Study of the Tail Dependence Structure in Global Financial Markets Using Extreme Value Theory

Abstract: The presence of tail dependencies invalidates the multivariate normality assumptions in portfolio risk management. The identification of tail (in)dependencies has drawn major attention in empirical financial studies. Yet it is still a challenging issue both theoretically and practically. Previous studies based on either a restrictive model or the null hypothesis of tail (perfect) dependence does not well describe or interpret extreme co-movements in financial markets. This paper examines tail dependence structures underlying a broad range of financial asset classes employing the newly developed tail quotient correlation coefficients. In theory, the original tail quotient correlation coefficient proposed in (Zhang 2008) is adapted to incorporate cases with varying data driven random thresholds. Our empirical results demonstrate different tail dependence structures underlying various global financial markets. Either omission or unanimous treatment of the tail dependence structures for different financial markets will lead to erroneous conclusions or suboptimal investment choices. The multivariate extreme value theory framework in this study has the potential to serve as an useful tool in exploiting arbitrage opportunities, optimizing asset allocations, and building robust risk management strategies

Optimal measurements to access classical correlations of two-qubit states

We analyze the optimal measurements accessing classical correlations in arbitrary two-qubit states. Two-qubit states can be transformed into the canonical forms via local unitary operations. For the canonical forms, we investigate the probability distribution of the optimal measurements. The probability distribution of the optimal measurement is found to be centralized in the vicinity of a specific von Neumann measurement, which we call the maximal-correlation-direction measurement (MCDM). We prove that for the states with zero-discord and maximally mixed marginals, the MCDM is the very optimal measurement. Furthermore, we give an upper bound of quantum discord based on the MCDM, and investigate its performance for approximating the quantum discord.Comment: 8 pages, 3 figures, version accepted by Phys. Rev.

Exploration of comorbidity mechanisms and potential therapeutic targets of rheumatoid arthritis and pigmented villonodular synovitis using machine learning and bioinformatics analysis

Background: Rheumatoid arthritis (RA) is a chronic autoimmune disease. Pigmented villonodular synovitis (PVNS) is a tenosynovial giant cell tumor that can involve joints. The mechanisms of co-morbidity between the two diseases have not been thoroughly explored. Therefore, this study focused on investigating the functions, immunological differences, and potential therapeutic targets of common genes between RA and PVNS.Methods: Through the dataset GSE3698 obtained from the Gene Expression Omnibus (GEO) database, the differentially expressed genes (DEGs) were screened by R software, and weighted gene coexpression network analysis (WGCNA) was performed to discover the modules most relevant to the clinical features. The common genes between the two diseases were identified. The molecular functions and biological processes of the common genes were analyzed. The protein-protein interaction (PPI) network was constructed using the STRING database, and the results were visualized in Cytoscape software. Two machine learning algorithms, least absolute shrinkage and selection operator (LASSO) logistic regression and random forest (RF) were utilized to identify hub genes and predict the diagnostic efficiency of hub genes as well as the correlation between immune infiltrating cells.Results: We obtained a total of 107 DEGs, a module (containing 250 genes) with the highest correlation with clinical characteristics, and 36 common genes after taking the intersection. Moreover, using two machine learning algorithms, we identified three hub genes (PLIN, PPAP2A, and TYROBP) between RA and PVNS and demonstrated good diagnostic performance using ROC curve and nomogram plots. Single sample Gene Set Enrichment Analysis (ssGSEA) was used to analyze the biological functions in which three genes were mostly engaged. Finally, three hub genes showed a substantial association with 28 immune infiltrating cells.Conclusion: PLIN, PPAP2A, and TYROBP may influence RA and PVNS by modulating immunity and contribute to the diagnosis and therapy of the two diseases

Triassic sedimentation and postaccretionary crustal evolution along the Solonker suture zone in Inner Mongolia, China

