An econometric analysis of ETF and ETF futures in financial and energy markets using generated regressors

It is well known that that there is an intrinsic link between the financial and energy sectors, which can be analyzed through their spillover effects, which are measures of how the shocks to returns in different assets affect each other’s subsequent volatility in both spot and futures markets. Financial derivatives, which are not only highly representative of the underlying indices but can also be traded on both the spot and futures markets, include Exchange Traded Funds (ETFs), which is a tradable spot index whose aim is to replicate the return of an underlying benchmark index. When ETF futures are not available to examine spillover effects, “generated regressors” may be used to construct both Financial ETF futures and Energy ETF futures. The purpose of the paper is to investigate the covolatility spillovers within and across the US energy and financial sectors in both spot and futures markets, by using “generated regressors” and a multivariate conditional volatility model, namely Diagonal BEKK. The daily data used are from 1998/12/23 to 2016/4/22. The data set is analyzed in its entirety, and also subdivided into three subset time periods. The empirical results show there is a significant relationship between the Financial ETF and Energy ETF in the spot and futures markets. Therefore, financial and energy ETFs are suitable for constructing a financial portfolio from an optimal risk management perspective, and also for dynamic hedging purposes.

Participation and performance trends in 6-hour ultra-marathoners – a retrospective data analysis of worldwide participation from 1991-2010

Participation trends in 6-hour ultra-marathons held word-wide were investigated to gain basic demographic data on 6-hour ultra-marathoners and where these races took place. Participation trends and the association between nationality and race performance were investigated in all 6-hour races held worldwide between 1991 and 2010. Participation increased linearly in both women and men across years. The annual number of finishes was significantly higher in men than in women (P=0.013). The male-to-female ratio remained stable at ~4 since 1991. Runners in age group 45-49 years showed the largest increase in participation for both men (800 participants in 18 years) and women (208 participants in 16 years). Europe attracted most of the runners from other continents (166 runners), more than all other continents combined (55 runners). European runners also showed the best top ten performances (73±3 km for women and 77±11 km for men), while African (with 65±9 km for men) and South American (54±4 km for women and 65±2 km for men) runners showed the weakest. To summarize, participation in 6-hour ultra-marathons increased across years. Most of the development took place in Europe and in athletes in the age group 45-49 years. Europe also attracted the most diverse field of athletes with runners from all other continents. European runners accounted for the most runners and achieved the best top ten performances.

Applying semantic knowledge to the automatic processing of temporal expressions and events in natural language

This paper addresses the problem of the automatic recognition and classification of temporal expressions and events in human language. Efficacy in these tasks is crucial if the broader task of temporal information processing is to be successfully performed. We analyze whether the application of semantic knowledge to these tasks improves the performance of current approaches. We therefore present and evaluate a data-driven approach as part of a system: TIPSem. Our approach uses lexical semantics and semantic roles as additional information to extend classical approaches which are principally based on morphosyntax. The results obtained for English show that semantic knowledge aids in temporal expression and event recognition, achieving an error reduction of 59% and 21%, while in classification the contribution is limited. From the analysis of the results it may be concluded that the application of semantic knowledge leads to more general models and aids in the recognition of temporal entities that are ambiguous at shallower language analysis levels. We also discovered that lexical semantics and semantic roles have complementary advantages, and that it is useful to combine them. Finally, we carried out the same analysis for Spanish. The results obtained show comparable advantages. This supports the hypothesis that applying the proposed semantic knowledge may be useful for different languages.

Multi-Element Analysis of Spanish Date Palm (Phoenix dactylifera L.) by Inductively Coupled Plasma-Based Techniques. Discrimination Using Multivariate Statistical Analysis

The elemental analysis of Spanish palm dates by inductively coupled plasma atomic emission spectrometry and inductively coupled plasma mass spectrometry is reported for the first time. To complete the information about the mineral composition of the samples, C, H, and N are determined by elemental analysis. Dates from Israel, Tunisia, Saudi Arabia, Algeria and Iran have also been analyzed. The elemental composition have been used in multivariate statistical analysis to discriminate the dates according to its geographical origin. A total of 23 elements (As, Ba, C, Ca, Cd, Co, Cr, Cu, Fe, H, In, K, Li, Mg, Mn, N, Na, Ni, Pb, Se, Sr, V, and Zn) at concentrations from major to ultra-trace levels have been determined in 13 date samples (flesh and seeds). A careful inspection of the results indicate that Spanish samples show higher concentrations of Cd, Co, Cr, and Ni than the remaining ones. Multivariate statistical analysis of the obtained results, both in flesh and seed, indicate that the proposed approach can be successfully applied to discriminate the Spanish date samples from the rest of the samples tested.

JAABA: interactive machine learning for automatic annotation of animal behavior

We present a machine learning-based system for automatically computing interpretable, quantitative measures of animal behavior. Through our interactive system, users encode their intuition about behavior by annotating a small set of video frames. These manual labels are converted into classifiers that can automatically annotate behaviors in screen-scale data sets. Our general-purpose system can create a variety of accurate individual and social behavior classifiers for different organisms, including mice and adult and larval Drosophila.

Analysis of analogue models by helical X-ray computed tomography

The aim of analogue model experiments in geology is to simulate structures in nature under specific imposed boundary conditions using materials whose rheological properties are similar to those of rocks in nature. In the late 1980s, X-ray computed tomography (CT) was first applied to the analysis of such models. In early studies only a limited number of cross-sectional slices could be recorded because of the time involved in CT data acquisition, the long cooling periods for the X-ray source and computational capacity. Technological improvements presently allow an almost unlimited number of closely spaced serial cross-sections to be acquired and calculated. Computer visualization software allows a full 3D analysis of every recorded stage. Such analyses are especially valuable when trying to understand complex geological structures, commonly with lateral changes in 3D geometry. Periodic acquisition of volumetric data sets in the course of the experiment makes it possible to carry out a 4D analysis of the model, i.e. 3D analysis through time. Examples are shown of 4D analysis of analogue models that tested the influence of lateral rheological changes on the structures obtained in contractional and extensional settings.

Handling the Problem of Unbalanced Data Sets in the Classification of Technical Equipment States

Questions of handling unbalanced data considered in this article. As models for classification, PNN and MLP are used. Problem of estimation of model performance in case of unbalanced training set is solved. Several methods (clustering approach and boosting approach) considered as useful to deal with the problem of input data.

Multivariate statistical analysis of highway accident and highway conditions. Final report.

Transportation Department, Office of University Research, Washington, D.C.

Statistical analysis of preliminary data for the safe performance curriculum driving knowledge test. Interim report.

DeKalb County School System, Decatur, Ga.

Hazardous material spills : a documentation and analysis of historical data /

Mode of access: Internet.

National Center for Statistics and Analysis collected technical studies. Volume I: accident data analysis of vehicle crashworthiness - ten papers.

Mode of access: Internet.