ABSTRACT
In this study, we modeled the log-return of three emerging markets' stock indices, namely, Shanghai SSE, Russia MOEX, and Bombay Stock Exchange Sensex using the generalized hyperbolic family of distributions. We found the generalized hyperbolic family of distributions as the best fit for describing the probability density based on AIC and likelihood ratio test. The coherent risk measure, i.e., the expected shortfall, predicted using the best fit probability distribution, was used as a market risk quantification metric. During the COVID-19 period, the Indian stock market showed maximum market risk, followed by the Russian. The Chinese market showed the least market risk. Our experiment demonstrated a significant (p = 0.000) difference in the three markets concerning the coherent risk at different probability levels from 0.001 to 0.05 in the COVID-19 period using the Jonckheere-Terpstra test. The coherent market risk increased substantially in the Indian and Russian markets during the COVID-19 pandemic compared to the pre-COVID-19 period. However, in the Chinese market, we found that the coherent risk decreased during the COVID-19 period compared to the pre-COVID-19 period. We carried out the empirical study using the adjusted daily closing values of SSE, MOEX, and Sensex from July 2018 to July 2021 and dividing the data sets into pre-COVID-19 and COVID-19 periods based on the first emergence of the COVID-19 case.
ABSTRACT
This paper examined the interconnectedness of COVID-19 and stock markets in some of th e most affected countries-USA, Italy, Spain and Germany. To this end, a time-varying cointegration technique was first employed to examine for the presence of comovementsbetween daily infections and stock market changes. A time-varying wild bootstrap likelihood ratio test was then employed to determine whether COVID-19 is a significant predictor of stock market performance. Lastly, an event study analysis was conducted to investigate the short-term effect of the outbreak on stock market returns. Findings revealed the existence of comovements between COVID-19 infections and stock price indices in all the selected countries. The rejection of the null hypothesis of no predictability was also recorded in all of the countries sampled. The event study analysis revealed that significant negative cumulative abnormal returns were predominant in all the countries. The reactions of the stock markets of the three European Union member countries included in the study to the pandemic are quite similar, suggesting that countries that are regionally and economically integrated are likely to experience relatively similar effects. The USA stock market was the most resilient to the impact of the outbreak
ABSTRACT
In testing hypotheses pertaining to Lorenz dominance (LD), researchers have examined second- and third-order stochastic dominance using empirical Lorenz processes and integrated stochastic processes with the aid of bootstrap analysis. Among these topics, analysis of third-order stochastic dominance (TSD) based on the notion of risk aversion has been examined using crossing (generalized) Lorenz curves. These facts motivated the present study to characterize distribution pairs displaying the TSD without second-order (generalized Lorenz) dominance. It further motivated the development of likelihood ratio (LR) goodness-of-fit tests for examining the respective hypotheses of the LD, crossing (generalized) Lorenz curves, and TSD through approximate Chi-squared distributions. The proposed LR tests were assessed using simulated distributions, and applied to examine the COVID-19 regional death counts of bivariate samples collected by the World Health Organization between March 2020 and February 2021.
ABSTRACT
Coronavirus disease (COVID-19) has been quickly spreading all over the world. As of 27th September 2020, a total of 382835 confirmed cases and 19755 deaths have been reported in Uttar Pradesh. The first case in India was registered on 30th January 2020. The data of Coronavirus cases in India and state-wise is available on the Ministry of Health and Family Welfare, Govt. of India. This paper aims to identify the Hot-spots (high rate cluster) of Coronavirus disease in Uttar Pradesh through the Scan Statistics methodology of clustering using the datasets till 27th September, 2020. The clusters (group of states) are reported through scan statistic using SaTscan software. We have identified the statistically significant clusters. The scanning of corona cases is done using simulation to detect the hotspot. The Poisson distribution is assumed for the corona cases. The expected and observed number of cases are compared through the likelihood ratio test. The highest value of the likelihood ratio among all is the hot-spot (most likely cluster). The results could be pretty helpful to the Government for taking strict actions for control, spread and effective management of medical resources in the country on a priority basis since the resources are very limited. © 2022 DAV College. All rights reserved.
ABSTRACT
Zoonotic Novel coronavirus disease 2019 (COVID-19) is highly pathogenic and transmissible considered as emerging pandemic disease. The virus belongs from a large virus Coronaviridae family affect respiratory tract of animal and human likely originated from bat and homology to SARA-CoV and MERS-CoV. The virus consists of single-stranded positive genomic RNA coated by nucleocapsid protein. The rate of mutation in any virulence gene may influence the phenomenon of host radiation. We have studied the molecular evolution of selected virulence genes (HA, N, RdRP and S) of novel COVID-19. We used a site-specific comparison of synonymous (silent) and non-synonymous (amino acid altering) nucleotide substitutions. Maximum Likelihood genealogies based on differential gamma distribution rates were used for the analysis of null and alternate hypothesis. The null hypothesis was found more suitable for the analysis using Likelihood Ratio Test (LRT) method, confirming higher rate of substitution. The analysis revealed that RdRP gene had the fastest rate evolution followed by HA gene. We have also reported the new motifs for different virulence genes, which are further useful to design new detection and diagnosis kit for COVID -19.