Birnbaum–Saunders autoregressive conditional duration models applied to high-frequency financial data

Modern financial markets now record the precise time of each stock trade, along with price and volume, with the aim of analysing the structure of the times between trading events—leading to a big data problem. In this paper, we propose and compare two Birnbaum–Saunders autoregressive conditional duration models specified in terms of time-varying conditional median and mean durations. These models provide a novel alternative to the existing autoregressive conditional duration models due to their flexibility and ease of estimation. Diagnostic tools are developed to allow goodness-of-fit assessment and to detect departures from assumptions, including the presence of outliers and influential cases. These diagnostic tools are based on the parameter estimates using residual analysis and the Cook distance for global influence, and different perturbation schemes for local influence. A thorough Monte Carlo study is presented to evaluate the performance of the maximum likelihood estimators, and the forecasting ability of the models is assessed using the traditional and density forecast evaluation techniques. The Monte Carlo study suggests that the parameter estimators are asymptotically unbiased, consistent and normally distributed. Finally, a full analysis of a real-world financial transaction data set, from the German DAX in 2016, is presented to illustrate the proposed approach and to compare the fitting and forecasting performances with existing models in the literature. One case related to the duration time is identified as potentially influential, but its removal does not change resulting inferences demonstrating the robustness of the proposed approach. Fitting and forecasting performances favor the proposed models and, in particular, the median-based approach gives additional protection against outliers, as expected

The Inverse Problem of Analog Gravity Systems

Analog gravity models of black holes and exotic compact objects provide a unique opportunity to study key properties of such systems in controlled laboratory environments. In contrast to astrophysical systems, analog gravity systems can be prepared carefully and their dynamical aspects thus investigated in unprecedented ways. While gravitational wave scattering properties of astrophysical compact objects are more connected to quasi-normal modes, laboratory experiments can also access the transmission and reflection coefficients, which are otherwise mostly relevant for Hawking radiation related phenomena. In this work, we report two distinct results. First, we outline a semi-classical, non-parametric method that allows for the reconstruction of the effective perturbation potential from the knowledge of transmission and reflection coefficients for certain types of potentials in the Schr\"odinger wave equation admitting resonant tunneling. Second, we show how to use our method by applying it to an imperfect draining vortex, which has been suggested as analog of extreme compact objects. Although the inverse problem is in general not unique, choosing physically motivated assumptions and requiring the validity of semi-classical theory, we demonstrate that the method provides efficient and accurate results.Comment: 11 pages, 7 figure

Influence of nutrients on enhancing laccase production by Botryosphaeria rhodina MAMB-05

The physiological requirements needed to enhance the production of laccases by the ascomycete Botryosphaeria rhodina MAMB-05 in submerged cultivation were examined under non-induced and induced (veratryl alcohol, VA) conditions. Under non-induced conditions (–VA), the initial pH, C:N ratio, and inorganic N source did not influence laccase production, in contrast to Tween 80, soybean oil, and copper, which significantly increased laccase production, and proline and urea, which suppressed laccase formation. In addition, Tween 60 could serve as the sole carbon source for the production of these enzymes. Under VA-induced conditions of fungal growth, factors such as inoculum type, time-point of addition of inducer, initial pH, C:N ratio, and type of N source, influenced the production of laccases; however, unlike the non-induced conditions, proline and urea did not act as suppressors. Each of these physiological conditions exerted different effects on biomass production. The nutritional conditions examined for B. rhodina MAMB-05 are discussed in relation to their influence on fungal growth and laccase production. [Int Microbiol 2007; 10(3):177-185

Specific insulin and proinsulin secretion in glucokinase-deficient individuals

Glucokinase (GCK) is an enzyme that regulates insulin secretion, keeping glucose levels within a narrow range. Mutations in the glucokinase gene cause a rare form of diabetes called maturity-onset diabetes of the young (MODY). An early onset (less than 25 years), autosomal dominant inheritance and low insulin secretion stimulated by glucose characterize MODY patients. Specific insulin and proinsulin were measured in serum by immunofluorimetric assays (IFMA) during a 75-g oral glucose tolerance test (OGTT). Two kindreds (SA and LZ) were studied and compared to non-diabetic unrelated individuals (control group 1) matched for age and body mass index (BMI). In one kindred, some of these subjects were also obese (BMI >26 kg/m2), and other family members also presented with obesity and/or late-onset NIDDM. The MODY patients were also compared to a group of five of their first-degree relatives with obesity and/or late-onset NIDDM. The proinsulin profile was different in members of the two MODY kindreds. Fasting proinsulin and the proinsulin/insulin ratio were similar in MODY members of kindred LZ and subjects from control group 1, but were significantly lower than in MODY members of kindred SA (P<0.02 and P<0.01, for proinsulin and proinsulin/insulin ratio, respectively). Moreover, MODY members of family SA had higher levels of proinsulin and proinsulin/insulin ratio, although not significantly different, when compared to their first-degree relatives and to subjects from control group 2. In conclusion, we observed variable degrees of proinsulin levels and proinsulin/insulin ratio in MODY members of two different kindreds. The higher values of these parameters found in MODY and non-MODY members of kindred SA is probably related to the obesity and late-onset NIDDM background present in this family.Hospital Santa CasaHôpital Saint-LouisFondation Jean DaussetUniversidade Federal de São Paulo (UNIFESP)UNIFESPSciEL

Quantum Configuration and Phase Spaces: Finsler and Hamilton Geometries

In this paper, we review two approaches that can describe, in a geometrical way, the kinematics of particles that are affected by Planck-scale departures, named Finsler and Hamilton geometries. By relying on maps that connect the spaces of velocities and momenta, we discuss the properties of configuration and phase spaces induced by these two distinct geometries. In particular, we exemplify this approach by considering the so-called $q$-de Sitter-inspired modified dispersion relation as a laboratory for this study. We finalize with some points that we consider as positive and negative ones of each approach for the description of quantum configuration and phases spaces.Comment: 22 pages. Matches published version. Invited contribution for Physics. Special Issue "New Advances in Quantum Geometry

Modeling Mortality Based on Pollution and Temperature Using a New Birnbaum–Saunders Autoregressive Moving Average Structure with Regressors and Related-Sensors Data

Environmental agencies are interested in relating mortality to pollutants and possible environmental contributors such as temperature. The Gaussianity assumption is often violated when modeling this relationship due to asymmetry and then other regression models should be considered. The class of Birnbaum–Saunders models, especially their regression formulations, has received considerable attention in the statistical literature. These models have been applied successfully in different areas with an emphasis on engineering, environment, and medicine. A common simplification of these models is that statistical dependence is often not considered. In this paper, we propose and derive a time-dependent model based on a reparameterized Birnbaum–Saunders (RBS) asymmetric distribution that allows us to analyze data in terms of a time-varying conditional mean. In particular, it is a dynamic class of autoregressive moving average (ARMA) models with regressors and a conditional RBS distribution (RBSARMAX). By means of a Monte Carlo simulation study, the statistical performance of the new methodology is assessed, showing good results. The asymmetric RBSARMAX structure is applied to the modeling of mortality as a function of pollution and temperature over time with sensor-related data. This modeling provides strong evidence that the new ARMA formulation is a good alternative for dealing with temporal data, particularly related to mortality with regressors of environmental temperature and pollution