk requirement estimates. Finally, the models' predictive ability is assessed with the help of out-of-sample conditional tests.Long memory Minimum capital risk requirement Moving block bootstrap Stochastic volatility Volatility persistence

Susceptibility of almond seedling rootstocks to capnode

5 pages,-- En: ISHS Acta Horticulturae 591: III International Symposium on Pistachios and Almonds.Almond rootstocks, particularly the bitter almond, have been described as more resistant to capnode (Capnodis tenebrionis L.) than other species. This work looks into the susceptibility of seedling rootstocks of the almond cultivars 'Garrigues', 'Atocha' and 'Desmayo Largueta' to grubs of capnode. Three months after the plants were inoculated with neonate grubs, damage in roots and presence of grubs were observed. Subsequently, roots were analysed by HPLC to determine prunasin content. Although a large variability was observed, 'Desmayo Largueta' seedlings were the most susceptible whereas 'Garrigues' seedlings were the most resistant. The results showed a high correlation between presence of grubs and damage in roots but there was no correlation between root damage and the content of prunasin. Obtaining seedling rootstocks from open pollination could be a tool for breeding almond rootstocks for resistance to capnode, mainly using the traditional Garrigues as female parent.Peer reviewe

Relationship between cyanogenic compounds in kernels, leaves, and roots of sweet and bitter kernelled almonds

4 pages, 3 tables.The relationship between the levels of cyanogenic compounds (amygdalin and prunasin) in kernels, leaves, and roots of 5 sweet-, 5 slightly bitter-, and 5 bitter-kernelled almond trees was determined. Variability was observed among the genotypes for these compounds. Prunasin was found only in the vegetative part (roots and leaves) for all genotypes tested. Amygdalin was detected only in the kernels, mainly in bitter genotypes. In general, bitter-kernelled genotypes had higher levels of prunasin in their roots than nonbitter ones, but the correlation between cyanogenic compounds in the different parts of plants was not high. While prunasin seems to be present in most almond roots (with a variable concentration) only bitter-kernelled genotypes are able to transform it into amygdalin in the kernel. Breeding for prunasin-based resistance to the buprestid beetle Capnodis tenebrionis L. is discussed.Peer reviewe