Higher-order moments and co-moments' contribution to spillover analysis and portfolio risk management

Unlike volatility, the skewness and kurtosis of asset returns are often neglected in the analysis of spillovers and risk management, although they capture the return asymmetry and fat-tailedness, respectively, arising from the non-normality of returns. In this paper, we provide evidence of the relevance and utility of considering spillovers in volatility and higher-order moments (skewness, and kurtosis) and co-moments (covariance, co-skewness, and co-kurtosis), and their implications for hedging. Using high-frequency data on the US stock, crude oil, and gold markets, a time-varying spillover approach and portfolio analysis, we reveal the following results. Firstly, besides volatility and covariance, co-skewness and co-kurtosis are relevant spillover transmitters across the stock, crude oil, and gold markets. Secondly, the level of total spillover increases when including not only covariance but also co-skewness and co-kurtosis, suggesting the relevance of considering higher order co-moments beyond volatility when studying spillovers. Thirdly, the inclusion of co-moments in the spillover analysis generates a significant improvement in hedging for all pairs, which is reflected in the significant increase in the utility function when co-skewness and co-kurtosis are considered. This result is noted when the COVID-19 sub-period is considered separately, except for oil‑gold. Overall, the findings matter for the system of interconnectivity across various assets and emphasize the implications and contributions of higher-order moments and co-moments to portfolio allocation and financial risk management. © 2023 Elsevier B.V.

Comparative Analysis of COVID 19 Detection from Cough Speech Using Machine Learning Classifiers

Corona Virus Disease-2019, or COVID-19, has been on the rise since its emergence, so its early detection is necessary to stop it from spreading rapidly. Speech detection is one of the best ways to detect it at an early stage as it exhibits variations in the nasopharyngeal cavity and can be performed ubiquitously. In this research, three standard databases are used for detection of COVID-19 from speech signal. The feature set includes the baseline perceptual features such as spectral centroid, spectral crest, spectral decrease, spectral entropy, spectral flatness, spectral flux, spectral kurtosis, spectral roll off point, spectral skewness, spectral slope, spectral spread, harmonic to noise ratio, and pitch. 05 ML based classification techniques have been employed using these features. It has been observed that Generalized Additive Model (GAM) classifier offers an average of 95% and a maximum of 97.55% accuracy for COVID-19 detection from cough signals. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Analysing the Changes in Shape of Lung using Pseudo Zernike Moments in Comparison to Invariant Moments

Aim: The aim of the analysis is to estimate the deformation in the shape of the lung due to incidence of COVID using pseudo Zernike moments in comparison to invariant moments. Materials and Methods: Images are obtained from Kaggle. Sample size of 176 acquired for the study using G power by considering factors effect size, standard error rate, algorithm power as 0.3, 0.05, 0.80 respectively. In this analysis the classification of normal and COVID subjects is made using seven invariant and pseudo-Zernike moment features. Classification is made using a neural network after extracting the feature values. Result: From the obtained results, the feature values of invariant moments were observed to be statistically significant (p<0.05) than pseudo-Zernike moments. The mean and standard deviation values of variance for normal and COVID subjects were (0.18\± 0.13,0.10± 0.13). For pseudo Zernike's M2 feature statistical values of normal and COVID subjects were (0.63± 0.22,0.56± 0.23). From the values, it is observed that the COVID subjects had loss in shape of lungs due to abnormality. Variance, skewness and kurtosis were found to be statistically significant in differentiating normal and COVID subjects. The accuracy and F1 score values of invariant moments were 0.98 and 0.97 respectively. Conclusion: Therefore, from this analysis it is observed that invariant moments provide significantly better classification between normal and COVID subjects when compared to pseudo Zernike moments. © 2022 IEEE.

Spatial and temporal patterns and factors influencing the spread of the COVID-19 pandemic in China

It is essential to unravel the spatial and temporal patterns of the spread of the epidemic in China during the backdrop of the global coronavirus disease 2019 (COVID-19) outbreak in 2020, as the underlying drivers are crucial for scientific formulation of epidemy-preventing strategies. A discriminant model for the spatio-temporal pattern of epidemic spread was developed for 317 prefecture-level cities using accumulated data on confirmed cases. The model was introduced for the real-time evolution of the outbreak starting from the rapid spread of COVID-19 on January 24, 2020, until the control on March 18, 2020. The model was used to analyze the basic characteristics of the spatio-temporal patterns of the epidemic spread by combining parameters such as peak position, full width at half maximum, kurtosis, and skewness. A multivariate logistic regression model was developed to unravel the key drivers of the spatio-temporal patterns based on traffic accessibility, urban connectivity, and population flow. The results of the study are as follows. (1) The straight-line distance of 588 km from Wuhan was used as the effective boundary to identify the four spatial patterns of epidemic spread, and 13 types of spatio-temporal patterns were obtained by combining the time-course categories of the same spatial pattern. (2) The spread of the epidemic was relatively severe in the leapfrogging model. Besides the short-distance leapfrogging model, significant differences emerged in the spatial patterns of the time course of epidemic spread. The peaks of the new confirmed cases in various spatio-temporal patterns were mostly observed on February 3, 2020. The average full widths at the half maximum of all ordinary cities were approximately 14 days, thus, resonating with the incubation period of the COVID-19 virus. (3) The degree of the population correlation with Wuhan city has mainly influenced the spreading and the short-distance leapfrogging spatial patterns. The existence of direct flight from Wuhan city exhibited a positive effect on the long-distance leapfrogging spatial pattern. The number of population outflows has significantly affected the leapfrogging spatial pattern. The integrated spatial pattern was influenced by both primary and secondary epidemic outbreak sites. Thus, cities should pay great attention to traffic control during the epidemic as analysis has shown that the spatio-temporal patterns of epidemic spread in the respective cities can curb the spread of the epidemic from key links. © 2022, Science Press. All right reserved.