Dataset Information

COVID-19: Tail risk and predictive regressions.

ABSTRACT: The paper focuses on econometrically justified robust analysis of the effects of the COVID-19 pandemic on financial markets in different countries across the World. It provides the results of robust estimation and inference on predictive regressions for returns on major stock indexes in 23 countries in North and South America, Europe, and Asia incorporating the time series of reported infections and deaths from COVID-19. We also present a detailed study of persistence, heavy-tailedness and tail risk properties of the time series of the COVID-19 infections and death rates that motivate the necessity in applications of robust inference methods in the analysis. Econometrically justified analysis is based on heteroskedasticity and autocorrelation consistent (HAC) inference methods, recently developed robust t-statistic inference approaches and robust tail index estimation.

SUBMITTER: Distaso W

PROVIDER: S-EPMC9714707 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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COVID-19: Tail risk and predictive regressions.

Distaso Walter W Ibragimov Rustam R Semenov Alexander A Skrobotov Anton A

PloS one 20221201 12

The paper focuses on econometrically justified robust analysis of the effects of the COVID-19 pandemic on financial markets in different countries across the World. It provides the results of robust estimation and inference on predictive regressions for returns on major stock indexes in 23 countries in North and South America, Europe, and Asia incorporating the time series of reported infections and deaths from COVID-19. We also present a detailed study of persistence, heavy-tailedness and tail ...[more]

PMID: 36454731

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Project description:Background: We previously identified a 50-gene signature that distinguished two groups of patients with increased risk of COVID-19 and IPF mortality. In this study we aimed to validate our previous findings and to study the source and trajectory of circulating immune cells expressing these 50 genes in patients with COVID19, post-COVID-19 Interstitial Lung Disease (ILD) and Idiopathic Pulmonary Fibrosis (IPF). Methods: Whole blood and Peripheral Blood Mononuclear cells (PBMC) were obtained from 216 hospitalized patients with COVID-19, five patients with post-COVID-19-ILD, six patients with IPF and four controls from the University of South Florida (USF)/Tampa General Hospital (TGH). We measured the expression of the 50-gene signature using the nCounter analysis system (Nanostring), and the levels of Interleukin 6 (IL6), interferon γ-induced protein (IP10), Secreted Phosphoprotein 1 (SPP1) and transforming growth factor beta (TGF-β) in these patients by Luminex and Elisa tests. A 7-gene PCR assay was generated to validate the identification of COVID-19 endotypes. For single-cell RNA sequencing (scRNA-seq) we used Chromium Single Cell Controller (10X Genomics). For analysis of gene expression, we used the Scoring Algorithm of Molecular Subphenotypes (SAMS), Cell Ranger and Seurat packages. For statistical analysis, we used Kaplan-Meier survival curves, CoxPH models, Two-way ANOVA, T-test, Fisher’s exact and the Wilcoxon rank test. Results: We identified the presence of three genomic risk profiles based on the 50-gene signature, and a subset of seven genes, associated with low, intermediate, or high-risk of mortality in COVID-19. These risk groups had significant differences in IL6, IP10, SPP1 and TGFβ-1 levels. By applying scRNA-seq, we discovered a novel subtype of Monocytic-Myeloid-Derived Suppressive cells (M-MDSCs) expressing CD14+HLA DRlowCD163+ and high levels of the 7-gene signature. We denominated these cells as 7Gene-M-MDSC. These cells were not observed in survivors with post-COVID-19-ILD. The 43-gene signature was mostly expressed in naive CD4 T and memory CD4 T cells, Tregs, memory CD8 T GZMB+, memory CD8 T GZMK+ and naive CD8 T cells. While increased expression of genes in the 43 gene signature was seen in T cell subsets from survivors with post-COVID-19 ILD, the expression of these genes remained low in IPF. Conclusion: A 50-gene, high-risk genomic profile in peripheral blood of COVID-19 can accurately predict COVID-19 mortality. This profile is characterized by a simultaneous increase in expression of seven genes originating from 7Gene-M-MDSCs and decreased expression of 43 genes originating primarily in T cell subsets. While increased expression of these 43 genes can be seen in survivors with post-COVID-19 ILD, their expression remains low in IPF.

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COVID-19: Tail risk and predictive regressions.

Publications

COVID-19: Tail risk and predictive regressions.

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