Detrital zircon U-Pb dating of the Xingfuzhilu Formation in southern Inner Mongolia yields a maximum depositional age of around 220 Ma. The predominantly Permian and Triassic zircons are characterized by oscillatory zoning and euhedral shapes, with mostly positive zircon εHf(t) values (+2.0 to +16.4), indicating that they were derived from a proximal magmatic source. Early-Middle Paleozoic zircons have variable zircon εHf(t) values from −6.2 to +11.2 and are characterized by weak oscillatory zoning and subhedral-subrounded shapes, suggesting that the sources are a proximal magmatic arc, possibly mixed with components of the Ondor Sum magmatic arc and the magmatic arc at the northern margin of the North China Craton. The remnants of Precambrian blocks in the southeastern Central Asian Orogenic Belt (CAOB), and the North China Craton may also have been a minor source region for the Xingfuzhilu succession. These results, combined with regional data, indicate that a closing remnant ocean basin or narrow seaway possibly existed in the Middle Permian (Guadalupian) immediately prior to final collision of the CAOB and closure of the Paleo-Asian Ocean. Subsequent collision resulted in the crustal uplift and thickening along the Solonker suture zone, accompanied by possible slab break-off and lithospheric delamination during the Latest Permian to Middle Triassic. The resultant orogen in the Late Triassic underwent exhumation and denudation of rocks in response to the postorogenic collapse and regional extension. Vertical crustal growth in the Triassic is documented by detrital zircons from the Xingfuzhilu Formation and appears to have been widespread across entire eastern CAOB

Optical Absorption Characteristics, Spatial Distribution, and Source Analysis of Colored Dissolved Organic Matter in Wetland Water around Poyang Lake

Colored dissolved organic matter (CDOM) is an important part of aquatic ecosystems and plays a key role in the biogeochemical cycle. In this study, CDOM absorption spectrum curves and water quality parameters from 30 sampling sites in the wetlands of Poyang Lake, Jiangxi Province, China, were collected in October 2016. The optical absorption characteristics and spatial distribution of CDOM, the correlation between the absorption coefficient of CDOM at a wavelength of 355 nm (ag(355)), and the concentration of dissolved organic carbon (DOC) were analyzed. Spectral characteristic parameters—namely, E2/E3 (the ratio of the CDOM absorption coefficient at a wavelength of 250 nm to the CDOM absorption coefficient at a wavelength of 365 nm), SUVA254 (the ratio of the CDOM absorption coefficient at a wavelength of 254 nm to the DOC concentration), and spectral slopes—were used to infer the composition and sources of CDOM in the Poyang Lake wetlands. The results showed the following: (1) the CDOM absorption spectrum of water of the Poyang Lake wetlands presented significant spatial variation, showing a trend of south > west > north > east; (2) there was a strong linear correlation between the CDOM absorption coefficient and the DOC concentration in the water of the Poyang Lake wetlands (ag(355) = 1.075DOC–0.659 (r2 = 0.723, p n = 30)); (3) the analysis of the spectral characteristic parameters E2/E3, SUVA254, and spectral slopes showed that the CDOM in the Poyang Lake wetlands has relatively high aromaticity and molecular weight, which were shown to be mainly affected by terrestrial inputs. The results showed that the molecular weight and aromaticity of CDOM were higher in the south of the lake than in other parts

Spatial–seasonal characteristics and influencing factors of dissolved organic carbon and chromophoric dissolved organic matter in Poyang Lake

As the largest organic carbon pool in water, dissolved organic carbon (DOC) plays a key role in the carbon cycle. In inland rivers and lakes, DOC is closely related to chromophoric dissolved organic matter (CDOM) with optical attenuation. In this study, the spatial distribution and seasonal variations of DOC and CDOM in Poyang Lake in 2014–2016 were investigated. The results demonstrated that the DOC concentration in Poyang Lake had a range of 1.34–5.56 mg/L with an average of 2.12 ± 0.54 mg/L. The absorption coefficient of CDOM at 355 nm had a range of 1.24–5.70 m−1 with an average of 2.71 ± 0.83 m−1. In terms of the spatial distribution, the concentrations of DOC and CDOM in the south of Poyang Lake were higher than those in the north of the lake. In terms of seasonal variations, the concentrations of DOC and CDOM were higher in spring and summer than in autumn and winter. The absorption coefficients of CDOM and DOC concentrations in Poyang Lake exhibited a significant linear correlation. The correlation between DOC and CDOM in some sections of Poyang Lake varied spatially and seasonally. The highest correlation was observed in wetland waters of the southern Poyang Lake in spring, while there was no significant correlation in northern section of the lake in most of the periods. The results revealed that water level, precipitation and the vegetation cover pattern had determining effects on the spatial heterogeneity of DOC and CDOM. The spectral characteristic parameters demonstrated that the main source of the CDOM in Poyang Lake was from terrestrial input

GainTKW: A Measurement System of Thousand Kernel Weight Based on the Android Platform

Thousand kernel weight (TKW) is an important parameter for the evaluation of grain yield. The traditional measurement method relies on manual steps: weighing and counting. In this paper, we developed a system for the automated evaluation of thousand kernel weight that combines a weighing module and Android devices, called “gainTKW”. The system is able to collect the weight information from the weighing module through a serial port using the RS232-micro USB cable. In the imaging process, we adopt a k-means clustering segmentation algorithm to solve the problem of uneven lighting. We used the marker-controlled watershed algorithm and area threshold method to count the number of kernels that are touching one another. These algorithms were implemented based on the OpenCV (Open Source Computer Vision) libraries. The system tested kernel images of six species taken with the Android device under different lighting conditions. The algorithms in this study can solve the segmentation problems caused by shadows, as well. The appropriate numbers of kernels, of different species, are counted with an error ratio upper limit of 3%. The application is convenient and easy to operate. For the experiments, we can prove the efficiency and accuracy of the developed system by comparing the results between the manual method and the proposed application

Variety Identification of Single Rice Seed Using Hyperspectral Imaging Combined with Convolutional Neural Network

The feasibility of using hyperspectral imaging with convolutional neural network (CNN) to identify rice seed varieties was studied. Hyperspectral images of 4 rice seed varieties at two different spectral ranges (380–1030 nm and 874–1734 nm) were acquired. The spectral data at the ranges of 441–948 nm (Spectral range 1) and 975–1646 nm (Spectral range 2) were extracted. K nearest neighbors (KNN), support vector machine (SVM) and CNN models were built using different number of training samples (100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1500, 2000, 2500 and 3000). KNN, SVM and CNN models in the Spectral range 2 performed slightly better than those in the Spectral range 1. The model performances improved with the increase in the number of training samples. The improvements were not significant when the number of training samples was large. CNN model performed better than the corresponding KNN and SVM models in most cases, which indicated the effectiveness of using CNN to analyze spectral data. The results of this study showed that CNN could be adopted in spectral data analysis with promising results. More varieties of rice need to be studied in future research to extend the use of CNNs in spectral data analysis

Automatic Segmentation and Counting of Aphid Nymphs on Leaves Using Convolutional Neural Networks

The presence of pests is one of the main problems in crop production, and obtaining reliable statistics of pest infestation is essential for pest management. Detection of pests should be automated because human monitoring of pests is time-consuming and error-prone. Aphids are among the most destructive pests in greenhouses and they reproduce quickly. Automatic detection of aphid nymphs on leaves (especially on the lower surface) using image analysis is a challenging problem due to color similarity and complicated background. In this study, we propose a method for segmentation and counting of aphid nymphs on leaves using convolutional neural networks. Digital images of pakchoi leaves at different aphid infestation stages were obtained, and corresponding pixel-level binary mask annotated. In the test, segmentation results by the proposed method achieved high overlap with annotation by human experts (Dice coefficient of 0.8207). Automatic counting based on segmentation showed high precision (0.9563) and recall (0.9650). The correlation between aphid nymph count by the proposed method and manual counting was high (R2 = 0.99). The proposed method is generic and can be applied for other species of pests